diff --git a/README.md b/README.md index 1cb408bd..6d40a09f 100644 --- a/README.md +++ b/README.md @@ -45,17 +45,17 @@ If you want to use ETHOS.FINE in a published work, please [**kindly cite followi ### Python package manager The installation process uses a Conda-based Python package manager. We highly recommend using [(Micro-)Mamba](https://mamba.readthedocs.io/en/latest/) instead of Conda. The recommended way to use Mamba on your system is to install the [Miniforge distribution](https://github.com/conda-forge/miniforge#miniforge3). They offer installers for Windows, Linux and OS X. In principle, Conda and Mamba are interchangeable. The commands and concepts are the same. The distributions differ in the methodology for determining dependencies when installing Python packages. Mamba relies on a more modern methodology, which (with the same result) leads to very significant time savings during the installation of FINE. Switching to Mamba usually does not lead to any problems, as it is virtually identical to Conda in terms of operation. -If you decide to use Conda you have to expect longer installation times for FINE. In this case, we recommend the installation [through our conda-forge package](#installation-via-conda-forge). +If you decide to use Conda you have to expect longer installation times for ETHOS.FINE. In this case, we recommend the installation [through our conda-forge package](#installation-via-conda-forge). ### Mixed Integer Linear Programming (MILP) solver The project environment includes [GLPK](https://sourceforge.net/projects/winglpk/files/latest/download) as Mixed Integer Linear Programming (MILP) solver. If you want to solve large problems it is highly recommended to install [GUROBI](http://www.gurobi.com/). See ["Installation of an optimization solver"](#installation-of-an-optimization-solver) for more information. ## Installation -If you would like to run FINE for your analysis we recommend to install it directly from conda-forge into a new Python environment. Alternatively you can install it from a local folder by using the `requirements.yml` file. If you want to work on the FINE code base choose "Installation for developers". It performs an editable installation of FINE and add some developer tools (e.g. pytest, black) to the environment. +If you would like to run ETHOS.FINE for your analysis we recommend to install it directly from conda-forge into a new Python environment. Alternatively you can install it from a local folder by using the `requirements.yml` file. If you want to work on the ETHOS.FINE code base choose "Installation for developers". It performs an editable installation of ETHOS.FINE and add some developer tools (e.g. pytest, black) to the environment. ### Installation via conda-forge -The simplest way ist to install FINE into a fresh environment from `conda-forge` with: +The simplest way ist to install ETHOS.FINE into a fresh environment from `conda-forge` with: ```bash mamba create -n fine -c conda-forge fine ``` @@ -71,7 +71,7 @@ git clone --depth 1 https://github.com/FZJ-IEK3-VSA/FINE.git ```bash cd fine ``` -3. It is recommended to create a clean environment with conda to use FINE because it requires many dependencies. +3. It is recommended to create a clean environment with conda to use ETHOS.FINE because it requires many dependencies. ```bash mamba env create -f requirements.yml ``` @@ -85,7 +85,7 @@ python -m pip install --no-deps . ``` ### Installation for developers -If you want to work on the FINE codebase you need to run. +If you want to work on the ETHOS.FINE codebase you need to run. ```bash git clone https://github.com/FZJ-IEK3-VSA/FINE.git ``` @@ -93,8 +93,8 @@ to get the whole git history and then ```bash mamba env create -f requirements_dev.yml ``` -This installs additional dependencies such as `pytest` and installs FINE from the folder in editable mode with `pip -e`. Changes in the folder are then reflected in the package installation. -Finally, install FINE in editable mode with: +This installs additional dependencies such as `pytest` and installs ETHOS.FINE from the folder in editable mode with `pip -e`. Changes in the folder are then reflected in the package installation. +Finally, install ETHOS.FINE in editable mode with: ```bash python -m pip install --no-deps --editable . ``` @@ -105,12 +105,12 @@ pytest ## Installation of an optimization solver -FINE requires an MILP solver which can be accessed using [PYOMO](https://pyomo.readthedocs.io/en/stable/index.html). It searches for the following solvers in this order: +ETHOS.FINE requires an MILP solver which can be accessed using [PYOMO](https://pyomo.readthedocs.io/en/stable/index.html). It searches for the following solvers in this order: - [GUROBI](http://www.gurobi.com/) - Recommended due to better performance but requires license (free academic version available) - Set as standard solver - [GLPK](https://sourceforge.net/projects/winglpk/files/latest/download) - - This solver is installed with the FINE environment. + - This solver is installed with the ETHOS.FINE environment. - Free version available - [CBC](https://projects.coin-or.org/Cbc) - Free version available @@ -132,7 +132,31 @@ Installation procedure can be found [here](https://projects.coin-or.org/Cbc). ## Examples -A number of [examples](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples) shows the capabilities of FINE. +A number of [examples](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples) shows the capabilities of ETHOS.FINE. +- [00_Tutorial](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/00_Tutorial) + - In this application, an energy supply system, consisting of two regions, is modeled and optimized. Recommended as starting point to get to know to ETHOS.FINE. +- [01_1node_Energy_System_Workflow](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/01_1node_Energy_System_Workflow) + - In this application, a single region energy system is modeled and optimized. The system includes only a few technologies. +- [02_EnergyLand](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/02_EnergyLand) + - In this application, a single region energy system is modeled and optimized. Compared to the previous examples, this example includes a lot more technologies considered in the system. +- [03_Multi-regional_Energy_System_Workflow](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/03_Multi-regional_Energy_System_Workflow) + - In this application, an energy supply system, consisting of eight regions, is modeled and optimized. The example shows how to model multi-regional energy systems. The example also includes a notebook to get to know the optional performance summary. The summary shows how the optimization performed. +- [04_Model_Run_from_Excel](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/04_Model_Run_from_Excel) + - ETHOS.FINE can also be run by excel. This example shows how to read and run a model using excel files. +- [05_District_Optimization](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/05_District_Optimization) + - In this application, a small district is modeled and optimized. This example also includes binary decision variables. +- [06_Water_Supply_System](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/06_Water_Supply_System) + - The application cases of ETHOS.FINE are not limited. This application shows how to model the water supply system. +- [07_NetCDF_to_save_and_set_up_model_instance](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/07_NetCDF_to_save_and_set_up_model_instance) + - This example shows how to save the input and optimized results of an energy system Model instance to netCDF files to allow reproducibility. +- [08_Spatial_and_technology_aggregation](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/08_Spatial_and_technology_aggregation) + - These two examples show how to reduce the model complexity. Model regions can be aggregated to reduce the number of regions (spatial aggregation). Input parameters are automatically adapted. Furthermore, technologies can be aggregated to reduce complexity, e.g. reducing the number of different PV components (technology aggregation). Input parameters are automatically adapted. +- [09_Stochastic_Optimization](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/9_Stochastic_Optimizatio) + - In this application, a stochastic optimization is performed. It is possible to perform the optimization of an energy system model with different input parameter sets to receive a more robust solution. +- [10_PerfectForesight](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/10_PerfectForesight) + - In this application, a transformation pathway of an energy system is modeled and optimized showing how to handle several investment periods with time-dependent assumptions for costs and operation. +- [11_Partload](https://github.com/FZJ-IEK3-VSA/FINE/tree/master/examples/11_Partload) + - In this application, a hydrogen system is modeled and optimized considering partload behavior of the electrolyzer. ## License diff --git a/docs/source/tutorialDoc.rst b/docs/source/tutorialDoc.rst index 0a5c0946..edb0923a 100644 --- a/docs/source/tutorialDoc.rst +++ b/docs/source/tutorialDoc.rst @@ -2,4 +2,30 @@ Tutorial ******** -Tutorial of how to model a small energy system can be found on the github page of FINE in the "examples" folder. \ No newline at end of file +The repository of ETHOS.FINE provides a certain amount of different `examples ` to get to know to the package. +The examples are sorted by their complexity and should provide a good overview on the scope of the package. + +* `00_Tutorial ` + * In this application, an energy supply system, consisting of two regions, is modeled and optimized. Recommended as starting point to get to know to ETHOS.FINE. +* `01_1node_Energy_System_Workflow ` + * In this application, a single region energy system is modeled and optimized. The system includes only a few technologies. +* `02_EnergyLand ` + * In this application, a single region energy system is modeled and optimized. Compared to the previous examples, this example includes a lot more technologies considered in the system. +* `03_Multi-regional_Energy_System_Workflow ` + * In this application, an energy supply system, consisting of eight regions, is modeled and optimized. The example shows how to model multi-regional energy systems. The example also includes a notebook to get to know the optional performance summary. The summary shows how the optimization performed. +* `04_Model_Run_from_Excel ` + * ETHOS.FINE can also be run by excel. This example shows how to read and run a model using excel files. +* `05_District_Optimization ` + * In this application, a small district is modeled and optimized. This example also includes binary decision variables. +* `06_Water_Supply_System ` + * The application cases of ETHOS.FINE are not limited. This application shows how to model the water supply system. +* `07_NetCDF_to_save_and_set_up_model_instance ` + * This example shows how to save the input and optimized results of an energy system Model instance to netCDF files to allow reproducibility. +* `08_Spatial_and_technology_aggregation ` + * These two examples show how to reduce the model complexity. Model regions can be aggregated to reduce the number of regions (spatial aggregation). Input parameters are automatically adapted. Furthermore, technologies can be aggregated to reduce complexity, e.g. reducing the number of different PV components (technology aggregation). Input parameters are automatically adapted. +* `9_Stochastic_Optimization ` + * In this application, a stochastic optimization is performed. It is possible to perform the optimization of an energy system model with different input parameter sets to receive a more robust solution. +* `10_PerfectForesight ` + * In this application, a transformation pathway of an energy system is modeled and optimized showing how to handle several investment periods with time-dependent assumptions for costs and operation. +* `11_Partload ` + * In this application, a hydrogen system is modeled and optimized considering partload behavior of the electrolyzer. \ No newline at end of file diff --git a/examples/00_Tutorial/00_Tutorial.ipynb b/examples/00_Tutorial/00_Tutorial.ipynb new file mode 100644 index 00000000..50927eb4 --- /dev/null +++ b/examples/00_Tutorial/00_Tutorial.ipynb @@ -0,0 +1,1968 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\n", + " \"ignore\"\n", + ") # For better visibility, warnings are turned off in this notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ETHOS.FINE Tutorial: 2-nodal Electricity Supply System" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this application of the ETHOS.FINE framework, an energy supply system, consisting of two-regions, is modeled and optimized.\n", + "\n", + "The workflow is structures as follows:\n", + "- Required packages are imported\n", + "- An energy system model instance is created\n", + "- Commodity sources are added to the energy supply system model\n", + "- Commodity conversion components are added to the energy supply system model\n", + "- Commodity storages are added to the energy supply system model\n", + "- Commodity transmission components are added to the energy supply system model\n", + "- Commodity sinks are added to the energy supply system model\n", + "- The energy supply system model is optimized\n", + "- Selected optimization results are presented" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import required packages\n", + "\n", + "The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import fine as fn # Provides objects and functions to model an energy system\n", + "import pandas as pd # Used to manage data in tables\n", + "import shapely as shp # Used to generate geometric objects\n", + "import numpy as np # Used to generate random input data\n", + "\n", + "np.random.seed(\n", + " 42\n", + ") # Sets a \"seed\" to produce the same random input data in each model run" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "import geopandas as gpd # Used to display geo-referenced plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model an energy system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an energy system model instance \n", + "\n", + "The structure of the energy supply system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", + "\n", + "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_naturalGas_lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "# Input parameters\n", + "locations = {\"regionN\", \"regionS\"}\n", + "commodityUnitDict = {\n", + " \"electricity\": r\"GW$_{el}$\",\n", + " \"naturalGas\": r\"GW$_{CH_{4},LHV}$\",\n", + " \"CO2\": r\"Mio. t$_{CO_2}$/h\",\n", + "}\n", + "commodities = {\"electricity\", \"naturalGas\", \"CO2\"}\n", + "numberOfTimeSteps, hoursPerTimeStep = 8760, 1\n", + "costUnit, lengthUnit = \"1e6 Euro\", \"km\"\n", + "\n", + "# Code\n", + "esM = fn.EnergySystemModel(\n", + " locations=locations,\n", + " commodities=commodities,\n", + " numberOfTimeSteps=numberOfTimeSteps,\n", + " commodityUnitsDict=commodityUnitDict,\n", + " hoursPerTimeStep=hoursPerTimeStep,\n", + " costUnit=costUnit,\n", + " lengthUnit=lengthUnit,\n", + " verboseLogLevel=0,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add source components\n", + "\n", + "Source components generate commodities across the energy system's virtual boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = \"Wind turbines\", \"electricity\"\n", + "hasCapacityVariable = True\n", + "operationRateMax = pd.DataFrame(\n", + " [[np.random.beta(a=2, b=7.5), np.random.beta(a=2, b=9)] for t in range(8760)],\n", + " index=range(8760),\n", + " columns=[\"regionN\", \"regionS\"],\n", + ").round(6)\n", + "capacityMax = pd.Series([400, 200], index=[\"regionN\", \"regionS\"])\n", + "investPerCapacity, opexPerCapacity = 1200, 1200 * 0.02\n", + "interestRate, economicLifetime = 0.08, 20\n", + "\n", + "# If data should be read from an excel file:\n", + "writer = pd.ExcelWriter(\"windTurbineProfile.xlsx\") # writes data to an excel file\n", + "operationRateMax.to_excel(writer) # (not required if excel file\n", + "writer.close() # already exists)\n", + "operationRateMax = pd.read_excel(\n", + " \"windTurbineProfile.xlsx\", index_col=0\n", + ") # reads an excel file located in\n", + "# the current working directory\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " operationRateMax=operationRateMax,\n", + " capacityMax=capacityMax,\n", + " investPerCapacity=investPerCapacity,\n", + " opexPerCapacity=opexPerCapacity,\n", + " interestRate=interestRate,\n", + " economicLifetime=economicLifetime,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = \"PV\", \"electricity\"\n", + "hasCapacityVariable = True\n", + "dailyProfileSimple = [\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0.05,\n", + " 0.15,\n", + " 0.2,\n", + " 0.4,\n", + " 0.8,\n", + " 0.7,\n", + " 0.4,\n", + " 0.2,\n", + " 0.15,\n", + " 0.05,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + "]\n", + "operationRateMax = pd.DataFrame(\n", + " [[u, u] for day in range(365) for u in dailyProfileSimple],\n", + " index=range(8760),\n", + " columns=[\"regionN\", \"regionS\"],\n", + ")\n", + "capacityMax = pd.Series([100, 100], index=[\"regionN\", \"regionS\"])\n", + "investPerCapacity, opexPerCapacity = 800, 800 * 0.02\n", + "interestRate, economicLifetime = 0.08, 25\n", + "\n", + "# If data should be read from an excel file:\n", + "writer = pd.ExcelWriter(\"PV_Profile.xlsx\") # writes data to an excel file\n", + "operationRateMax.to_excel(writer) # (not required if excel file\n", + "writer.close() # already exists)\n", + "operationRateMax = pd.read_excel(\n", + " \"PV_Profile.xlsx\", index_col=0\n", + ") # reads an excel file located in\n", + "# the current working directory\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " operationRateMax=operationRateMax,\n", + " capacityMax=capacityMax,\n", + " investPerCapacity=investPerCapacity,\n", + " opexPerCapacity=opexPerCapacity,\n", + " interestRate=interestRate,\n", + " economicLifetime=economicLifetime,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = \"Natural gas import\", \"naturalGas\"\n", + "hasCapacityVariable = False\n", + "commodityCost = 0.03\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " commodityCost=commodityCost,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add conversion components\n", + "\n", + "Conversion components convert m commodities into n other commodities." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, physicalUnit = \"Gas power plants\", r\"GW$_{el}$\"\n", + "commodityConversionFactors = {\n", + " \"electricity\": 1,\n", + " \"naturalGas\": -1 / 0.63,\n", + " \"CO2\": 201 * 1e-6 / 0.63,\n", + "}\n", + "hasCapacityVariable = True\n", + "investPerCapacity, opexPerCapacity = 650, 650 * 0.03\n", + "interestRate, economicLifetime = 0.08, 30\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=name,\n", + " physicalUnit=physicalUnit,\n", + " commodityConversionFactors=commodityConversionFactors,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " investPerCapacity=investPerCapacity,\n", + " opexPerCapacity=opexPerCapacity,\n", + " interestRate=interestRate,\n", + " economicLifetime=economicLifetime,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add storage components\n", + "\n", + "Storage components can store commodities across time steps.\n", + "\n", + "The self discharge of a storage technology is described in FINE in percent per hour. If the literature value is given in percent per month, e.g. 3%/month, the self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = \"Batteries\", \"electricity\"\n", + "hasCapacityVariable = True\n", + "chargeEfficiency, dischargeEfficiency, selfDischarge = (\n", + " 0.95,\n", + " 0.95,\n", + " 1 - (1 - 0.03) ** (1 / (30 * 24)),\n", + ")\n", + "chargeRate, dischargeRate = 1, 1\n", + "investPerCapacity, opexPerCapacity = 150, 150 * 0.01\n", + "interestRate, economicLifetime, cyclicLifetime = 0.08, 22, 12000\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " chargeEfficiency=chargeEfficiency,\n", + " cyclicLifetime=cyclicLifetime,\n", + " dischargeEfficiency=dischargeEfficiency,\n", + " selfDischarge=selfDischarge,\n", + " chargeRate=chargeRate,\n", + " dischargeRate=dischargeRate,\n", + " investPerCapacity=investPerCapacity,\n", + " opexPerCapacity=opexPerCapacity,\n", + " interestRate=interestRate,\n", + " economicLifetime=economicLifetime,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add transmission components\n", + "\n", + "Transmission components transmit commodities between regions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = \"AC cables\", \"electricity\"\n", + "hasCapacityVariable = True\n", + "capacityFix = pd.DataFrame(\n", + " [[0, 30], [30, 0]], columns=[\"regionN\", \"regionS\"], index=[\"regionN\", \"regionS\"]\n", + ")\n", + "distances = pd.DataFrame(\n", + " [[0, 400], [400, 0]], columns=[\"regionN\", \"regionS\"], index=[\"regionN\", \"regionS\"]\n", + ")\n", + "losses = 0.0001\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " capacityFix=capacityFix,\n", + " distances=distances,\n", + " losses=losses,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " regionN regionS\n", + "regionN 0 400\n", + "regionS 400 0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add sink components\n", + "\n", + "Sinks remove commodities across the energy system´s virtual boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [], + "scrolled": true + }, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = (\n", + " \"Electricity demand\",\n", + " \"electricity\",\n", + ")\n", + "hasCapacityVariable = False\n", + "dailyProfileSimple = [\n", + " 0.6,\n", + " 0.6,\n", + " 0.6,\n", + " 0.6,\n", + " 0.6,\n", + " 0.7,\n", + " 0.9,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 1,\n", + " 0.9,\n", + " 0.8,\n", + "]\n", + "operationRateFix = pd.DataFrame(\n", + " [\n", + " [(u + 0.1 * np.random.rand()) * 25, (u + 0.1 * np.random.rand()) * 40]\n", + " for day in range(365)\n", + " for u in dailyProfileSimple\n", + " ],\n", + " index=range(8760),\n", + " columns=[\"regionN\", \"regionS\"],\n", + ").round(2)\n", + "\n", + "# If data should be read from an excel file:\n", + "writer = pd.ExcelWriter(\"demandProfile.xlsx\") # writes data to an excel file\n", + "operationRateFix.to_excel(writer) # (not required if excel file\n", + "writer.close() # already exists)\n", + "operationRateFix = pd.read_excel(\n", + " \"demandProfile.xlsx\", index_col=0\n", + ") # reads an excel file located in\n", + "# the current working directory\n", + "\n", + "# Code\n", + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " operationRateFix=operationRateFix,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Input parameters\n", + "name, commodity = (\n", + " \"CO2 to enviroment\",\n", + " \"CO2\",\n", + ")\n", + "hasCapacityVariable = False\n", + "commodityLimitID, yearlyLimit = \"CO2 limit\", 366 * (1 - 0.8)\n", + "\n", + "# Code\n", + "if yearlyLimit > 0:\n", + " esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=name,\n", + " commodity=commodity,\n", + " hasCapacityVariable=hasCapacityVariable,\n", + " commodityLimitID=commodityLimitID,\n", + " yearlyLimit=yearlyLimit,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimize energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Clustering time series data with 30 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(4.5907 sec)\n", + "\n" + ] + } + ], + "source": [ + "# Input parameters\n", + "numberOfTypicalPeriods = 30\n", + "\n", + "# Code\n", + "esM.aggregateTemporally(numberOfTypicalPeriods=numberOfTypicalPeriods)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series aggregation specifications:\n", + "Number of typical periods:30, number of time steps per period:24, number of segments per period:12\n", + "\n", + "Declaring sets, variables and constraints for SourceSinkModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.2880 sec)\n", + "\n", + "Declaring sets, variables and constraints for ConversionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.0469 sec)\n", + "\n", + "Declaring sets, variables and constraints for StorageModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.2688 sec)\n", + "\n", + "Declaring sets, variables and constraints for TransmissionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.0705 sec)\n", + "\n", + "Declaring shared potential constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring linked component quantity constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring commodity balances...\n", + "\t\t(0.0469 sec)\n", + "\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring objective function...\n", + "\t\t(0.5837 sec)\n", + "\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp22uv40_l.glpk.raw --wglp\n", + " C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpp5a6mi5p.glpk.glp --cpxlp C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp5xbzbdwu.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp5xbzbdwu.pyomo.lp'...\n", + "11599 rows, 8152 columns, 32238 non-zeros\n", + "75924 lines were read\n", + "Writing problem data to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpp5a6mi5p.glpk.glp'...\n", + "61571 lines were written\n", + "GLPK Simplex Optimizer 5.0\n", + "11599 rows, 8152 columns, 32238 non-zeros\n", + "Preprocessing...\n", + "10391 rows, 6286 columns, 28324 non-zeros\n", + "Scaling...\n", + " A: min|aij| = 3.190e-04 max|aij| = 5.455e+02 ratio = 1.710e+06\n", + "GM: min|aij| = 2.926e-01 max|aij| = 3.417e+00 ratio = 1.168e+01\n", + "EQ: min|aij| = 8.563e-02 max|aij| = 1.000e+00 ratio = 1.168e+01\n", + "Constructing initial basis...\n", + "Size of triangular part is 9659\n", + " 0: obj = 0.000000000e+00 inf = 1.390e+04 (720)\n", + " 743: obj = 9.946047517e+04 inf = 0.000e+00 (0) 7\n", + "Perturbing LP to avoid stalling [2561]...\n", + "Warning: basis matrix is ill-conditioned (cond = 1.05e+13)\n", + "Removing LP perturbation [6046]...\n", + "* 6046: obj = 3.976375055e+04 inf = 6.799e-11 (0) 48\n", + "OPTIMAL LP SOLUTION FOUND\n", + "Time used: 2.0 secs\n", + "Memory used: 13.5 Mb (14202885 bytes)\n", + "Writing basic solution to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp22uv40_l.glpk.raw'...\n", + "19760 lines were written\n", + "\n", + "Status: ok\n", + "Termination condition: optimal\n", + "Statistics: \n", + " Branch and bound: \n", + " Number of bounded subproblems: 0\n", + " Number of created subproblems: 0\n", + "Error rc: 0\n", + "Time: 2.342017412185669\n", + "\n", + "\n", + "Name: unknown\n", + "Lower bound: 39763.7505472458\n", + "Upper bound: 39763.7505472458\n", + "Number of objectives: 1\n", + "Number of constraints: 11599\n", + "Number of variables: 8152\n", + "Number of nonzeros: 32238\n", + "Sense: minimize\n", + "\n", + "Solve time: 2.8094606399536133 sec.\n", + "\n", + "Processing optimization output...\n", + "for SourceSinkModel ... (0.6032sec)\n", + "for ConversionModel ... (0.1950sec)\n", + "for StorageModel ... (0.8247sec)\n", + "for TransmissionModel ... (0.3710sec)\n", + "\t\t(2.0342 sec)\n", + "\n" + ] + } + ], + "source": [ + "# Input parameters\n", + "timeSeriesAggregation = True\n", + "solver = \"glpk\"\n", + "\n", + "# Code\n", + "esM.optimize(timeSeriesAggregation=timeSeriesAggregation, solver=solver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Output of selected results\n", + "\n", + "For the assessment of the optimization result, several result output functions are available. They can be categorized into output in form of tables, geo-referenced output visualization and the full time series visualization.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a regional shape file and visualize it\n", + "\n", + "Information on the geometrical shape of the investigated regions can either be downloaded from a website (e.g. from https://gadm.org/) or manually created. In this notebook, the geometries are manually created." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create two circles, representing the two regions, and store their geometries in a shape file\n", + "shpRegionS = shp.geometry.Point(0.5, 0.5).buffer(0.5)\n", + "shpRegionN = shp.geometry.Point(0.5, 1.5).buffer(0.5)\n", + "regionsGdf = gpd.GeoDataFrame(\n", + " {\"geometry\": [shpRegionN, shpRegionS], \"regionName\": [\"regionN\", \"regionS\"]},\n", + " index=[\"regionN\", \"regionS\"],\n", + " crs=\"epsg:3035\",\n", + ")\n", + "regionsGdf.to_file(\"regions.shp\")\n", + "\n", + "# Create a line, representing the connection between the two regions, and store its geometry in a\n", + "# shape file\n", + "lines = shp.geometry.LineString([(0.5, 0.5), (0.5, 1.5)])\n", + "linesGdf = gpd.GeoDataFrame(\n", + " {\n", + " \"geometry\": [lines, lines],\n", + " \"loc0\": [\"regionN\", \"regionS\"],\n", + " \"loc1\": [\"regionS\", \"regionN\"],\n", + " },\n", + " index=[\"regionN_regionS\", \"regionS_regionN\"],\n", + " crs=\"epsg:3035\",\n", + ")\n", + "linesGdf.to_file(\"lines.shp\")\n", + "\n", + "# Visualize the geometric representation of the two regions\n", + "fig, ax = fn.plotLocations(\"regions.shp\", indexColumn=\"regionName\", plotLocNames=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display optimization summaries\n", + "\n", + "For each modeling class, an optimization summary can be stored and displayed. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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regionNregionS
ComponentPropertyUnit
CO2 to enviromentoperation[Mio. t$_{CO_2}$/h*h/a]0.51801472.681986
[Mio. t$_{CO_2}$/h*h]0.51801472.681986
Electricity demandoperation[GW$_{el}$*h/a]205207.79328596.33
[GW$_{el}$*h]205207.79328596.33
Natural gas importNPVcontribution[1e6 Euro]77.31545210848.057682
TAC[1e6 Euro/a]77.31545210848.057682
commodCosts[1e6 Euro/a]77.31545210848.057682
operation[GW$_{CH_{4},LHV}$*h/a]2577.181733361601.922745
[GW$_{CH_{4},LHV}$*h]2577.181733361601.922745
PVNPVcontribution[1e6 Euro]0.01959.002674
TAC[1e6 Euro/a]0.01959.002674
capacity[GW$_{el}$]0.021.540989
capexCap[1e6 Euro/a]0.01614.346848
commissioning[GW$_{el}$]-0.021.540989
invest[1e6 Euro]0.017232.791298
operation[GW$_{el}$*h/a]0.024373.629192
[GW$_{el}$*h]0.024373.629192
opexCap[1e6 Euro/a]0.0344.655826
Wind turbinesNPVcontribution[1e6 Euro]22638.3730860.0
TAC[1e6 Euro/a]22638.3730860.0
capacity[GW$_{el}$]154.8212470.0
capexCap[1e6 Euro/a]18922.6631610.0
commissioning[GW$_{el}$]154.821247-0.0
invest[1e6 Euro]185785.4962560.0
operation[GW$_{el}$*h/a]285458.5250530.0
[GW$_{el}$*h]285458.5250530.0
opexCap[1e6 Euro/a]3715.7099250.0
\n", + "
" + ], + "text/plain": [ + " regionN \\\n", + "Component Property Unit \n", + "CO2 to enviroment operation [Mio. t$_{CO_2}$/h*h/a] 0.518014 \n", + " [Mio. t$_{CO_2}$/h*h] 0.518014 \n", + "Electricity demand operation [GW$_{el}$*h/a] 205207.79 \n", + " [GW$_{el}$*h] 205207.79 \n", + "Natural gas import NPVcontribution [1e6 Euro] 77.315452 \n", + " TAC [1e6 Euro/a] 77.315452 \n", + " commodCosts [1e6 Euro/a] 77.315452 \n", + " operation [GW$_{CH_{4},LHV}$*h/a] 2577.181733 \n", + " [GW$_{CH_{4},LHV}$*h] 2577.181733 \n", + "PV NPVcontribution [1e6 Euro] 0.0 \n", + " TAC [1e6 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e6 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] -0.0 \n", + " invest [1e6 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e6 Euro/a] 0.0 \n", + "Wind turbines NPVcontribution [1e6 Euro] 22638.373086 \n", + " TAC [1e6 Euro/a] 22638.373086 \n", + " capacity [GW$_{el}$] 154.821247 \n", + " capexCap [1e6 Euro/a] 18922.663161 \n", + " commissioning [GW$_{el}$] 154.821247 \n", + " invest [1e6 Euro] 185785.496256 \n", + " operation [GW$_{el}$*h/a] 285458.525053 \n", + " [GW$_{el}$*h] 285458.525053 \n", + " opexCap [1e6 Euro/a] 3715.709925 \n", + "\n", + " regionS \n", + "Component Property Unit \n", + "CO2 to enviroment operation [Mio. t$_{CO_2}$/h*h/a] 72.681986 \n", + " [Mio. t$_{CO_2}$/h*h] 72.681986 \n", + "Electricity demand operation [GW$_{el}$*h/a] 328596.33 \n", + " [GW$_{el}$*h] 328596.33 \n", + "Natural gas import NPVcontribution [1e6 Euro] 10848.057682 \n", + " TAC [1e6 Euro/a] 10848.057682 \n", + " commodCosts [1e6 Euro/a] 10848.057682 \n", + " operation [GW$_{CH_{4},LHV}$*h/a] 361601.922745 \n", + " [GW$_{CH_{4},LHV}$*h] 361601.922745 \n", + "PV NPVcontribution [1e6 Euro] 1959.002674 \n", + " TAC [1e6 Euro/a] 1959.002674 \n", + " capacity [GW$_{el}$] 21.540989 \n", + " capexCap [1e6 Euro/a] 1614.346848 \n", + " commissioning [GW$_{el}$] 21.540989 \n", + " invest [1e6 Euro] 17232.791298 \n", + " operation [GW$_{el}$*h/a] 24373.629192 \n", + " [GW$_{el}$*h] 24373.629192 \n", + " opexCap [1e6 Euro/a] 344.655826 \n", + "Wind turbines NPVcontribution [1e6 Euro] 0.0 \n", + " TAC [1e6 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e6 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] -0.0 \n", + " invest [1e6 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e6 Euro/a] 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "srcSnkSummary = esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=1)\n", + "display(esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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regionNregionS
ComponentPropertyUnit
Gas power plantsNPVcontribution[1e6 Euro]51.4824193138.932372
TAC[1e6 Euro/a]51.4824193138.932372
capacity[GW$_{el}$]0.66654440.63983
capexCap[1e6 Euro/a]38.4848092346.455682
commissioning[GW$_{el}$]0.66654440.63983
invest[1e6 Euro]433.25364526415.889689
operation[GW$_{el}$*h/a]1623.624492227809.211329
[GW$_{el}$*h]1623.624492227809.211329
opexCap[1e6 Euro/a]12.997609792.476691
\n", + "
" + ], + "text/plain": [ + " regionN regionS\n", + "Component Property Unit \n", + "Gas power plants NPVcontribution [1e6 Euro] 51.482419 3138.932372\n", + " TAC [1e6 Euro/a] 51.482419 3138.932372\n", + " capacity [GW$_{el}$] 0.666544 40.63983\n", + " capexCap [1e6 Euro/a] 38.484809 2346.455682\n", + " commissioning [GW$_{el}$] 0.666544 40.63983\n", + " invest [1e6 Euro] 433.253645 26415.889689\n", + " operation [GW$_{el}$*h/a] 1623.624492 227809.211329\n", + " [GW$_{el}$*h] 1623.624492 227809.211329\n", + " opexCap [1e6 Euro/a] 12.997609 792.476691" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "convSummary = esM.getOptimizationSummary(\"ConversionModel\", outputLevel=1)\n", + "display(esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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regionNregionS
ComponentPropertyUnit
BatteriesNPVcontribution[1e6 Euro]979.8099770.776892
TAC[1e6 Euro/a]979.8099770.776892
capacity[GW$_{el}$*h]60.4641434.367647
capexCap[1e6 Euro/a]889.11375564.225421
commissioning[GW$_{el}$*h]60.4641434.367647
invest[1e6 Euro]9069.621497655.147059
operationCharge[GW$_{el}$*h/a]17987.201468464.062248
[GW$_{el}$*h]17987.201468464.062248
operationDischarge[GW$_{el}$*h/a]16223.850915418.62235
[GW$_{el}$*h]16223.850915418.62235
opexCap[1e6 Euro/a]90.6962156.551471
\n", + "
" + ], + "text/plain": [ + " regionN regionS\n", + "Component Property Unit \n", + "Batteries NPVcontribution [1e6 Euro] 979.80997 70.776892\n", + " TAC [1e6 Euro/a] 979.80997 70.776892\n", + " capacity [GW$_{el}$*h] 60.464143 4.367647\n", + " capexCap [1e6 Euro/a] 889.113755 64.225421\n", + " commissioning [GW$_{el}$*h] 60.464143 4.367647\n", + " invest [1e6 Euro] 9069.621497 655.147059\n", + " operationCharge [GW$_{el}$*h/a] 17987.201468 464.062248\n", + " [GW$_{el}$*h] 17987.201468 464.062248\n", + " operationDischarge [GW$_{el}$*h/a] 16223.850915 418.62235\n", + " [GW$_{el}$*h] 16223.850915 418.62235\n", + " opexCap [1e6 Euro/a] 90.696215 6.551471" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "storSummary = esM.getOptimizationSummary(\"StorageModel\", outputLevel=1)\n", + "display(esM.getOptimizationSummary(\"StorageModel\", outputLevel=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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regionNregionS
ComponentPropertyUnitLocationIn
AC cablescapacity[GW$_{el}$]regionNNaN30.0
regionS30.0NaN
commissioning[GW$_{el}$]regionNNaN30.0
regionS30.0NaN
operation[GW$_{el}$*h/a]regionNNaN85592.305998
regionS5709.684381NaN
[GW$_{el}$*h]regionNNaN85592.305998
regionS5709.684381NaN
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" + ], + "text/plain": [ + " regionN regionS\n", + "Component Property Unit LocationIn \n", + "AC cables capacity [GW$_{el}$] regionN NaN 30.0\n", + " regionS 30.0 NaN\n", + " commissioning [GW$_{el}$] regionN NaN 30.0\n", + " regionS 30.0 NaN\n", + " operation [GW$_{el}$*h/a] regionN NaN 85592.305998\n", + " regionS 5709.684381 NaN\n", + " [GW$_{el}$*h] regionN NaN 85592.305998\n", + " regionS 5709.684381 NaN" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transSummary = esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=1)\n", + "display(esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display regional and operational plots\n", + "\n", + "Georeferenced plots as well as plots representing time series can be displayed for each component." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind turbines" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# If wind turbines are built, their capacities are displayed in a geo-referenced plot\n", + "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", + " fig, ax = fn.plotLocationalColorMap(\n", + " esM,\n", + " \"Wind turbines\",\n", + " \"regions.shp\",\n", + " \"regionName\",\n", + " perArea=False,\n", + " zlabel=\"Capacity\\n[GW]\\n\",\n", + " figsize=(4, 4),\n", + " )\n", + "else:\n", + " print(\"No wind turbines built.\")\n", + "\n", + "# If wind turbines are built in regionN, their operation is displayed as heatmap\n", + "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Wind turbines\",\n", + " \"regionN\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", + " )\n", + "\n", + "# If wind turbines are built in regionS, their operation is displayed as heatmap\n", + "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Wind turbines\",\n", + " \"regionS\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the Day\",\n", + " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", + " orientation=\"vertical\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PV systems" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# If PV systems are built, their capacities are displayed in a geo-referenced plot\n", + "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", + " fig, ax = fn.plotLocationalColorMap(\n", + " esM,\n", + " \"PV\",\n", + " \"regions.shp\",\n", + " \"regionName\",\n", + " perArea=False,\n", + " zlabel=\"Capacity\\n[GW]\\n\",\n", + " figsize=(4, 4),\n", + " )\n", + "else:\n", + " print(\"No PV systems built.\")\n", + "\n", + "# If PV systems are built in regionS, their operation is displayed as heatmap\n", + "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"PV\",\n", + " \"regionN\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", + " )\n", + "\n", + "# If PV systems are built in regionS, their operation is displayed as heatmap\n", + "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"PV\",\n", + " \"regionS\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", + " orientation=\"vertical\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gas power plants" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# If CCGT plants are built, their capacities are displayed in a geo-referenced plot\n", + "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", + " fig, ax = fn.plotLocationalColorMap(\n", + " esM,\n", + " \"Gas power plants\",\n", + " \"regions.shp\",\n", + " \"regionName\",\n", + " perArea=False,\n", + " zlabel=\"Capacity\\n[GW]\\n\",\n", + " figsize=(4, 4),\n", + " )\n", + "else:\n", + " print(\"No CCGT plants built.\")\n", + "\n", + "# If CCGT plants are built in regionS, their operation is displayed as heatmap\n", + "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Gas power plants\",\n", + " \"regionN\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", + " orientation=\"vertical\",\n", + " )\n", + "\n", + "# If CCGT plants are built in regionS, their operation is displayed as heatmap\n", + "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Gas power plants\",\n", + " \"regionS\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", + " orientation=\"vertical\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Batteries" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "code_folding": [], + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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yMiZNmlTmfr28vGyO6GF6cOVhhoQqD6b9/vLLLyV20ShLWWXi4eEBqVSKF198scSyCg0NBSB+6C5bw1KRyuHj42MepqyisAp8MrhBgwblnmZJ+vTpgz179mDHjh2IiYmxeztqEZJHlpiYiDfeeANubm547bXXAPD/SBljgmGQAGD16tUwGAxWaahUKrz66qtYv349Pv74YzzxxBPo3Llzmfvu0aMH/vvvP5w+fVoQvmHDBnAch6ioqIc6ppKGr7JXdHQ0ZDIZbty4YXOYK3v6IJZVJo6OjoiKisKZM2cQHh5ucx+mlquYobtI1WLr0qhpwtmTJ0+ia9eucHR0RP369fH++++bn5YuTWlPBtvzFDIAXLx4Eb1794ajoyN8fHwwadIk/PHHH+A4TnDp39alUbVajTlz5pjnE6xTpw4mTZqE+/fvC+KFhISgf//+2LNnD1q3bg0HBwc0bdrUas7Bovtq3rw55syZY/P/TEmoRUhEuXDhgvk+VGpqKo4ePWruUL9t2zbzL1dXV1d069YNH374Iby9vRESEoK4uDh89913cHd3t5n2xIkTsWzZMpw6dQqrV6+2Kz/Tp0/Hhg0b8PTTT2PhwoUIDg7GH3/8gZUrV2LChAlo3LjxQx1nScNX2SskJAQLFy7E3LlzcfPmTfTp0wceHh5ISUnBP//8AycnJyxYsKDMdMoqk88++wxdunRB165dMWHCBISEhCA3NxfXr1/Hjh07zPdIxQzdRaqH5ORkPP/885g5cybmz5+Pbdu2Yc6cOQgMDDRP4Fua7du34+jRo5g3bx78/f3h6+uL//77D506dTJ3ifL398fevXvx+uuvIz09HfPnzwfAj6saEREBJycnfPXVV/D19cVPP/2EyZMnl7lfxhgGDhyIAwcOYM6cOejatSvOnTuH+fPn49ixYzh27JjgB/T//d//YebMmZg9ezb8/PywevVqjBs3Dg0bNkS3bt0EaUulUixduhQDBgzA+vXrzbPR25MpQspUfIg1hULBfH19WUREBFuyZAlLTU212ubOnTtsyJAhzMPDg7m4uLA+ffqwCxcusODgYJtPZDLGWGRkJPP09GQPHjywO28JCQls5MiRzMvLi8nlctakSRP24YcfMoPBIIgn5qnRkoavKunpWdNTnkWHd2KMse3bt7OoqCjm6urKlEolCw4OZkOHDmX79++3+/jKKpNbt26xsWPHsjp16jC5XM58fHxYp06d2KJFiwTx7B26CwCbNGmS3fkjFcvWEGOmCWeLPy3dvHlzFh0dXWaaKOHJYHufQn7zzTcZx3FW3YCio6OtvgejR48WDCG3Z88emxPwbt68mQFg33zzjTksODiYqVQqwSTDBQUFzNPTk7322mvmMNP3b8uWLYwxxrp06cLq1q1rngh49OjRzMnJqeTyKHENIY9ZSkoKU6lU7M0336zsrFQZVCakpIrQ39/fKu5zzz3HmjZtWmaaANigQYMEYQUFBeYuPzqdTrDs2rWLAWC7du1ijDHWvn17FhYWZpXuunXryqwIZ82axQBY/Xg2Go3MycmJDR8+3BwWHBzMOnToYLWfDh06sD59+pjfF68I//rrLwaAvf/+++Y8lFYR0j1CUunu3LmDI0eOYNy4cZBIJIJR+msrKhNSluJPLgPi7m0XfwpVzFPIxSe2NbFneMWMjAzIZDKrB4A4joO/v7/Vk94Pc5ydOnXCwIED8f7779s1KwXdIySVbvXq1Vi4cCFCQkLw448/ok6dOpWdpUpHZUIqWvEng8U8hezl5WXuIlRUcnJymfv18vKCXq9HWlqaoDJkjCE5ORnt2rUTcxglWrp0KVq2bIklS5aUGZdahKTSxcbGwmg04ubNmzQgdiEqE/K4iXkKOSIiAhcuXMB///0nSGPTpk1l7sc0uW/xyX9//fVX5OfnP9Tkv7Y0bdoUY8eOxRdffIHExMRS41KLkBBCCAD7n0KeNm0a1qxZg759+2LhwoXw8/PDxo0bcfnyZQCARFJyG6tXr16Ijo7GW2+9hZycHHTu3Nn81OiTTz6JF198sdyOJzY2Fj/++CMOHToEJyenEuNRi5AQQggAoHnz5jh9+jRatmyJd955B71798a4cePwyy+/CFpqgYGBiIuLQ+PGjTF+/Hg8//zzUCgUWLhwIQCU2EUK4C/Jbt++HTNmzMDatWvRr18/fPTRR3jxxRdx8OBBq77HjyIwMBDTpk0rMx5NzEsIIaRcvPrqq/jpp5+QkZEBhUJR2dmxW41vEa5cuRKhoaFQqVRo06YNjh49WtlZqnRHjhxBTEwMAgMDzb/OimKMITY2FoGBgXBwcEBkZCQuXrwoiKPRaDBlyhR4e3vDyckJzzzzDO7cufMYj+LxW7p0Kdq1awcXFxf4+vpi4MCBuHLliiAOlZ1tX331FcLDw+Hq6gpXV1d07NgRu3fvNq+ncqt+Fi5ciNWrV+PgwYP4/fff8eqrr2L16tWYPn16taoEAdTsDvWbNm1icrmcffvtt+y///5jU6dOZU5OToLOmbXRrl272Ny5c9mvv/5qNdUPY3zHaxcXF/brr7+y8+fPs+HDh7OAgACWk5NjjjN+/HhWp04dtm/fPnb69GkWFRXFWrVqZTV/Xk0SHR3N1q5dyy5cuMDOnj3Lnn76aRYUFMTy8vLMcajsbPv999/ZH3/8wa5cucKuXLnC3n77bSaXy83zHFK5VT9LlixhjRs3Zo6OjkyhULAWLVqwTz75hBmNxsrOmmg1uiJs3749Gz9+vCCsadOmbPbs2ZWUo6qneEVoNBqZv7+/uSMqY4yp1Wrm5ubGVq1axRjjJ3mVy+Vs06ZN5jh3795lEomE7dmz57HlvbKlpqYyACwuLo4xRmUnloeHB1u9ejWVG6l0NfbSqFarxalTp9C7d29BeO/evfH3339XUq6qvlu3biE5OVlQbkqlEhEREeZyO3XqFHQ6nSBOYGAgWrZsWavKNjs7GwDg6ekJgMrOXgaDAZs2bUJ+fj46duxI5UYqXY2tCNPT02EwGKxGOvDz87Or02dtZSqb0sotOTkZCoXCauLW2lS2jDHMmDEDXbp0QcuWLQFQ2ZXl/PnzcHZ2hlKpxPjx47Ft2zY0b96cyo1UuhpbEZoUHz2BMUZzrdnhYcqtNpXt5MmTce7cOfz0009W66jsbGvSpAnOnj2L48ePY8KECRg9erSgQzaV2+M1ZswY86zuph9zJhqNBitWrEBERAS8vLwgl8vh5eWFyMhIfP3118jNzQXAt+7d3d3Rt29fq/Q/+eQTcByHESNGWK177733wHEczp07BwD49NNPzXnhOM6uiavLU42tCL29vSGVSq1+Laampto1Hl5tZZr9vLRy8/f3h1artRrDr7aU7ZQpU/D777/j0KFDqFu3rjmcyq50CoUCDRs2RNu2bbF06VK0atUKn332GZVbJfL398exY8ewceNGc1haWho6deqEGTNmoEmTJvjmm29w8OBBfPfddwgPD8esWbMwceJEAPy0R127dsX//vc/6PV6QdqHDx+Gk5MTDh06ZLXfw4cPw8vLC2FhYQCA5557DseOHcO4ceMq8GhLVmMrQoVCgTZt2mDfvn2C8H379qFTp06VlKuqLzQ0FP7+/oJy02q1iIuLM5dbmzZtIJfLBXGSkpJw4cKFGl22jDFMnjwZW7duxcGDB83jLppQ2YnDGINGo6Fyq0RKpRIdOnRAeHi4OeyFF17A+fPnsW/fPnzzzTcYMmQIunbtioEDB+Lzzz/HzZs3ER0dbY4fFRWFvLw8/Pvvv+Ywo9GIo0ePYsKECUhJScGlS5fM67RaLY4dO4bIyEhza97f3x8dOnQQ/LB8rCrrKZ3HwdR94rvvvmP//fcfmzZtGnNycmLx8fGVnbVKlZuby86cOcPOnDnDALCPP/6YnTlzxtyt5P3332dubm5s69at7Pz582zEiBE2H2WvW7cu279/Pzt9+jTr3r17jX+UfcKECczNzY0dPnyYJSUlmZei8wRS2dk2Z84cduTIEXbr1i127tw59vbbbzOJRML+/PNPxhiVW2UoPj0SY4z9888/ouejPH36NAPAli5dahV29epVFhAQwFasWGFed+TIEQaAffnll1ZpmebHTEtLE39Aj6BGV4SMMbZixQoWHBzMFAoFa926tflR99rMNHdX8cU0Wa7RaGTz589n/v7+TKlUsm7durHz588L0igoKGCTJ09mnp6ezMHBgfXv358lJiZWwtE8PrbKDABbu3atOQ6VnW1jx441fw99fHxYjx49zJUgY1RulcFWRbh48WIGgO3du9fudAwGA/Pw8GC9e/c2hy1fvpwFBAQwxhgbPnw4e/bZZ83rFixYwABYTerLGFWEhBBCHiNbFeH48eMZAHb58mVBuNFoFEzUW7wVPnDgQObk5MR0Oh1jjLGYmBj23HPPMcYYW7lyJfPx8TF3tI+KimK+vr4281RZFWGNvUdICCGkfPz222+CiXrd3NwE66OiopCfn4+TJ0+a7w9GRkYC4KdsSktLw8WLF6HRaHD8+HFERUVVwlGUjCpCQgghAICgoCAAQEJCgiA8MjISJ0+exMmTJ9G/f3+r7UwV26FDh3DmzBncv38fERERAPgZLXx8fHD48GEcP34cBQUFVBESQgipmnr16gUA+P333wXh7u7uVpPzFtWyZUtzZXf48GH4+fmhadOm5vXdunXDoUOHcPjwYQCgipAQQkjV1LZtW/Tu3RvffvutqJl6OI4zD4m3b98+c2vQJCIiAnFxcTh06BACAwPRuHHj8s76I6EZ6gkhhJj98MMPiI6ORs+ePTFmzBhER0fD19cXOTk5OHfuHPbv3w9XV1er7aKiovDLL7/gzz//xJdffilYFxERgYyMDBw5cgQjR458XIdiN6oICSGEmPn4+ODYsWP49ttvsXnzZvz888/Iy8uDq6srWrRogddffx0vv/yy1Xamy52MMasWYVhYGDw9PZGZmWl+iKYqqRWXRjUaDWJjY6HRaCo7K9UOld3DoXJ7eFR2j5der4fBYBCEKZVKTJ48GUePHkVWVhZ0Op25RTdr1izzjCtFNWvWDIzvkocWLVoI1nEch4yMDDDGbFaijDHo9XoYjcbyPTg71ZqKcMGCBfTFeghUdg+Hyu3hUdk9PgkJCZDL5WjVqlWl5uOzzz6DXC7He++9BwA4fvw4+vXrBw8PDzg4OKBRo0bmdSanT59Gz5494ezsDHd3dwwePBg3b958qP3TpVFCCKmFYmNjMXnyZACAg4NDpeZl5MiR6NKlCwBgz549GDBgAIYNG4YNGzbA2dkZN27cwL1798zxL1++jMjISDzxxBP4+eefoVarMW/ePHTt2hVnz56Fj4+PqP1zjDFWrkdUBeXk5MDNzQ3Z2dk2b/KSklHZPRwqt4dHZVd73b17F02aNMGoUaOwcuXKEuMNGzYMhw4dwo0bN8znSEJCAho1aoTp06fjgw8+ELXfWtEiNF13Ns0oTuyXk5Mj+EvsQ+X28GpS2THGkJubi8DAQEgk1f9OlFqthlarFb2dQqGASqUqM97q1auRn5+Pt956q8Q4er0eO3fuxKhRowQ/lIKDgxEVFYVt27ZRRWhLZmYmAMuoCUS8evXqVXYWqiUqt4dXk8ru9u3blTfFUDlRq9UIDXZGcqqh7MjF+Pv749atW2VWhkeOHIGnpycuX76MAQMG4MKFC/D09MTgwYOxbNkyuLq64saNGygoKBBMHWUSHh6Offv2Qa1W21XxmtSKitA0EkKdRXPhfscJeiVQEGiEc7wEjmlGTJ27GfN+G4FNz36OVz58HQ4ZBhR4ScEZAY8NJyBt2QTsWjwknh5I7xkEzgjc7/kAeq0MKJDC8ZYMnlf0SO4og0FlRPAfOiR1VEKiB5T3gfutdWiyPBNX3vCAy3kF1N5AyO/ZuPsWhzrLgOTZRhiOuYPJgIKGWsjvKYAG+dAVyBGymSFvci7cht6A1MUZ90a3gEEFyPMAl9sGaF0kUHtw8LysRW49BTw2nMDNZe1gdDBA7qyFQmnAhlbrMGLj61DeB5zvGZH6tAby6w4I3paBGy96QhGaC02CC/xOMCT1NiBoK4Nm0n1k5Tug7kopshs4oKBfLoxnXOFxxYCUGC1CXzmH7OfbI72rFq5nlfBZdQKJ7z6F4KWncWPxE2gw9yxY6yYo8FNBomMwyjg4JeQh40k35NQHnO4BfnEZMFy5jqRpT0HvCDimAlonIOCzE0gb/xSYBOj7/N841VkKr/3uuLylCbzPPYDeUYacUAX0KkDvCBhlgN7NCM5HDYVSD8NFV8gKgIJmajSaeQOo6w9Oo0dyDz/4/ZWJm8M84RIP+B5NxZXXPQGlEZu7fo1JSyYjOzofuOYMnbsBKr98aBNd0PD7TNzp7YU6cTl4EOgIrZsUmc0Bt2aZyL7sCYOnDpAA0jQ5Gm5Ix+0FSigOukLjBnhcMSA/QIr8uoDTXUCiBzKf0INjgPt5GXy+PwujRoMbK1oDagk+7PETVrz+LNLDlHDIYHDd9A/SX3kKD/yA4KX/IvP5tuZzMuMJNzgl6+Hw3z0kPRMMVSZDXh0OWneASRgMzkY4X5fC/aYeqn3nkPJqa/iuPAEASHz3Kf4zOJKGxBgf1P3q/xA/qxU8LjF4nExGalQAvM/k4NZAVwQe0UJiYLjXUQmdhxFSNYcG36cjMVYJ1T5XZLUywDFeirrfXUDq8JZwvquH3kECvSOHrKaAwUeHRlP/g1GjQdrmxsjPV6LholzcXuSAhl7pSMj2QM59B6guq1B3XzaSurqByQDOCGja5yEi6DoO/dUKro2zYNjvifz2BUCSCq5NspCV6AaHJCl0zoCicQ6kR10ROvAmbv1WH1oXQKIDZGpAlcVwvz4HmRoI2pGOy5Pd4Rgvg/ypLOiPeUDrDqjSgDpbbuLyvHrgZAwypQ4N3snA7Q/dIT3qivwghuCdatwaJYHcQYeIoOuIO/gEpGr+HFRlAg8CGertVUN+MQHX32oCj3/UOLvtPbi4uFTSf7/yo9VqkZxqwK1TwXB1sb91m5NrRGibBKSnpwtacEqlEkqlUhD37t27ePDgAZ599lnMmTMHn376KU6ePIn58+fjwoULOHr0KDIyMgDA5pOrnp6eYIwhKysLAQEBduexVlSEpskfJSoVpEoVmBKQOBghVUogkxvh6CKFRKWCs4sEUoUKMrkBUgVfEco4OaRSJRingESigFShAmcEJI5GSKQyAFJIlTLI5HpIVDIwByNkMimkSiUkUkCqACQOUsikSkgcVJAqFZCqAJlUDakjB5kUkDoaAaUKTAZIHCSQqhRgjgZIIIdMxiB11PL54BSQKlWAEpDqAJncAINCAqmSg0wmgVShgIyTQ+KgAhwMkDhKIFXq+eNSqSBVAjK5ERJHDlKVis+TSgWpoxYSlQoyOYPEwQCZjMHgpISUqfhjUfBxOCVfNhJHCZ8fhYrPr1LJv1epzPuXcXIwmQoyuQoS8BWhTKrjt1Hx5SKTKsFxcv4zKQyTKgvLXKkCkwBKZzlknBRyJ/7YZTIjIJNBqlCAKQGmBDg5YFQZwTkCUqUOUKkgNQISB0DG8YlyUv44zMdcuH+JgwpQGs2fvdTRAKhUMDgYIHU08OUiVfL7lmogk6tgUEj5Y3Dk02IOUkACSFRyPq6jkk9LCfO5ZDpmiQSQOPAVoVQhg4xTwMgZ+XxwEji6SCGTqSBVKiFVMHM5S1Uwvzadk1KFCjKZHjJJYf7kDFIlB4mKrwiZgxFSpRQyud5cpjJODgD8+VBYBny4orBcGJ+eQgWZtPC8kEkg4RikKiUMDkZIwAmOU+Jg4PfDKQq/P3pALgFTFObFQWo+TqmjEhLGpy11VPKfq04JiUZVWMZq/rMvrAiljnoonAvz5qgElCpIHBn/GTuavlNSGFSA1FEDqVJlPlekSr68pQyQKhikKg5ShiLfRRmkjkowZeE5qQRkEgUkDipwMgaJSsqXhSNfRhIVg8z0HXWUWvLF+HNQqoQ5jrk85cL/QTWBkzO/2MtQ+BRK8Rb+/PnzERsbKwgzGo1Qq9WYP38+Zs+eDYAf51ShUGDatGk4cOAAHB0dAZRepmLLu/pftCaEEPLYGMFELwB/eTg7O9u8zJkzxypt09W76OhoQXjfvn0B8F0mTHFMLcOiMjMzwXEc3N3dRR1TrWgREkIIKR86ZoBORGcDHeMfVnR1dS3zKeDw8HAcP37cKtzUuUEikaBBgwZwcHDA+fPnreKdP38eDRs2FHV/EKAWISGEEBEetkVojyFDhgAAdu/eLQjftWsXAKBDhw6QyWSIiYnB1q1bkZuba46TmJiIQ4cOYfDgwaKPiVqEhBBC7GYEg0FE5SamIuzduzdiYmKwcOFCGI1GdOjQAf/++y8WLFiA/v37mzvdL1iwAO3atUP//v0xe/Zsc4d6b29vzJw5U/QxUYuQEEKI3SqyRQgAmzdvxrRp0/DNN9+gb9+++OqrrzB9+nT88ssv5jhNmzbF4cOHIZfLMXToUIwZMwYNGzbEkSNHRI8qA1CLkBBCiAgGxmAQcY9QTFyAH+7t/fffx/vvv19qvDZt2mD//v2i0i5JrW4R5jTXl7juQXQuslpYf4Dp7Q3ILBxY3fU03wfm/PSvzOvrNU82v64T9wD+B1LgfkouSEPS1HJdW/NBHrSnPaz2ozjlZH7t9Ya4R4G9T/HxlUXS8GyXan7tdMoyrqB78wyw025Wabi9KbcKM2k8+RauftMeX87/whx29Zv2gjhXv7EM4qvc+Y9gnd+/whHmfXvfAQBoe2fDp/ddyOoElrjvonJbaqFtXsCn0STdZpwrKxtB6++CpE8UVutSPin5d2Dn1leAM/yNfa2/dR+w6O6nkXvKGwAwr9MOLOm0lY/rZ7u/mN9J/pj99ieh8avC8ihedo/Ctxdflv26n8LkbvsE6wIO3S9xf5JAf/NrvZ8bMtrpzO/7fXwYAMCa52F05BG4tsi0Os78FhpcXtEEADBsGX9/J7PIBASSugGQ1QlE4AzhqCS5cwJRcNoTL7QSlgkA/DPzcwxseA57zoWVdshWcudYzp86Rx6UGld9mu+LFtjWMo6l5yk53E7x50v8Z5bv5tCoY4Jty8qXzyn78lvd6MBEL1UdtQgJIYTYzcAsfQPtjV/VUUVICCHEbsbCRUz8qo4qQkIIIXYzgoMB9t+uMYqIW1moIiSEEGI3HeOgY/ZXbmLiVhaqCAkhhNjNILJFKCZuZaGKkBBCiN2MjINRRCtPTNzKQhUhIYQQu1GLkBBCSK2mZ1LomP1d0PXVoEVYqzrUu16TwvffB/A7rcGyfhsBAHnP5eCbITEwKhmmjJ4MAMgOkQq2K6jngh6n0nHz1VBz2IkuqwAA7uf43xJ5ATK8PfBXAMD+H9cAANqsPAPVGkvneZdzlk7dX/32DTRXLB3ZOz77f/jylVXY0f0LGFTMHD8vkN9m773/A0LqwPe0Gj5ndCjokgfHO/kAAIMCkMxJgaHIgOsul/kO8ZorbubjAoD7I/j8eHdOwpLd35vD23W4irwACVzPyZEfyG/LLrngt83fYP7cdTj/FF9e9d+8LCgbqdKAGZO28PlQMkScyYH7Jf60yq/jgOBZV5D4Sxj0DhyuvWQZeT63cwHU9Szvxw350/z63qAQPOicjzYjzuF0Lz8AQHqnLPiezMe1UQqobucgu0uBOb5fJ74ztPKcIwznLWV6IPJzAEBib74MnZIN+PCPdWjS8RY+mmP5/FzOKTBt1CQ4JelxptNqBHdKFBxjYm8F8oOM+Hr718gPkGH2u9/DFqOSIbGPA84/tRFjpvwBgwoIfusKfP/NNR+zbA2f7+97fS3Y1umyHJ7npPhy2GAY305H+LD/4HUsBbKQYPj+kwvPSwwRZ/OQ0YXvkF5Qj+/QnvC8AePj4gAAOcH8Pxzv/xM+sO5wJw9RZ+8j8BvLcRmU/DmmrucO3zM63HkmAN7njNA8l4WEvvxADI6fpmLaMzsAwHwemsukjwMudODPiTXR35rDc4P5v83f5GcG8LgM7Ir6HHdiAnBvUAgA4HjXlQAgOP+LY52zBe9VKRxyz3mhwBc4020V5sRsw0fN+fOux+B/ceSFD6G54ganFP7YnZL5v0+uOAtNl1yw4cIBFzilAY4pfBl8+Np35vAxRy0d+6WXncz5NKW766fVkCn1uNyZPwccCsep8Ot0D74n8zHjmR24/qIMQ4/z35PcelW/EhDL1CIUs1R11CIkhBBiNwOTwCCiRUgd6gkhhNQoRnAwiriYKHbQ7cpAFSEhhBC70cMyhBBCajUdk0LHpGVHNMevwMyUE6oICSGE2M0ICQx0aZQQQkhtJf5hGaoICSGE1CBGSGrcwzK1qh9hwyHXrcIct9juy6S76WwVVn9Noo2YD2/2wK3m139va1VKTPE6DP8/q30AgOsW2xPH2jJh8O6H2nfdETdthjf6Pq/U7QpuueLWLb9S4zTcWPJkypXl1+TWj5xG12GnS11veN72xMNBm4W/ZW8Wlt+BxEbYflf8OaXYYj1JdFG77rQodX1xziPulbiu+LkpxpLdg8yv9/3Wtsz02C/eovcxasiBMtO1pcHG6jDx0MPTMqnopaqjFiEhhBC7GZkERhGXRo10aZQQQkhNYhD5sIyhGlwapYqQEEKI3YwADGJmn6i4rJQbqggJIYTYTfzDMlX/UZRKz+HSpUvRrl07uLi4wNfXFwMHDsSVK1cEcRhjiI2NRWBgIBwcHBAZGYmLFy9WUo4JIaT2MnWoF7NUdZVeEcbFxWHSpEk4fvw49u3bB71ej969eyM/P98cZ9myZfj444/x5Zdf4uTJk/D390evXr2Qm5tbSsqEEELKm6kfoZilqqv0S6N79uwRvF+7di18fX1x6tQpdOvWDYwxfPrpp5g7dy4GDx4MAFi/fj38/PywceNGvPbaa5WRbUIIqZXEPyxT9SvCKpfD7Gx+HjJPT08AwK1bt5CcnIzevXub4yiVSkRERODvv/+2mYZGo0FOTo5gIYQQ8uiMjBO9VHVVqiJkjGHGjBno0qULWrZsCQBITk4GAPj5CTta+/n5mdcVt3TpUri5uZmXevXqVWzGRQpZoDO/Vr5l6bi/YNI4u9Oo/6qwc7+sX1qZ2+S3sUxm6/K68GJA5ht17dpv+hBHAEDjVy0TmH7/cn/Be34936E/c7AKtjR8+YrNcJMGryWUur7Ry5YJgh1fV8B5sfUACADQZOI1AEDAdK05TD+D/5HlN91253zJdFeb4WL8MaYbAOHxN3r1miBOZmv+PChedoq+JXdCb/TSv+bX6a0f7rH04vsrL00nWT7Tu0NL75hflqBJmWXGeeOD8ebXg996o8R49V/jy9Olje1BCZZNeLHEbUOmZpWZD4A/B2sLPZNBJ2LRs0q/8FimKlURTp48GefOncNPP/1ktY7jhL8qGGNWYSZz5sxBdna2ebl9+3aF5JcQQmobmqG+Ak2ZMgW///47jhw5grp1La0Tf39/AHzLMCAgwByemppq1Uo0USqVUCqVFZthQgiphcSPLFOl2ls2VXoOGWOYPHkytm7dioMHDyI0NFSwPjQ0FP7+/ti3b585TKvVIi4uDp06dXrc2SWEkFrNALGtwqqv0luEkyZNwsaNG/Hbb7/BxcXFfN/Pzc0NDg4O4DgO06ZNw5IlS9CoUSM0atQIS5YsgaOjI0aOHFnJuSeEkNpFZ5RBarS/6tAZaYi1Mn311VcAgMjISEH42rVrMWbMGADArFmzUFBQgIkTJyIrKwtPPfUU/vzzT7i42D+TAiGEkEfHwMEo4r4fq4n3CJOTk8337coDs2Nkco7jEBsbi9jY2HLbLyGEEPHET8xb6XfgyiQ6h0FBQRgxYgT++uuvisgPIYSQKoz6EQJ45513cPToUXTr1g1PPPEEvvvuOxQUFJS9ISGEkGrPNLKMmKWqE53DefPmISEhAT/99BNcXV3xyiuvoG7dunjjjTdw48aNishjhXj//Rfsindvlu0nU7usmilqf1cmWbp6aH2dSow38jNx6RZXUn4fVdPVEyokXXvya08ctbd1d5mYL2aJzk+bb6aJ3qY4e8pq8ieTAACJb9ie3T55dv0St32Uz1jstt8v7l/iOtNxzlg+vsQ4ADDsc2Fn94iVbwi+D0VdGV/x9/0vT/CqsLTXL4qpsLSrCj2Til6quoeqqqVSKYYNG4YjR47g7NmzGDJkCFatWoUmTZqgf//+2Lt3b3nnkxBCSBVgYJzopap75DZrWFgY+vbti5YtW8JoNOLAgQPo168f2rZti6tXr5ZHHgkhhFQRdI+wiPT0dCxduhShoaEYOnQoZDIZNm/ejJycHGzfvh25ubnm7g+EEEJqBlY4soy9C6sGT42K7j5x4sQJrFixAlu2bAFjDMOHD8fUqVPRurXlXkdMTAxkMhkGDhxYnnklhBBSyXSMAyeictNVgxah6IqwY8eO8Pf3x+zZszFhwgT4+vrajBcSEkJDoBFCSA1TE8caFV0RbtiwAcOHD4dcLi81XrNmzXDo0KGHzhghhJCqxyhyZBkxcSuL6IrwhRfs63ZACCGk5hH7JGh1eGr0occavXDhAi5dumSzM/2oUaMeKVOEEEKqppp4aVR0Dh88eICePXsiPDwczz33HMaMGYMxY8bgpZdeMi9VVWcPywzhPv/wM2Cn9NJaxfP/5G/B+zx//vfC3cFBgJH/deN7Rji7eYHtW6Wl6ul43fzar8cdPt1TakGcB778/sI+m4CCOkU6G9fxRWLfkmdS7+5+SbAPg5FDk/n3kdybz7fWX9hxucBfBbVP4T59+H32dP7PvN7nrP2TqXT14veZ7y88veJjLPmV1AtEfkApl9fr2C7Q+H5K3HnaG5I6lrkpix+Lie8Z4Wd7twf/Vx1ge0CD271k8Dmjh8bfCXqjsBNwkzU5AIAHdnzOJZUVV8cyRq/vyXw0nxtvft/D/T9BXEncaQCArp4nAKCNLz+5tKxuHQD8Odl0zgUkRlvKOOiLczb3e7uve4l5Lf4ZpPXS8C8Kz3PPY0kAgAIf61/1puP0PZlrDmu0SjgJdgefePgVO6d9zugACM9/XbFO18YAYad3p6g0AIDWlz9/B0T8A3VhlNAe8eb07nU3AgDu9bB8BizQB1m91PBc5CBIMyNKY35tMFr/K+zpfNEq3eIMkdl8vgrPQc/jyUjoZ5mtvt7XF2xuV50ZIIGe2b/UyJFl3nvvPcTHxyMuLg6MMWzduhX79u3D4MGD0ahRI5w+fboi8kkIIaQKoH6EAH777Te89dZb5idCg4KC0KNHD2zZsgWtW7c2T6tECCGk5hHTh1DsZdTKIjqH8fHxaNq0KaRSKTiOw4MHD8zrnn/+eWzfvr0880cIIaQKoRYhAHd3d+Tn5wMAfH19ce2a5b6bTqczryOEEFLziLk/aFqqOtFPjYaFheHq1avo06cPoqKisGTJEjRq1AgKhQILFy5Eq1atKiKfhBBCqgCxrbzq0CIUXRGOGzfO3ApcvHgxunTpgoiICAB8a3HXrl3lm0NCCCFVBlWEAIYNG2Z+HRoaiqtXr+LgwYPgOA6dOnWCp6dnuWaQEEJI1VETK8JHvnjr5OSEmJgY9O/fv9pVgk1W5plfB+219AvMeI1/IrbJ5/fMYd/s6G1+3fizxBLT1K4NwBMnnytz3z2PTilxXdAeXZnbJw7whs6F8fF38H2sArbeLHM7k/inlRh8eBJU33kAAO5EM5vx2hwbV2o67gccza+D9uqw7vceNuO5/HRc8D5xQOkd8hKf8S5xnc4FSBzkj8arNAj+VoKEpxUlxi2Kc9Eh5tBkQd+7ogwufN+zxL7WE4neeM4NPf5X8mcGAEF/CvuaBf+RK3hf1jGXZd3vPXB3SLBV+NS9/GhP914Ot7md1gVY83svQVjQ3rLPseLqfMuXs+N37lbrGn3Nf39ujapnDivte1IWU5/TXT/y30U/p1yrOHV6Wqcvc+H7jsqcLceX8Iz1/yXf/Qo4umiswmfvsf3dNaX75IlRcD1g6Yfq48z/Dyl+Di7ZNQgAkPRiS5vpVWcGxonrR1gNKkK7WoSJieJO6KCgoIfKDCGEkKqtJrYI7aoIQ0JCwHEixpYz2D8KCSGEkOqj1laEa9asMVeEOp0OixYtgqOjI4YPHw5/f38kJSVh8+bNePDgAebNm1ehGSaEEFJ5am1FWHSm+bfffhvNmzfHzp07IZFY7rXMmzcPTz/9tKBfISGEkJqFMQ5MROUmJq4tq1evxiuvvAInJyfk5eUJ1p0+fRqzZs3C8ePHIZPJ0L17d3z00UeoX7++qH2Iflhmw4YNmDhxoqASBACJRIKJEyfi+++/F5skIYSQauJxdqi/e/cu3njjDQQGBlqtu3z5MiIjI6HVavHzzz9jzZo1uHr1Krp27Yq0tDRR+xHdfSIjI8Pm1EsAUFBQgKysLLFJEkIIqSYeZ4tw/Pjx6NatGzw9PfHLL78I1s2bNw9KpRI7d+6Eqyv/lHGbNm3QqFEjfPTRR/jggw/s3o/oqrp169ZYuHAh0tPTBeFpaWlYuHAhnnzySbFJEkIIqSYe11ijP/zwA+Li4rBy5UqrdXq9Hjt37sSQIUPMlSAABAcHIyoqCtu2bRO1L9EtwuXLl6Nnz54ICQlBjx494O/vj+TkZBw4cAAAsH//frFJEkIIqSYeR4swNTUV06ZNw/vvv4+6detarb9x4wYKCgoQHm7ddzY8PBz79u2DWq2GSqWya3+iW4QdOnTAyZMn0b9/f5w4cQJr1qzBiRMnEBMTgxMnTqBDhw5ik3yspNkPIMsunLBVVsbhy6RQ5AL3ez4oFm75/aDI4zuiB3bnJyQ1zefq2+2eYBN5DgeF8D4vACC7iWVS2YR+wo7c8nzAuWeq+f3tPlLIcjRQZFs6C6dE8B2ZM57nW+IK637HpWISy0nassc1yPMA957Jgjix7/OTLbt3TcHdZ0MBANKwpub18f1VgvcAwIU3hrHnfdx7VnjTOqObpRNzRi81n98PSu9objomabbwkjyTSMCklvzL8wFZYRmruvFXLAwt+f3nNxN20J+5bDzymnlBnscgt/G5mHTuJJwwN7h7ApYseRGnPm0NAGjf+TIAYOkqYUfs+Bj+c733bH1Is4qdP0XI8yxlKS88l7JeaMuvzM5BfD8HSLOF29s6J9Xdcoustz2JrGnbop9Vak8t5IXfB0W2sNtT/huWiYQdeqbBWFjWRc8ZEyblYGxh+azvDAkSfE+Kk+fY/ucozRV+J4vO1yvLByZ3sf1DOznaeoJt765JVmGu3VIhL7aPhKf5fBq7ZVvFL2humQD60GcdUVovMmmO7VtGNY2RcTAY7V9MLcKcnBzBotFYD2hgMnHiRDRp0gQTJkywuT4jIwMAbA7i4unpCcaYqNt0oluEANCsWTNs2rTpYTYlhBBSjRnBgYOI7hOFcevVqycInz9/PmJjY63i//rrr9ixYwfOnDlTZv/10taL6fv+UBUhIYSQ2ulhL43evn1bcD9PqVRaxc3Ly8OkSZMwZcoUBAYG4v79+wAArZZv8d+/fx9yuRxeXl4ALC3DojIzM8FxHNzd3e3OI1WEhBBC7GZkHLiH6FDv6uoqqAhtSU9PR0pKCpYvX47ly5dbrffw8MCAAQPwyy+/wMHBAefPn7eKc/78eTRs2NDu+4NAOQy6/aiOHDmCmJgYBAYGguM4qxnux4wZA47jBEtVvw9JCCE1FWPiF3v5+/vj0KFDVkt0dDRUKhUOHTqERYsWQSaTISYmBlu3bkVuruX+eGJiIg4dOoTBgweLOqZKbxHm5+ejVatWeOmllzBkyBCbcfr06YO1a9ea3ysU9s02QAghpHwZjRJwRvvbUEYRcVUqFSIjI63C161bB6lUKli3YMECtGvXDv3798fs2bOhVqsxb948eHt7Y+bMmXbvE6gCFWHfvn3Rt2/fUuMolUr4+/uXGocQQkjFe9hLo+WtadOmOHz4MN566y0MHTpUMMSaj4+PqLQqvSK0x+HDh+Hr6wt3d3dERERg8eLF8PV9tLndCCGEiCf2cqeYuCVZt24d1q1bZxXepk2bcum7/lD3CC9fvowRI0YgICAACoUCp0+fBsA3VQ8dOvTImSqqb9+++PHHH3Hw4EEsX74cJ0+eRPfu3Uvtg6LRaKz6rBBCCHl0fEXIiVgqO8dlE10Rnj17Fu3atUNcXBwiIyMFcw/m5eVh1apV5ZrB4cOH4+mnn0bLli0RExOD3bt34+rVq/jjjz9K3Gbp0qVwc3MzL8X7r5TG4GAd1uBTSwdlpa/tztHqlXXw+bWostO3/0EmAIBihSdux/CdwZsstX8G+uKaeqaWGefOqoYAAMkKbyT2t+6oapI2tLld+/T5wkZhFtHoE8vAAN9d7mi1/uD5ZnbtpziD6tG/ee3cb9kMz11p/7lUktwR9j/s1eC9s7bDPy2503xRts5nE70KaPKx7U7gzj75gvcOKzxEp2/LnWeEtzgS+zgCADo6XzenVVqa69b2wbspnYtkzHruU1OaYoR53is7UhHvplpmnhf7na7uHtcQa4+T6Ipw9uzZCA8Px/Xr1/H999+DFanu27dvj5MnT5ZrBosLCAhAcHBwqdM9zZkzB9nZ2ebl9u3bFZonQgipLcS1BsX1Oawsou8R/vXXX/jhhx/g6OhoNRO9n58fkpOTS9iyfGRkZOD27dsICAgoMY5SqbTZWZMQQsgjYoWLmPhVnOiKkDFWYveFrKws0RVQXl4erl+/bn5/69YtnD17Fp6envD09ERsbCyGDBmCgIAAxMfH4+2334a3tzcGDRokNuuEEEIeldhWXjVoEYq+NBoeHl7iFBd79uxBmzZtRKX377//4sknnzRP3zRjxgw8+eSTmDdvHqRSKc6fP48BAwagcePGGD16NBo3boxjx47BxcWljJQJIYSUN6ORE71UdaJbhFOnTsXIkSPh5OSEF198EQDfm//gwYNYs2aN1eSJZYmMjBTcZyxu7969YrNICCGkojBOXCuvGrQIRVeEw4cPx40bNxAbG4vPP/8cADBkyBDIZDIsWLAAMTEx5Z5JQgghVUNl9COsaA/Vof7tt9/GqFGjsGfPHqSmpsLb2xvR0dEIDg4u7/wRQgipSmrgwzIPPeh23bp18fLLL+Ptt9/Gq6++Wu0qwewW7pAkJiP0B2Gz/YGf5bXhVgIkhfN96gM08Pv0b/51fAIyG1v/hvBZrITzPS2e9LtrtU5S2GWuwJ8/K0L90wXrczbVhWdoFjKbWXdKyg82QNlVON1IgZ+wH1lOKH8cEuv5Sc1SegSg/gZ+/wZfLQL8s+B0Tw0AcPQV9ht7EMxP+Bvmz09umtWYnyE1L9SI3GIfNRdi6VupCxRmIDfE8i2o/7WlH2SBHxPkN3ShXrCd2s+I5nMTrI6p8bephesZZLfToLqTDaOvcHAFvR9f2FlNrB/qqhuYCZlfyROoZjWWgfO1PVhDff+0ErcTpiGFLoQv16LHbzrmnBAJspo5IrN3Q0htfF45oRwkrVtYhesCtSjwtZRHbnRLNF6XC8eAkmcWNp0nps+laFkW2Bi1MPh74b+EvJaWEZwc7vKDGzveKzAfZ4EvkNXMGfLbwvMzL9QIfTz/+WU2tZzTecFGZDWRm78PumC1eZ3GTw+ploPazwiNH/9EumOK9X/R45+0w95fnkIbz9vw9M1F8uYQtAi29AMsmibAn0t1v5fhXjcnvgx0gtVwCeEn5JVwDIpA4feguHA/fj97P+tiybe/znwO5rX0RePVaeACau4kvdR9oojU1FQkJCSgoMD6A+/WrdsjZYoQQkjVxBgHJuIBmBpZESYlJeHFF180D6VmetCF4zgwxsBxnFX/QkIIITVEDbw0KroinDx5Ms6cOYMPPvgA4eHh1HGdEEJqFa5wERO/ahNdEcbFxeGjjz7CSy+9VBH5IYQQUpVRi5C/BCpmEGtCCCE1iJHjFzHxqzjRT40+++yz2LlzZ0XkhRBCSBVn6kcoZqnq7GoRmuYbBIBhw4bhlVdegdFoRExMDLy8vKzit27duvxySAghpOqorZdG27ZtC46zNG8ZY/jyyy+xYsUKQTx6apQQQmq42jrE2tq1ays6H4+NzssJBpUUTndLnuF+6owtWPjb0FLTcUpheODDQeNm+ZA13krICiw/An77pTOUWZZtNG6l503xowfkDyzba10B3AOY0tJ5XuvpAIOD9RXtBqviS08cgPfZXOidbM8cUnSfjmkAFMIO+6aOzY1XZ+HaKOGkvQHfKwHYLs/G32Ygv7HtSV1NskMVcDcW6Xhe2NGb+XmBS7J0Ytd7OUKekltq3g1FHmJ2SuLLUu+vBSC1vY0bB32RbRxTGIr3jDUEaHCj51pEX37aHJZbT/jlzq/H4HkF0AVo4XZNDr9T/GfU+NsM6L2dzfHubKoPoy/glKSHQ0I2UiK9zetM51L9VTcBrQ7w8QTyH0Dv5QxbXE8lwehpWffhlG/x6sEx0LjbPtZ/N4fD1qev9VLiQQCDY6oMDmk6aAJ0kJvK467tiahNx5njzMEpydJDfdcry7Arrxkaf5cD0xnklGxAUicJpFoOTMHgmGJEtjNfPnKl3kbqtulcGaRpHNSe1v9Yk34KBebCnKbBwKe/45dOQOGgAt4X9NBEWqfr/L0b8FbZ+y/6mWs8rPOg93SC4x1hh/wF4zfg2a/KTrs64Ri/iIlf1dlVEY4ePbqi80EIIaQ6oIdlgO7du+Py5cs21129ehXdu3d/5EwRQgipothDLFWc6O4Thw8fRk5Ojs11ubm5iIuLe+RMEUIIqaJq68My9kpKSoKjo2N5JkkIIaQqqa0V4W+//YbffvvN/P69996Dj4+PIE5BQQEOHz5snmmeEEJIzcMZOXAi7vuJiVtZ7KoI//vvP2zZsgUAP7LMwYMHIZEIby8qlUqEhYXhs88+K/9cEkIIqRpqa4twzpw5mDNnDgBAIpHg0KFDaN++fYVmjBBCCHkcRN8jNBqNZUcihBBSI3EQ2Y+wwnJSfh56hvrqSqrWQ6oRVuaZT7gDANLDrGeHZ2X8VLA1w7gtTGp/3NLSlqr1kKpL/jFiK7+m40ru5PJQ+6xIZZXvgzahYDKAeyocEnXpIxZJtMIZ2O0h1fDHnNHS+rMvLxK1zmZ4ajdvGIv0fZdqgMxwV0tAAT/TuqRAuL2pzNIjAm2mK1WX/F/KVnlLC8tVVsp25c1YwudeUjgASDSP9i81uT2fuNH2eAOPTKKxf3CAas3Uj1DMUsWV61OjhBBCarjaeo+QEEIIAWrxEGuEEEIIgBrZIrTrHuGMGTNw+/ZtAEBiYiJ0Otv3PAghhNRwNXCINbsqwk8//RRJSUkAgNDQUJw5c6ZCM0UIIaRqMnWoF7NUdXZdGvXw8EBKSgoAy5yDhBBCaqEaeGnUroqwQ4cOGDdunLkT/cyZM+Hu7m4zLsdxguHYCCGE1Bw18WEZuy6Nrly5Ep07d8bVq1fBcRyuX7+O8+fPl7hUB2lPOAAAkjop4Hn2PgDA+zzfb+vjKz2s4k9sGwdt33bwcc0DAGS04KApnG92Ux7/QpHN3ztdG3QUjd0sE8qa4pncGs3wbaOfAACpbTloPYxIf0qH5Gg90sP4Tk5ad0t852tyFOeQJEG7rpfh5MlPnHp3aIh5nbpw0lBTGt7n1WjqnMwfb2clNB6ALFmBFU03FuaP4UGycPJXx2v8FK7rQncBALJaAA3d0pEwwAvuVzkkd/XExswOAIDEPvx7AHil7VEAQHJnfhbixIGWiWcze4TC3TMfBZ4SOCZZrip4ffM35PctE/uq0vjj4+7nwvHULQAAO3EOAJDS3Q/1I+LhUGR7SbISGS8KJ0St75aJjDApkjs6QZKuQHJ7Gd5s9ye+arIR+mT+s09pZ/kd6HVBjRc7/I3MFhxYsgphqtvQujO0cr0NLk2JWSlP4tuGm8zxg79PgNbDiHDXOwCABj/zn4M0TY6sFkBCP7ng+Ises/tVI9LDLZ+pqewAwPNcDu4NqY8HHRqguFfaHoVjsjAsqZsbloZvR6B7NhZej7HaBgCe7HoVr7W1zApTdH8mDql83pI6K6BIlSPQLRsAkNbaFbuSWljFl6XyxwkA6eH8uZLUyRlzbg/AjpRwJAxwN8fNaCmFQyoHVRrgck2KzOaWfzvsmjO07kaEKVIE6auSpPgptw7/2k2NTQ1/N69T5DBI29/HifRgAEBWey0e9OInbI5tvQPsmjPea/M7GhV+Bx2SJEjqxJd3A/d0czpftPoJWg8j7vYxoJlTEoLcs7AwfAc0HoCmMPvJTynM6Yb+eA/rgvcDAOR5fL7WNfkRAH8OmsorOcoXc57cA7UHhw9vRluVXbVXA+8R2tUiDA4OxrZt2wDwQ6xt376dhlgjhJDayAhwYgYYqwaDkYnuPnHo0CE0b968IvJCCCGkqqut9wiLioiIAABcv34dBw8eREZGBry9vREVFYWGDRuWewYJIYRUHTXxHqHoipAxhilTpmDVqlWCAbglEgkmTpyIzz//vFwzSAghhFQk0YNuf/LJJ1i5ciVee+01nDhxArdv38aJEycwfvx4rFy5Ep988omo9I4cOYKYmBgEBgaC4zhs375dsJ4xhtjYWAQGBsLBwQGRkZG4ePGi2GwTQggpDzXwYRnRFeHq1asxZcoUrFixAu3atUOdOnXQrl07fPnll5g0aRK+/fZbUenl5+ejVatW+PLLL22uX7ZsGT7++GN8+eWXOHnyJPz9/dGrVy/k5uaKzTohhJBHxDH+YRm7l5pYEd68eRP9+/e3ua5///64efOmqPT69u2LRYsWYfDgwVbrGGP49NNPMXfuXAwePBgtW7bE+vXr8eDBA2zcuFFs1gkhhDwqahECbm5uSEhIsLkuISEBrq6uNtc9jFu3biE5ORm9e/c2hymVSkRERODvv/8ucTuNRoOcnBzBQggh5NGZHpYRs1R1oivCXr164Z133sGpU6cE4WfPnsX8+fMRHV1+HUiTk/new35+foJwPz8/8zpbli5dCjc3N/NSr149AECQPMMcJzeU/3Rk+UDGk+7QzchAergKykxgWKjtsVTleZaJNw0hBaXm3UFie2ByRaalI3iQzAWyB/x7yQO+I/2DEB3yZ2QLtgn46wH+bf2zMJ0c4PyvzSCTGnFpDv/j49K7web1IfI08z7Swy2TzsoK+53Lc4TD5MlyhLOVBvxVgPoyS5g8JA8OUv6YfA8nQ57Pl1+3iSf49fkMedP5fAcr0iF7IDzu+MWdIM8zQiE1mPNfGnnhle8HbUP5Fx1amfdz75cQwfaKYsciyZJDJdVBHaqFrPBjkj0APtrFdziXZ/OnvazIR5gepsLm37qBC82HPNeSnorjj3mq9xGb+VRxlnOCm8Z3CpeH8IMuJMwSxlUUHpPH/uvID7FMMizPZwgpPDczWvGfZVZD4XNs9VRZhccq/K9i+jxVMj1GBR23yp8iS4LLvzYWhJk+K2Wm9fHIhOMSIDfEOo45bkgeFFlAXuGxyB8ArwUcEsQJUmagINTyXQj4Xy70oXzBB8lcEHDcUn7yTJk5z/Ji50fb42MF7x88UCI11wVZmU5AvtSc3rw9zyLguB5zdw8zn6/yXJjPR1NYi8nn8dL+lwVpqmTCiXXry7WQPbCka9J9oqWce/w1GbJMmfkczA2F+bsBACPqnUSNQy1CvpKRyWRo3749wsLC0Lt3b4SFhaFNmzaQSCRYunRpuWey+NimZY13OmfOHGRnZ5sX08wZhBBCHo2o+4NiO99XEtEVYb169XD27FnMmjULTk5OuHXrFpycnDB79mycOXMGdevWLbfM+fv7A4BV6y81NdWqlViUUqmEq6urYCGEEFIOamCL8KEm5vX29q6Qll9xoaGh8Pf3x759+/Dkk08CALRaLeLi4vDBBx9U+P4JIYQIUYf6CpCXl4fr16+b39+6dQtnz56Fp6cngoKCMG3aNCxZsgSNGjVCo0aNsGTJEjg6OmLkyJGVmGtCCKmlaIi18vfvv/8iKirK/H7GjBkAgNGjR2PdunWYNWsWCgoKMHHiRGRlZeGpp57Cn3/+CRcXl8rKMiGE1Fpi7/tVh3uElV4RRkZGgrGSfzJwHIfY2FjExsY+vkwRQgixjVqEhBBCarOaeI9Q9FOjWq221BYcIYSQGqwGPjUqqiJUq9VwcHCwGhi7OpHeToPiThbcL5XcD7GojLYldIy/zM9y7pRssLm+OFXbTDgll32x3OWSZeZyxxQ+flJnR3OY/HYGlHdK7o1eUn7tZcpjUmcHQbjxsuWebEqU/yPto6iMtjrIGoSWGQcApLdTSo3nmMrgmGp573RJUeb+nZINcEopMovKZacytwEAXYiP4H1+XUt5FS0rAJAkCvOd1bMh3C7zncCz22oseUmxdOj2PqeBPoHv/yq5I9y+rM/YMVld4jpb2yrv8AMhON6zHiTC81LJ/8XYJf44TcdSEuci53RSFxcoLzvAoQ3fmz+pg/CiVGCbeyWm45BW6m4s++hQ+oUutzZ2JlSMNsiz1PXS22nwKKW8aowaWBGKujSqUqng5eUFJyf7/lkQQgipWejSKICYmBhs27atIvJCCCGkiqOxRgE899xz+OOPPzB27Fjs3LkTp06dwunTpwULIYSQGqoCL40ePHgQY8eORdOmTeHk5IQ6depgwIABVmNbA8Dp06fRs2dPODs7w93dHYMHDxY9+5GJ6KdGTYNqr1u3DuvXrxesM40BajDYd9+MEEJINVRBrbyvvvoKGRkZmDp1Kpo3b460tDQsX74cHTp0wN69e9G9e3cAwOXLlxEZGYknnngCP//8M9RqNebNm4euXbvi7Nmz8PHxKWNPQqIrwrVr14rdhBBCSA1RkR3qV6xYAV9fX0FYnz590LBhQyxZssRcEc6bNw9KpRI7d+40jyXdpk0bNGrUCB999JHoIThFV4SjR48WuwkhhJAaoiIflileCQKAs7Mzmjdvbp5FSK/XY+fOnRg1apRgQoXg4GBERUVh27ZtoitC0fcICSGE1GKPuftEdnY2Tp8+jRYtWgAAbty4gYKCAoSHh1vFDQ8Px/Xr16FWl9yFyBbRLcKxY8eWup7jOHz33Xdik31s8tsGQa8su/73vGh5fWOqFD0LX3MrfAHcMq/LaM73oVJMuAcs8BKk4dgxHVlZTlBe4vuYZTaXACIuE2Q2l8C1bQZwhL/efWV2AwQcNULvIP73y6Y1PWBUAhJtyXH8X72JK/sbwKNTGhBX8jRX9kqepAZOKnGvi8Tmfq9OVQCWbnTwuMhB51LyKfmgbUip+8tqysGoMgLMvj6iAJDRQgqDElAV61o25adXAJX1N9hpwh1ov68nCLt4uR4CS9nHg3b1za/vd1LDbxffv9GgLJaX5jI4pBXZZ4dWuPmME+rE6QDcEcS9MVUKtzjO6nxy/U+OjDA5dJeAu84uKNqTUtcpF8hSCeJfme4A/738r+r0VpZuUfkr68IZltmVH0zMgsNsKdIvecMNxWZdLgc9tr1R5j+j7KYGvBZxEFs/6llGTAuHjhkw7OG/l8XLu6hv1j2Nps9csztdW/LbBD3S9tXFw7YIc3KE/Z+VSiWUylI+lEKTJk1Cfn4+5s6dCwDIyOAnsfb0tO7X6enpCcYYsrKyEBAQYHceRVeEBw8etJoUNyMjA3l5eXB3d4e7u7vYJAkhhFQXDznWaL16wh+Q8+fPL3MM6XfffRc//vgjvvjiC7Rp00awrrTJ2UtbZ4voijA+Pt5m+MGDBzFx4kRs2bJFbJKEEEKqiYd9WOb27duCe3pltQYXLFiARYsWYfHixZg8ebI53MuLb+GbWoZFZWZmguM40Q2ycrtH2L17d0yePBlTp04tryQJIYRUNQ95j9DV1VWwlFYRLliwwDzr0Ntvvy1Y16BBAzg4OOD8+fNW250/fx4NGzaESqWyWleacn1Ypnnz5vjnn3/KM0lCCCFVCMeY6EWM9957D7GxsXjnnXcwf/58q/UymQwxMTHYunUrcnNzzeGJiYk4dOgQBg8eLPqYynUapri4OHh7e5dnkoQQQqqSCpyPcPny5Zg3bx769OmDp59+GsePHxes79ChAwC+xdiuXTv0798fs2fPNneo9/b2xsyZM0Vkjie6Ily4cKFVmEajwblz57B79268+eabojNBCCGkeqjIDvU7duwAAOzZswd79uyxWm+aArBp06Y4fPgw3nrrLQwdOhQymQzdu3fHRx99JHpUGeAhKkJbT/kolUqEhIRg4cKFVBESQkgNVpEd6g8fPmx33DZt2mD//v32J14K0RWh0SiieieEEFKzVOCl0cpS60aWyWokw/3G/GHfe64xAODBwBzcSfCG5zOWTss6Jw6Zo9rbTCN7VEcAQPMe15HfQDjRaXyM3Cp+UM8EAICmgfVoB0/1vIihEcLr4KkJnujU/QK09S3xm6+aAAC430iK7IbCPjLKeP7pK6d4vnO/qhffO7xDd35UgKLHBQBtelwyv741wAExkSfN79X1NSgu10Y/4cw+/CSuh+42Mr9PS7R0cM3tkyeI75JgeZ3XW7jORO8E6B2FYQlD+b83hjsis7Hwd9udYSFIGGy5DCJtmAuX+veFaUbz7+v1ThCE5wUBwb3j8aCBDroGBXAbYD0hbL9I6xHvASA3hC//MRFHAACcIz+hbnyicHgofbwzACCribzEYwYsZZnfQA/tQEv+s5o5mre3l94JyGnAYHAywuhke/D74p/V/Yb89yGngfA/VnZD4byj8YM8YXQ04tYA6/lIbz9bD+robOH2hd+TsgyPOFbiuvt9H2BNm3UAAP/61o/LP6xjt0NL3Hednolo0IMfNMM9Oslq/cHCc96UL5OsYucn65VVHlmtcmriNEwP9bCMTqfDhg0bcODAAWRkZMDb2xs9e/bECy+8ALnc/i8tIYSQaqYGtghFV4TZ2dno0aMHTp8+DScnJ/j7++Pvv//GTz/9hJUrV+LAgQOCTpOEEEJqEMbAGUXUbiK7T1QG0ZdG586diytXrmDz5s3Izc3FtWvXkJubi59//hlXrlwxjwdHCCGk5qmJl0ZFV4Tbt2/HwoUL8eyzzwrChw4ditjYWGzbtq3cMkcIIaSKecyzTzwOoi+NpqWl2Zz+AgBatWqF9PT0R84UIYSQqqki+xFWFtEtwjp16uB///ufzXV//fUXAgNLm5CGEEJIdWaqCMUsVZ3oFuHw4cOxZMkSuLi4YPTo0fDy8kJGRgZ++OEHLFmyBDNmzKiIfBJCCKkKGBP3AEw1eFjmoUaWOXPmDN544w28+eabkMlk0Ov1YIwhOjq6zPmlCCGEVF8VObJMZRF9aVSpVGLPnj3mcUVHjRqFN998E3v37sXu3buhUCjKTqQSuSQa4Xyb/2SkRfq3O1/nfxPkBxsh5wxwS9BDprb9CcoK+PD8mX5Q3bX0m0x70gESNV+kE3wOm8O5qXx3EtkdFQqChR3w//dPM5yY004Q5nxdijuzGkB61zKVyH/jv+LX3WHm/APA4ND/g9t1/r3HVb4DtfcUfh9JsywzoxeV/kaw+bVUzeH/5jxhfi+/W/LUKON9D1nyeIzv7J1zw9383vm61Lxe+bezYFvPS3yn8WHBp1D/tdtWaae1doVrgkFwbDnt6griuCYKr7HI1MLPUHfHCdl33aAMysWrfocBAMZjfP5uHA9GcdeOh0B5Rw7JHQdbh4sLc8Ix0Z0fjEAO64k+D7zTBRPdr0N2h/+cHK8L+9C6Xue3cU0wWI7ZxillKkvVXeHvUtd4LQDAJdF2x/iixgT+DTnHl5/jHQ5OiRI43JZDE6w1xzHt2vRZ5Yfw6boUlrnTndInM5WqAYc7UkGZ5wcbzeu4Y25QcZbz2/Q9KcuxuU8BAAzB/DmiLpJnnVqGiR9OAQAk3/MAAGS2tGzbPMi6w7tJ0e+gyWu+fJj+hrNg3wAwLoAfIEE6zQX6GfyAA2nHrGc5z7rhgawWRnO+pCH5APj/LUXVmZgNOVf2Z1ft0MMyFtHR0YiOji7PvBBCCKniOKO4foSi+hxWknKdhokQQkjNVhMvjdpVEdavb/sSmy0cx+HGjRsPnSFCCCFVWG0dYq158+bgOMv9A8YYdu3ahS5dusDNza3CMgfwD+csWLBAEObn54fk5OQK3S8hhBBrtbZFuHPnTsF7vV4PhUKBTz/9FK1bt66QjBXVokULwbxTUqm0lNiEEEIqjJHxi5j4VdxD3SMs2jp8HGQyGfz9/R/rPgkhhFjjmMiRZap+PVg95iO8du0aAgMDERoaiueeew43b96s7CwRQkjtZOpQL2ap4qr8U6NPPfUUNmzYgMaNGyMlJQWLFi1Cp06dcPHiRXh5edncRqPRQKOxTDCbk5PzuLJLCCE1Wk28R1jlW4R9+/bFkCFDEBYWhp49e+KPP/4AAKxfv77EbZYuXQo3NzfzUq9evVL3obvAP/ATfzEQTMp/agY5f/nX+7gczsdtd7hO7uRifn3nRF2r9R82+wVTnjyM5C4e0JzgO+hyUuuzIuUp68mMkzvw+8w9YZmB3f+vss8o7+NypHfjOwEnFaYRf1E4/mtSR+E08MlP8YMgnD/JPx2cecKvzP3YgzvBl2t4B+FTxLlRTQAAbidU8D4ux7VX+cveBjkHY7GicD0vh/dxOTzP279fmVT84IZ3LlpfejeVS1G3TgQBAK6NkSOlHf870ihnyHsh2ypuUaZjntD6iCDc44RwAIOCCx6C94F/6a3SKu2cNBaetwYF+LKUWM6Zd8J2wft4kQKWMHgcL3kABUH+C8/D4p8Ps3E+28P0fQCA5PZ8OUqkRiSfCBTkGQDut7F00v/mTFfBOoW09A7rHzT71Sos9x/Ld8q076KSu3ggubP1Q4A3xgj/XZryxUlsl0FmpPUgDjVCbe1Qf/r0acF7g4E/+S5fvmwzfkU+QOPk5ISwsDBcu3atxDhz5swRjHmak5NTZmVICCGkbJyBgRPRzOMMVb8mtKsibNu2rc0HZF588UXBe8YYOI4zV5QVQaPR4NKlS+jatWuJcZRKJZRK+37pEkIIsR/HGDgR9/3ExK0sdlWEa9eureh8lOiNN95ATEwMgoKCkJqaikWLFiEnJwejR4+utDwRQkitVVs71FdmpXPnzh2MGDEC6enp8PHxQYcOHXD8+HEEB9fQ6++EEFKV0TRMj9+mTZsqOwuEEEIK1cSnRqt8RUgIIaTqqLUPyxBCCCEAauSl0Srfj7Ay7GvpXHYkANrCboRfTx1itW7ymCmWeK5lpONqfaLoytjG5ER0kI30uFLTKB6uc7HvRH1z9ATB+8tjG9u1XVEHng6D2r3iT7uFL7z0yGloXRm6vP4aAGD0M69Yrde5GtF9ykS70qroY147tC9+btuoQtJWPVt6H8mi3oscCMlwbdkRi9DZOP8B4PspMQAAhavG5vqyTB0z2Sps9qhXhfsucu5/NuzZwrCyv7P25EvjxmFbRAM7clrN1NZ+hIQQQghQi7tPEEIIIQD42STE3PerqbNPEEIIqZ2oRUgIIaR2YxD5sEyF5aTcUEVICCHEfjXwqVGqCAkhhNiNMzBwIpp51I+QEEJIzUItQkIIIbVaDawIqUN9Mc0W3ETGK50qLv1FGVDcVFVY+rWV9qZlkmT/ZdYT6j6qG8Pcyz3N8pY0NtwqrNmSkjvDN1t4+5H32WzBTcH7+Bceft7PBkv5iW6bLb5v9zapn9Z/6P2Vl+D3S16X/HzLx5eRx8VUEYpZqjhqERJCCLGfEYD19LSlx6/iqCIkhBBiN85oBMfZX7txxqpfE1JFSAghxH5GkfMw0cgyhBBCapQa+LAMVYSEEEJEEPsADFWEhBBCahKDEWAi7vvRPUJCCCE1ChNZEYqJW0moIiSEEGK/GniPkDrUF3PjdduzfG/6oneJ29yNkAMANO78+wZbXkNKe6XNuFcm+QMA+myYJSpfzb6ZYDP8frcQUekUNXLt9DLjhG2aan6d3F44EMDdnu5W8X/9vEep6WV0DbQKqz/3X2g8rOMWeD3c6XntVRmSn3K0uW7oupmi0kptY8lDr+8tn1nzRamF66UAAN+PKn6QhK2f2S7buatGI6WTjQIEcHmiNwBgy2e9rNZdmxJq136z+jcrcd2NKdbfl/R+De1Kt7gr4/lBEa5M8gEA3I2wPTCCwUNvfp0/5n6J6dn6Dqa0dygxfnJHVyz6amSJ65stzkKzn/lZ75m7zpLv16w/+y9WDC4xnWrPyMQvVRy1CAkhhNivBrYIqSIkhBBiPyODqOFiqEVICCGkRjEaIa4ipIdlCCGE1CR0aZQQQkitRhUhIYSQ2owZDGDMYH98o/1xKwtVhIQQQuzHRHaJqAYtwlrVj3DRyhce2760bsDEteOxblXfEuM0WWe7b6AYGnfbH+Frn0955H08+d00wXudm33bfbBc2Bcrbbmlv5rG3Xois5SJ7UTnrSxa9/JJR+9m+9fszZfqAAB0heuvj7RvMuCNX9nuj/rSF1NthpvsWBJV6nqtHZ/NB98OLztSOVv7QUyJ68rj/C8tPa0bMGXdq8Iw15K3L16GXb99Q5Du9TF+j57JmoAm5iWEEFKrGY2AiPkIaYg1QgghNQozGMA4EfcIRdxPrCy16tIoIYSQR1TBl0bz8vIwbdo0BAYGQqVS4YknnsCmTZsq6GB41aYiXLlyJUJDQ6FSqdCmTRscPXq0srNECCG1TwWPNTp48GCsX78e8+fPx+7du9GuXTuMGDECGzdurKADqiaXRjdv3oxp06Zh5cqV6Ny5M77++mv07dsX//33H4KCgio7e4QQUnswkUOsiWgR7tq1C/v27cPGjRsxYsQIAEBUVBQSEhLw5ptvYvjw4ZBKpSIzXLZq0SL8+OOPMW7cOLz88sto1qwZPv30U9SrVw9fffVVZWeNEEJqFWZkohd7bdu2Dc7Oznj22WcF4S+99BLu3buHEydOlPfhAKgGFaFWq8WpU6fQu7fwsfPevXvj77//rqRcEUJI7cQMBtGLvS5cuIBmzZpBJhNerAwPDzevrwhV/tJoeno6DAYD/PyEfXj8/PyQnJxscxuNRgONRmN+n52dDQAwaNXQ64wwSDkYJHx/NoMGMKgBiQYwFhhhUEtg0AJ6nQF6GQeDVgKDDIAW0OsLt+c4GDT8bwim5y+BcwY+jlECGGGEUS0BNIXpywBmMK1jMKg5MBkACWCUGMEZODApA1PoYdDwlxwMaoDJ+O2hAQwcoNcZYeQ4GDjAIOFgkAAGLcBpGQwaDnqdAQatFAYZYJTyaUDK78OgkcDIAKYtTFvH551JACPHwBk5GA0MnJoD01v2aZTy+TX/ZNLw+zRIAIMRMKqNfByNFAYjvw4SwCDly4VJCstSr+XLTiMFpwVY4Xpo+fIxHZ9By8Gg4fPA9Hw6Ei3AMQa9jvGfnZYDtPxT2QbTvtQMzMBgNDIABhjURnCFp4BRzQAtB6OxsOw5ADI+b9AU3sYA/9lzhT9emRQwGhmMUiOglgBaDpyxcBttYboFRjApA6eWwCjlj828LykrPBaYPxdm5J8858uhyPlj5MvSIOPzYtACej2feYNOCgM487nIdAxGI2c+J2HkzxEmBTgNwGkLzzmuMD0Zvy+JKdxU3kbAqBaeP0wHGMCHm841FHb7NGg4/rMvPA+MHAPTFn6HCsvDIDHl33Q+Wj5PoPCck/KfF8B/H8AAo4SBcfznZ1BLzXk2qAFjgQYG6ACJEcYCDpzeAKaTgmN6GJgOEpkBxgI1DA/UMKrlMEr4755RWuQ7WHjsRjV//vPfRQZjgQGcWgqjXA+DxsD3CjCdDwbL94OPX5iuKa9KHQzQwaiXwFhggEFjMP9vMZUXZwQMejV/7NWgL5299EwjqkuEHvzcjTk5OYJwpVIJpVI4Z2RGRgbq169vlYanp6d5fUWo8hWhCccJO2IzxqzCTJYuXYoFCxZYhV9at7BC8va43KjsDJBa42ZlZ6CGyc3NhZubnSNSVFEKhQL+/v74X/Iu0ds6OzujXr16grD58+cjNjbWKm5J/9fLWvcoqnxF6O3tDalUatX6S01NtWolmsyZMwczZswwvzcajcjMzISXl1eFFSQhhBTHGENubi4CAwMrOyuPTKVS4datW9BqtaK3tdVwKd4aBAAvLy+brb7MzEwAlpZheavyFaFCoUCbNm2wb98+DBo0yBy+b98+DBgwwOY2tprc7u7uFZlNQgixqbq3BItSqVRQqVQVln5YWBh++ukn6PV6wX3C8+fPAwBatmxZIfut8g/LAMCMGTOwevVqrFmzBpcuXcL06dORmJiI8ePHV3bWCCGElJNBgwYhLy8Pv/76qyB8/fr1CAwMxFNPPVUh+63yLUIAGD58ODIyMrBw4UIkJSWhZcuW2LVrF4KDgys7a4QQQspJ37590atXL0yYMAE5OTlo2LAhfvrpJ+zZswc//PBDhfQhBACO1aTHmQghhFRreXl5mDt3Ln7++WdkZmaiadOmmDNnDp577rkK2ydVhIQQQmq1anGPkBBCCKkoVBESQgip1agiJIQQUqtRRUgIIaRWo4qQEEJIrUYVISGEkFqNKkJCCCG1GlWEhBBCajWqCAkhhNRqVBESQgip1agiJIQQUqtRRUgIIaRW+3/nB/VlLxrQEQAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# If batteries are built, their capacities are displayed in a geo-referenced plot\n", + "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\")].sum() > 0:\n", + " fig, ax = fn.plotLocationalColorMap(\n", + " esM,\n", + " \"Batteries\",\n", + " \"regions.shp\",\n", + " \"regionName\",\n", + " perArea=False,\n", + " zlabel=\"Capacity\\n[GWh]\\n\",\n", + " figsize=(4, 4),\n", + " )\n", + "else:\n", + " print(\"No batteries built.\")\n", + "\n", + "# If batteries are built in regionS, their storage inventory is displayed as heatmap\n", + "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\"), \"regionN\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Batteries\",\n", + " \"regionN\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"State of charge\\nin regionN\\n[GW]\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + " orientation=\"vertical\",\n", + " )\n", + "\n", + "# If batteries are built in regionS, their storage inventory is displayed as heatmap\n", + "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\"), \"regionS\"] > 0:\n", + " fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Batteries\",\n", + " \"regionS\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"State of charge\\nin regionS\\n[GW]\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + " orientation=\"vertical\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AC cables" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The built AC cable capacities are displayed\n", + "fig, ax = fn.plotLocations(\"regions.shp\", indexColumn=\"regionName\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM,\n", + " \"AC cables\",\n", + " \"lines.shp\",\n", + " loc0=\"loc0\",\n", + " loc1=\"loc1\",\n", + " fig=fig,\n", + " ax=ax,\n", + " cbHeight=0.4,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electricity demand" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The electricity demand time series in regionN is displayed\n", + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Electricity demand\",\n", + " \"regionN\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Demand\\nin regionN\\n[GW]\",\n", + " orientation=\"vertical\",\n", + ")\n", + "\n", + "# The electricity demand time series in regionS is displayed\n", + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Electricity demand\",\n", + " \"regionS\",\n", + " figsize=(5, 3),\n", + " xlabel=\"Day of the year\",\n", + " ylabel=\"Hour of the day\",\n", + " zlabel=\"Demand\\nin regionS\\n[GW]\",\n", + " orientation=\"vertical\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "431px", + "width": "510px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "399px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Tutorial/FINE_Tutorial_Part1.pdf b/examples/00_Tutorial/FINE_Tutorial_Part1.pdf similarity index 100% rename from examples/Tutorial/FINE_Tutorial_Part1.pdf rename to examples/00_Tutorial/FINE_Tutorial_Part1.pdf diff --git a/examples/Tutorial/run.bat b/examples/00_Tutorial/run.bat similarity index 100% rename from examples/Tutorial/run.bat rename to examples/00_Tutorial/run.bat diff --git a/examples/Tutorial/scenarioInput.xlsx b/examples/00_Tutorial/scenarioInput.xlsx similarity index 100% rename from examples/Tutorial/scenarioInput.xlsx rename to examples/00_Tutorial/scenarioInput.xlsx diff --git a/examples/1node_Energy_System_Workflow/1node_Example.ipynb b/examples/01_1node_Energy_System_Workflow/01_1node_Example.ipynb similarity index 94% rename from examples/1node_Energy_System_Workflow/1node_Example.ipynb rename to examples/01_1node_Energy_System_Workflow/01_1node_Example.ipynb index e419ca53..b7bf30ca 100644 --- a/examples/1node_Energy_System_Workflow/1node_Example.ipynb +++ b/examples/01_1node_Energy_System_Workflow/01_1node_Example.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workflow for a 1node energy system\n", + "# Workflow for a single node energy system\n", "\n", - "In this application of the FINE framework, a multi-regional energy system is modeled and optimized.\n", + "In this application of the ETHOS.FINE framework, a single region energy system is modeled and optimized.\n", "\n", "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", "\n", @@ -27,12 +27,12 @@ "source": [ "# 1. Import required packages and set input data path\n", "\n", - "The FINE framework is imported which provides the required classes and functions for modeling the energy system." + "The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "scrolled": true }, @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -147,13 +147,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [ "nbval-check-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2300.4069071646272" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data[\"Wind (onshore), operationRateMax\"].sum()" ] @@ -174,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -368,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -571,7 +582,7 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -585,7 +596,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/1node_Energy_System_Workflow/InputData/Locations.png b/examples/01_1node_Energy_System_Workflow/InputData/Locations.png similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/Locations.png rename to examples/01_1node_Energy_System_Workflow/InputData/Locations.png diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx similarity index 100% rename from examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx diff --git a/examples/1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx b/examples/01_1node_Energy_System_Workflow/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx similarity index 100% rename from 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examples/1node_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx rename to examples/01_1node_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx diff --git a/examples/1node_Energy_System_Workflow/getData.py b/examples/01_1node_Energy_System_Workflow/getData.py similarity index 100% rename from examples/1node_Energy_System_Workflow/getData.py rename to examples/01_1node_Energy_System_Workflow/getData.py diff --git a/examples/EnergyLand/Energyland.ipynb b/examples/02_EnergyLand/02_Energyland.ipynb similarity index 82% rename from examples/EnergyLand/Energyland.ipynb rename to examples/02_EnergyLand/02_Energyland.ipynb index ea13f473..c9f89ea2 100644 --- a/examples/EnergyLand/Energyland.ipynb +++ b/examples/02_EnergyLand/02_Energyland.ipynb @@ -6,7 +6,8 @@ "source": [ "# Workflow for the EnergyLand energy system\n", "\n", - "In this application of the FINE framework, a 1-node energy system is modeled and optimized.\n", + "In this application of the ETHOS.FINE framework, a single node energy system is modeled and optimized.\n", + "Compared to the previous examples, this example includes a lot more technologies considered in the system. \n", "\n", "The workflow is structures as follows:\n", "1. Required packages are imported and the input data path is set\n", @@ -21,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -31,7 +32,7 @@ "" ] }, - "execution_count": 17, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -55,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -128,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -176,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -207,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -289,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -362,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -418,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -508,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -532,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -561,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -667,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -695,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -730,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -758,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -786,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -828,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -857,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -886,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -918,7 +919,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -954,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -988,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1021,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1055,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1124,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1152,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1180,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1208,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1236,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1264,7 +1265,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1292,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1320,7 +1321,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1357,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1389,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1420,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1463,7 +1464,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1488,7 +1489,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1513,7 +1514,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1545,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1570,7 +1571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1597,7 +1598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1623,22 +1624,237 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Clustering time series data with 48 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(12.4891 sec)\n", + "\n" + ] + } + ], "source": [ "esM.aggregateTemporally(numberOfTypicalPeriods=48)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series aggregation specifications:\n", + "Number of typical periods:48, number of time steps per period:24, number of segments per period:12\n", + "\n", + "Declaring sets, variables and constraints for SourceSinkModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(1.2383 sec)\n", + "\n", + "Declaring sets, variables and constraints for ConversionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(1.5481 sec)\n", + "\n", + "Declaring sets, variables and constraints for StorageModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.7013 sec)\n", + "\n", + "Declaring shared potential constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring linked component quantity constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring commodity balances...\n", + "\t\t(0.3827 sec)\n", + "\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring objective function...\n", + "\t\t(1.6648 sec)\n", + "\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", + "Academic license - for non-commercial use only\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp8i62a3gw.pyomo.lp\n", + "Reading time = 0.15 seconds\n", + "x1: 40198 rows, 32770 columns, 122153 nonzeros\n", + "Changed value of parameter QCPDual to 1\n", + " Prev: 0 Min: 0 Max: 1 Default: 0\n", + "Changed value of parameter Threads to 3\n", + " Prev: 0 Min: 0 Max: 1024 Default: 0\n", + "Parameter logfile unchanged\n", + " Value: Default: \n", + "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", + "Optimize a model with 40198 rows, 32770 columns and 122153 nonzeros\n", + "Model fingerprint: 0x9abf5e1b\n", + "Coefficient statistics:\n", + " Matrix range [6e-07, 2e+01]\n", + " Objective range [2e-04, 2e+03]\n", + " Bounds range [4e-01, 8e+02]\n", + " RHS range [2e+05, 2e+05]\n", + "\n", + "Concurrent LP optimizer: dual simplex and barrier\n", + "Showing barrier log only...\n", + "\n", + "Presolve removed 10257 rows and 10174 columns\n", + "Presolve time: 0.06s\n", + "Presolved: 29941 rows, 22596 columns, 110126 nonzeros\n", + "\n", + "Ordering time: 0.21s\n", + "\n", + "Barrier statistics:\n", + " Dense cols : 30\n", + " Free vars : 417\n", + " AA' NZ : 2.534e+05\n", + " Factor NZ : 1.226e+06 (roughly 30 MBytes of memory)\n", + " Factor Ops : 2.085e+08 (less than 1 second per iteration)\n", + " Threads : 2\n", + "\n", + " Objective Residual\n", + "Iter Primal Dual Primal Dual Compl Time\n", + " 0 2.01108826e+07 -5.73317228e+05 3.05e+05 2.02e+01 1.89e+05 0s\n", + " 1 1.94818908e+07 -3.82935355e+07 2.30e+05 4.00e+01 8.44e+04 0s\n", + " 2 1.52530822e+07 -4.69963473e+07 8.29e+04 9.76e+00 2.89e+04 0s\n", + " 3 1.17121126e+07 -4.14418440e+07 2.84e+04 4.42e+00 1.15e+04 0s\n", + " 4 9.17974497e+06 -2.98202157e+07 1.31e+04 1.28e+00 4.83e+03 0s\n", + " 5 5.95458142e+06 -1.96594236e+07 4.05e+03 4.36e-01 1.71e+03 1s\n", + " 6 3.52972430e+06 -1.04622171e+07 1.23e+03 1.46e-01 6.06e+02 1s\n", + " 7 1.96878082e+06 -5.25221018e+06 3.65e+02 5.89e-02 2.31e+02 1s\n", + " 8 1.16299926e+06 -2.34288776e+06 9.87e+01 2.24e-02 9.17e+01 1s\n", + " 9 9.08000368e+05 -1.10350601e+06 4.80e+01 1.08e-02 4.87e+01 1s\n", + " 10 7.74570961e+05 -1.89209246e+05 2.77e+01 3.35e-03 2.21e+01 1s\n", + " 11 6.93286086e+05 7.43527328e+04 1.86e+01 1.19e-03 1.38e+01 1s\n", + " 12 6.08962725e+05 2.36750133e+05 1.03e+01 8.63e-04 8.06e+00 1s\n", + " 13 5.75308867e+05 2.76712745e+05 7.25e+00 4.44e-04 6.40e+00 1s\n", + " 14 5.44296989e+05 3.22363155e+05 5.56e+00 3.29e-04 4.71e+00 1s\n", + " 15 4.97943299e+05 3.45644464e+05 3.35e+00 2.72e-04 3.21e+00 1s\n", + " 16 4.76186007e+05 3.73649151e+05 2.28e+00 2.27e-04 2.15e+00 1s\n", + " 17 4.60800070e+05 3.84057303e+05 1.64e+00 2.14e-04 1.60e+00 1s\n", + " 18 4.47154442e+05 3.96768925e+05 1.06e+00 1.77e-04 1.05e+00 1s\n", + " 19 4.37355714e+05 4.02230136e+05 7.07e-01 1.47e-04 7.30e-01 1s\n", + " 20 4.33233522e+05 4.04657189e+05 5.51e-01 1.31e-04 5.93e-01 1s\n", + " 21 4.28158671e+05 4.08852604e+05 3.75e-01 9.72e-05 4.00e-01 1s\n", + " 22 4.24048914e+05 4.11216576e+05 2.31e-01 7.46e-05 2.66e-01 1s\n", + " 23 4.21446949e+05 4.13194747e+05 1.45e-01 5.77e-05 1.71e-01 1s\n", + " 24 4.19859392e+05 4.14481080e+05 9.54e-02 4.69e-05 1.11e-01 1s\n", + " 25 4.18785518e+05 4.14992645e+05 6.31e-02 4.04e-05 7.84e-02 1s\n", + " 26 4.18086801e+05 4.15685165e+05 4.22e-02 3.20e-05 4.96e-02 1s\n", + " 27 4.17637300e+05 4.16018342e+05 2.92e-02 3.01e-05 3.34e-02 1s\n", + " 28 4.17252235e+05 4.16189670e+05 1.82e-02 2.77e-05 2.19e-02 1s\n", + " 29 4.17112823e+05 4.16277118e+05 1.44e-02 2.45e-05 1.72e-02 1s\n", + " 30 4.16857650e+05 4.16366195e+05 7.50e-03 2.05e-05 1.01e-02 1s\n", + " 31 4.16667754e+05 4.16464449e+05 2.67e-03 1.50e-05 4.20e-03 1s\n", + " 32 4.16603458e+05 4.16511116e+05 1.17e-03 1.02e-05 1.90e-03 1s\n", + " 33 4.16581754e+05 4.16521252e+05 7.53e-04 8.31e-06 1.25e-03 1s\n", + " 34 4.16560754e+05 4.16532098e+05 3.30e-04 7.17e-06 5.91e-04 1s\n", + " 35 4.16551260e+05 4.16537236e+05 1.49e-04 6.29e-06 2.89e-04 1s\n", + " 36 4.16547534e+05 4.16540631e+05 8.21e-05 7.05e-06 1.42e-04 1s\n", + " 37 4.16546264e+05 4.16540986e+05 6.05e-05 6.29e-06 1.09e-04 1s\n", + " 38 4.16544744e+05 4.16541662e+05 3.58e-05 4.77e-06 6.35e-05 1s\n", + " 39 4.16544314e+05 4.16541963e+05 2.88e-05 3.30e-06 4.84e-05 1s\n", + " 40 4.16543577e+05 4.16542112e+05 1.73e-05 2.49e-06 3.02e-05 1s\n", + " 41 4.16543537e+05 4.16542133e+05 1.67e-05 2.37e-06 2.89e-05 1s\n", + " 42 4.16543253e+05 4.16542313e+05 1.24e-05 1.23e-06 1.93e-05 1s\n", + " 43 4.16542793e+05 4.16542379e+05 6.25e-06 6.61e-07 8.53e-06 1s\n", + " 44 4.16542705e+05 4.16542391e+05 4.66e-06 5.04e-07 6.45e-06 1s\n", + " 45 4.16542691e+05 4.16542411e+05 4.44e-06 2.95e-07 5.78e-06 1s\n", + " 46 4.16542691e+05 4.16542411e+05 1.55e-05 2.95e-07 5.77e-06 2s\n", + " 47 4.16542691e+05 4.16542411e+05 1.55e-05 2.95e-07 5.77e-06 2s\n", + " 48 4.16542691e+05 4.16542410e+05 1.55e-05 3.39e-06 5.77e-06 2s\n", + " 49 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.77e-06 2s\n", + " 50 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.77e-06 2s\n", + " 51 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.76e-06 2s\n", + " 52 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.76e-06 2s\n", + " 53 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.76e-06 2s\n", + " 54 4.16542691e+05 4.16542410e+05 1.55e-05 3.34e-06 5.76e-06 2s\n", + " 55 4.16542691e+05 4.16542410e+05 2.30e-05 3.34e-06 5.76e-06 2s\n", + "\n", + "Barrier performed 55 iterations in 1.85 seconds\n", + "Sub-optimal termination - objective 4.16542691e+05\n", + "\n", + "Crossover log...\n", + "\n", + " 0 DPushes remaining with DInf 0.0000000e+00 3s\n", + "\n", + " 16569 PPushes remaining with PInf 2.2963787e-03 3s\n", + " 3424 PPushes remaining with PInf 1.9025261e-01 5s\n", + " 0 PPushes remaining with PInf 4.2323792e-04 6s\n", + "\n", + " Push phase complete: Pinf 4.2323792e-04, Dinf 1.7842411e+04 6s\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 25050 4.1654244e+05 0.000000e+00 1.784241e+04 6s\n", + " 26050 4.1654242e+05 0.000000e+00 0.000000e+00 7s\n", + "\n", + "Solved with barrier\n", + "Solved in 26050 iterations and 6.62 seconds\n", + "Optimal objective 4.165424248e+05\n", + "\n", + "Status: ok\n", + "Return code: 0\n", + "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Termination condition: optimal\n", + "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Wall time: 6.621715545654297\n", + "Error rc: 0\n", + "Time: 7.929924964904785\n", + "\n", + "\n", + "Name: x1_copy\n", + "Lower bound: 416542.42479385773\n", + "Upper bound: 416542.42479385773\n", + "Number of objectives: 1\n", + "Number of constraints: 40198\n", + "Number of variables: 32770\n", + "Number of binary variables: 0\n", + "Number of integer variables: 0\n", + "Number of continuous variables: 32770\n", + "Number of nonzeros: 122153\n", + "Sense: minimize\n", + "\n", + "Solve time: 9.078521966934204 sec.\n", + "\n", + "Processing optimization output...\n", + "for SourceSinkModel ...(1.2538sec)\n", + "for ConversionModel ...(0.8390sec)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component LTHeatstorage\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for StorageModel ... 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EnergyLand
ComponentPropertyUnit
BioslurrySourceNPVcontribution[1e6 Euro]1388.317177
TAC[1e6 Euro/a]1388.317177
capacity[GW$_{bioslurry}$]2.9
commissioning[GW$_{bioslurry}$]2.9
commodCosts[1e6 Euro/a]1388.317177
............
nGasSourcecommodCosts[1e6 Euro/a]4488.14816
operation[GW$_{CH4}$*h/a]175318.287486
[GW$_{CH4}$*h]175318.287486
pT_demandoperation[Mio pkm/h*h/a]867000.0
[Mio pkm/h*h]867000.0
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EnergyLand
ComponentPropertyUnit
BEV_PCarNPVcontribution[1e6 Euro]220091.47299
TAC[1e6 Euro/a]220091.47299
capacity[Mio pkm/h]98.972603
capexCap[1e6 Euro/a]206111.988744
commissioning[Mio pkm/h]98.972603
............
oilBoilercommissioning[GW$_{LTHeat}$]21.75865
invest[1e6 Euro]7180.354406
operation[GW$_{LTHeat}$*h/a]81395.730459
[GW$_{LTHeat}$*h]81395.730459
opexCap[1e6 Euro/a]294.394531
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" + ], + "text/plain": [ + " EnergyLand\n", + "Component Property Unit \n", + "BEV_PCar NPVcontribution [1e6 Euro] 220091.47299\n", + " TAC [1e6 Euro/a] 220091.47299\n", + " capacity [Mio pkm/h] 98.972603\n", + " capexCap [1e6 Euro/a] 206111.988744\n", + " commissioning [Mio pkm/h] 98.972603\n", + "... ...\n", + "oilBoiler commissioning [GW$_{LTHeat}$] 21.75865\n", + " invest [1e6 Euro] 7180.354406\n", + " operation [GW$_{LTHeat}$*h/a] 81395.730459\n", + " [GW$_{LTHeat}$*h] 81395.730459\n", + " opexCap [1e6 Euro/a] 294.394531\n", + "\n", + "[110 rows x 1 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": { "scrolled": true, "tags": [ "nbval-skip" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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EnergyLand
ComponentPropertyUnit
LTHeatstorageNPVcontribution[1e6 Euro]4079.379892
TAC[1e6 Euro/a]4079.379892
capacity[GW$_{LTHeat}$*h]247.38751
capexCap[1e6 Euro/a]3703.953755
commissioning[GW$_{LTHeat}$*h]247.38751
invest[1e6 Euro]36365.963952
operationCharge[GW$_{LTHeat}$*h/a]117664.977694
[GW$_{LTHeat}$*h]117664.977694
operationDischarge[GW$_{LTHeat}$*h/a]105911.360512
[GW$_{LTHeat}$*h]105911.360512
opexCap[1e6 Euro/a]363.65964
opexCharge[1e6 Euro/a]11.766498
Li-ion batteriesNPVcontribution[1e6 Euro]4013.435236
TAC[1e6 Euro/a]4013.435236
capacity[GW$_{el}$*h]204.845145
capexCap[1e6 Euro/a]3663.356074
commissioning[GW$_{el}$*h]204.845145
invest[1e6 Euro]24581.417448
operationCharge[GW$_{el}$*h/a]59393.176059
[GW$_{el}$*h]59393.176059
operationDischarge[GW$_{el}$*h/a]55281.203076
[GW$_{el}$*h]55281.203076
opexCap[1e6 Euro/a]344.139844
opexCharge[1e6 Euro/a]5.939318
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" + ], + "text/plain": [ + " EnergyLand\n", + "Component Property Unit \n", + "LTHeatstorage NPVcontribution [1e6 Euro] 4079.379892\n", + " TAC [1e6 Euro/a] 4079.379892\n", + " capacity [GW$_{LTHeat}$*h] 247.38751\n", + " capexCap [1e6 Euro/a] 3703.953755\n", + " commissioning [GW$_{LTHeat}$*h] 247.38751\n", + " invest [1e6 Euro] 36365.963952\n", + " operationCharge [GW$_{LTHeat}$*h/a] 117664.977694\n", + " [GW$_{LTHeat}$*h] 117664.977694\n", + " operationDischarge [GW$_{LTHeat}$*h/a] 105911.360512\n", + " [GW$_{LTHeat}$*h] 105911.360512\n", + " opexCap [1e6 Euro/a] 363.65964\n", + " opexCharge [1e6 Euro/a] 11.766498\n", + "Li-ion batteries NPVcontribution [1e6 Euro] 4013.435236\n", + " TAC [1e6 Euro/a] 4013.435236\n", + " capacity [GW$_{el}$*h] 204.845145\n", + " capexCap [1e6 Euro/a] 3663.356074\n", + " commissioning [GW$_{el}$*h] 204.845145\n", + " invest [1e6 Euro] 24581.417448\n", + " operationCharge [GW$_{el}$*h/a] 59393.176059\n", + " [GW$_{el}$*h] 59393.176059\n", + " operationDischarge [GW$_{el}$*h/a] 55281.203076\n", + " [GW$_{el}$*h] 55281.203076\n", + " opexCap [1e6 Euro/a] 344.139844\n", + " opexCharge [1e6 Euro/a] 5.939318" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" ] @@ -1699,7 +2340,7 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1713,7 +2354,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/EnergyLand/Input_profiles_fine.xlsx b/examples/02_EnergyLand/Input_profiles_fine.xlsx similarity index 100% rename from examples/EnergyLand/Input_profiles_fine.xlsx rename to examples/02_EnergyLand/Input_profiles_fine.xlsx diff --git a/examples/EnergyLand/Output_profiles_fine.xlsx b/examples/02_EnergyLand/Output_profiles_fine.xlsx similarity index 100% rename from examples/EnergyLand/Output_profiles_fine.xlsx rename to examples/02_EnergyLand/Output_profiles_fine.xlsx diff --git a/examples/EnergyLand/Potentials.xlsx b/examples/02_EnergyLand/Potentials.xlsx similarity index 100% rename from examples/EnergyLand/Potentials.xlsx rename to examples/02_EnergyLand/Potentials.xlsx diff --git a/examples/EnergyLand/getData.py b/examples/02_EnergyLand/getData.py similarity index 100% rename from examples/EnergyLand/getData.py rename to examples/02_EnergyLand/getData.py diff --git a/examples/EnergyLand/images/strukturExample.png b/examples/02_EnergyLand/images/strukturExample.png similarity index 100% rename from examples/EnergyLand/images/strukturExample.png rename to examples/02_EnergyLand/images/strukturExample.png diff --git a/examples/EnergyLand/images/strukturExample.vsdx b/examples/02_EnergyLand/images/strukturExample.vsdx similarity index 100% rename from examples/EnergyLand/images/strukturExample.vsdx rename to examples/02_EnergyLand/images/strukturExample.vsdx diff --git a/examples/03_Multi-regional_Energy_System_Workflow/03_Multi-regional_Energy_System_Workflow.ipynb b/examples/03_Multi-regional_Energy_System_Workflow/03_Multi-regional_Energy_System_Workflow.ipynb new file mode 100644 index 00000000..938cac66 --- /dev/null +++ b/examples/03_Multi-regional_Energy_System_Workflow/03_Multi-regional_Energy_System_Workflow.ipynb @@ -0,0 +1,5227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workflow for a multi-regional energy system\n", + "\n", + "In this application of the ETHOS.FINE framework, a multi-regional energy system is modeled and optimized.\n", + "\n", + "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", + "\n", + "The workflow is structures as follows:\n", + "1. Required packages are imported and the input data path is set\n", + "2. An energy system model instance is created\n", + "3. Commodity sources are added to the energy system model\n", + "4. Commodity conversion components are added to the energy system model\n", + "5. Commodity storages are added to the energy system model\n", + "6. Commodity transmission components are added to the energy system model\n", + "7. Commodity sinks are added to the energy system model\n", + "8. The energy system model is optimized\n", + "9. Selected optimization results are presented\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Import required packages and set input data path\n", + "\n", + "The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import fine as fn\n", + "import matplotlib.pyplot as plt\n", + "from getData import getData\n", + "import pandas as pd\n", + "import os\n", + "\n", + "cwd = os.getcwd()\n", + "data = getData()\n", + "\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Create an energy system model instance \n", + "\n", + "The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", + "\n", + "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "locations = {\n", + " \"cluster_0\",\n", + " \"cluster_1\",\n", + " \"cluster_2\",\n", + " \"cluster_3\",\n", + " \"cluster_4\",\n", + " \"cluster_5\",\n", + " \"cluster_6\",\n", + " \"cluster_7\",\n", + "}\n", + "commodityUnitDict = {\n", + " \"electricity\": r\"GW$_{el}$\",\n", + " \"methane\": r\"GW$_{CH_{4},LHV}$\",\n", + " \"biogas\": r\"GW$_{biogas,LHV}$\",\n", + " \"CO2\": r\"Mio. t$_{CO_2}$/h\",\n", + " \"hydrogen\": r\"GW$_{H_{2},LHV}$\",\n", + "}\n", + "commodities = {\"electricity\", \"hydrogen\", \"methane\", \"biogas\", \"CO2\"}\n", + "numberOfTimeSteps = 8760\n", + "hoursPerTimeStep = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "esM = fn.EnergySystemModel(\n", + " locations=locations,\n", + " commodities=commodities,\n", + " numberOfTimeSteps=8760,\n", + " commodityUnitsDict=commodityUnitDict,\n", + " hoursPerTimeStep=1,\n", + " costUnit=\"1e9 Euro\",\n", + " lengthUnit=\"km\",\n", + " verboseLogLevel=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "CO2_reductionTarget = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Add commodity sources to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1. Electricity sources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind onshore" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Wind (onshore)\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " operationRateMax=data[\"Wind (onshore), operationRateMax\"],\n", + " capacityMax=data[\"Wind (onshore), capacityMax\"],\n", + " investPerCapacity=1.1,\n", + " opexPerCapacity=1.1 * 0.02,\n", + " interestRate=0.08,\n", + " economicLifetime=20,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Full load hours:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster_0 1572.003960\n", + "cluster_1 2350.292663\n", + "cluster_2 2374.507270\n", + "cluster_3 2186.572278\n", + "cluster_4 1572.650655\n", + "cluster_5 1767.840650\n", + "cluster_6 2719.564564\n", + "cluster_7 1553.045964\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"Wind (onshore), operationRateMax\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind offshore" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Wind (offshore)\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " operationRateMax=data[\"Wind (offshore), operationRateMax\"],\n", + " capacityMax=data[\"Wind (offshore), capacityMax\"],\n", + " investPerCapacity=2.3,\n", + " opexPerCapacity=2.3 * 0.02,\n", + " interestRate=0.08,\n", + " economicLifetime=20,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Full load hours:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster_0 0.000000\n", + "cluster_1 4435.420314\n", + "cluster_2 4301.655834\n", + "cluster_3 3902.391858\n", + "cluster_4 0.000000\n", + "cluster_5 0.000000\n", + "cluster_6 4609.508396\n", + "cluster_7 0.000000\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"Wind (offshore), operationRateMax\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PV" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"PV\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " operationRateMax=data[\"PV, operationRateMax\"],\n", + " capacityMax=data[\"PV, capacityMax\"],\n", + " investPerCapacity=0.65,\n", + " opexPerCapacity=0.65 * 0.02,\n", + " interestRate=0.08,\n", + " economicLifetime=25,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Full load hours:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cluster_0 1113.216464\n", + "cluster_1 1053.579422\n", + "cluster_2 1058.005181\n", + "cluster_3 1079.872237\n", + "cluster_4 1140.407380\n", + "cluster_5 1051.848141\n", + "cluster_6 1069.843344\n", + "cluster_7 1085.697466\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"PV, operationRateMax\"].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exisisting run-of-river hydroelectricity plants" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Existing run-of-river plants\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " operationRateFix=data[\"Existing run-of-river plants, operationRateFix\"],\n", + " tsaWeight=0.01,\n", + " capacityFix=data[\"Existing run-of-river plants, capacityFix\"],\n", + " investPerCapacity=0,\n", + " opexPerCapacity=0.208,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2. Methane (natural gas and biogas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Natural gas" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Natural gas purchase\",\n", + " commodity=\"methane\",\n", + " hasCapacityVariable=False,\n", + " commodityCost=0.0331 * 1e-3,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Biogas" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Biogas purchase\",\n", + " commodity=\"biogas\",\n", + " operationRateMax=data[\"Biogas, operationRateMax\"],\n", + " hasCapacityVariable=False,\n", + " commodityCost=0.05409 * 1e-3,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Add conversion components to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combined cycle gas turbine plants" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"CCGT plants (methane)\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " commodityConversionFactors={\n", + " \"electricity\": 1,\n", + " \"methane\": -1 / 0.6,\n", + " \"CO2\": 201 * 1e-6 / 0.6,\n", + " },\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=0.65,\n", + " opexPerCapacity=0.021,\n", + " interestRate=0.08,\n", + " economicLifetime=33,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### New combined cycle gas turbine plants for biogas" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"New CCGT plants (biogas)\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " commodityConversionFactors={\"electricity\": 1, \"biogas\": -1 / 0.63},\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=0.7,\n", + " opexPerCapacity=0.021,\n", + " interestRate=0.08,\n", + " economicLifetime=33,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### New combined cycly gas turbines for hydrogen" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"New CCGT plants (hydrogen)\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.63},\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=0.7,\n", + " opexPerCapacity=0.021,\n", + " interestRate=0.08,\n", + " economicLifetime=33,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electrolyzers" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"Electrolyzer\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.7},\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=0.5,\n", + " opexPerCapacity=0.5 * 0.025,\n", + " interestRate=0.08,\n", + " economicLifetime=10,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### rSOC" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "capexRSOC = 1.5\n", + "\n", + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"rSOEC\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " linkedConversionCapacityID=\"rSOC\",\n", + " commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.6},\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=capexRSOC / 2,\n", + " opexPerCapacity=capexRSOC * 0.02 / 2,\n", + " interestRate=0.08,\n", + " economicLifetime=10,\n", + " )\n", + ")\n", + "\n", + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"rSOFC\",\n", + " physicalUnit=r\"GW$_{el}$\",\n", + " linkedConversionCapacityID=\"rSOC\",\n", + " commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.6},\n", + " hasCapacityVariable=True,\n", + " investPerCapacity=capexRSOC / 2,\n", + " opexPerCapacity=capexRSOC * 0.02 / 2,\n", + " interestRate=0.08,\n", + " economicLifetime=10,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Add commodity storages to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.1. Electricity storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lithium ion batteries\n", + "\n", + "The self discharge of a lithium ion battery is here described as 3% per month. The self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Li-ion batteries\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " chargeEfficiency=0.95,\n", + " cyclicLifetime=10000,\n", + " dischargeEfficiency=0.95,\n", + " selfDischarge=1 - (1 - 0.03) ** (1 / (30 * 24)),\n", + " chargeRate=1,\n", + " dischargeRate=1,\n", + " doPreciseTsaModeling=False,\n", + " investPerCapacity=0.151,\n", + " opexPerCapacity=0.002,\n", + " interestRate=0.08,\n", + " economicLifetime=22,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.2. Hydrogen storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hydrogen filled salt caverns\n", + "The maximum capacity is here obtained by: dividing the given capacity (which is given for methane) by the lower heating value of methane and then multiplying it with the lower heating value of hydrogen." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Salt caverns (hydrogen)\",\n", + " commodity=\"hydrogen\",\n", + " hasCapacityVariable=True,\n", + " capacityVariableDomain=\"continuous\",\n", + " capacityPerPlantUnit=133,\n", + " chargeRate=1 / 470.37,\n", + " dischargeRate=1 / 470.37,\n", + " sharedPotentialID=\"Existing salt caverns\",\n", + " stateOfChargeMin=0.33,\n", + " stateOfChargeMax=1,\n", + " capacityMax=data[\"Salt caverns (hydrogen), capacityMax\"],\n", + " investPerCapacity=0.00011,\n", + " opexPerCapacity=0.00057,\n", + " interestRate=0.08,\n", + " economicLifetime=30,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.3. Methane storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Methane filled salt caverns" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Salt caverns (biogas)\",\n", + " commodity=\"biogas\",\n", + " hasCapacityVariable=True,\n", + " capacityVariableDomain=\"continuous\",\n", + " capacityPerPlantUnit=443,\n", + " chargeRate=1 / 470.37,\n", + " dischargeRate=1 / 470.37,\n", + " sharedPotentialID=\"Existing salt caverns\",\n", + " stateOfChargeMin=0.33,\n", + " stateOfChargeMax=1,\n", + " capacityMax=data[\"Salt caverns (methane), capacityMax\"],\n", + " investPerCapacity=0.00004,\n", + " opexPerCapacity=0.00001,\n", + " interestRate=0.08,\n", + " economicLifetime=30,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5.4 Pumped hydro storage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pumped hydro storage" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Pumped hydro storage\",\n", + " commodity=\"electricity\",\n", + " chargeEfficiency=0.88,\n", + " dischargeEfficiency=0.88,\n", + " hasCapacityVariable=True,\n", + " selfDischarge=1 - (1 - 0.00375) ** (1 / (30 * 24)),\n", + " chargeRate=0.16,\n", + " dischargeRate=0.12,\n", + " capacityFix=data[\"Pumped hydro storage, capacityFix\"],\n", + " investPerCapacity=0,\n", + " opexPerCapacity=0.000153,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. Add commodity transmission components to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.1. Electricity transmission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AC cables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "esM.add(fn.LinearOptimalPowerFlow(esM=esM, name='AC cables', commodity='electricity',\n", + " hasCapacityVariable=True, capacityFix=data['AC cables, capacityFix'],\n", + " reactances=data['AC cables, reactances']))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The distances of a component are set to a normalized value of 1.\n" + ] + } + ], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"AC cables\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " capacityFix=data[\"AC cables, capacityFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DC cables" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"DC cables\",\n", + " commodity=\"electricity\",\n", + " losses=data[\"DC cables, losses\"],\n", + " distances=data[\"DC cables, distances\"],\n", + " hasCapacityVariable=True,\n", + " capacityFix=data[\"DC cables, capacityFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.2 Methane transmission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Methane pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"Pipelines (biogas)\",\n", + " commodity=\"biogas\",\n", + " distances=data[\"Pipelines, distances\"],\n", + " hasCapacityVariable=True,\n", + " hasIsBuiltBinaryVariable=True,\n", + " bigM=300,\n", + " locationalEligibility=data[\"Pipelines, eligibility\"],\n", + " capacityMax=data[\"Pipelines, eligibility\"] * 15,\n", + " sharedPotentialID=\"pipelines\",\n", + " investPerCapacity=0.000037,\n", + " investIfBuilt=0.000314,\n", + " interestRate=0.08,\n", + " economicLifetime=40,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3 Hydrogen transmission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hydrogen pipelines" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"Pipelines (hydrogen)\",\n", + " commodity=\"hydrogen\",\n", + " distances=data[\"Pipelines, distances\"],\n", + " hasCapacityVariable=True,\n", + " hasIsBuiltBinaryVariable=True,\n", + " bigM=300,\n", + " locationalEligibility=data[\"Pipelines, eligibility\"],\n", + " capacityMax=data[\"Pipelines, eligibility\"] * 15,\n", + " sharedPotentialID=\"pipelines\",\n", + " investPerCapacity=0.000177,\n", + " investIfBuilt=0.00033,\n", + " interestRate=0.08,\n", + " economicLifetime=40,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Add commodity sinks to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.1. Electricity sinks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electricity demand" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"Electricity demand\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=False,\n", + " operationRateFix=data[\"Electricity demand, operationRateFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.2. Hydrogen sinks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fuel cell electric vehicle (FCEV) demand" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "FCEV_penetration = 0.5\n", + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"Hydrogen demand\",\n", + " commodity=\"hydrogen\",\n", + " hasCapacityVariable=False,\n", + " operationRateFix=data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.3. CO2 sinks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CO2 exiting the system's boundary" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"CO2 to enviroment\",\n", + " commodity=\"CO2\",\n", + " hasCapacityVariable=False,\n", + " commodityLimitID=\"CO2 limit\",\n", + " yearlyLimit=366 * (1 - CO2_reductionTarget),\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8 Temporal Aggregation" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [ + "nbval-ignore-output" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Clustering time series data with 7 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(15.3571 sec)\n", + "\n" + ] + } + ], + "source": [ + "esM.aggregateTemporally(numberOfTypicalPeriods=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series aggregation specifications:\n", + "Number of typical periods:7, number of time steps per period:24, number of segments per period:12\n", + "\n", + "Declaring sets, variables and constraints for SourceSinkModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.5831 sec)\n", + "\n", + "Declaring sets, variables and constraints for ConversionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.2009 sec)\n", + "\n", + "Declaring sets, variables and constraints for StorageModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(1.6147 sec)\n", + "\n", + "Declaring sets, variables and constraints for TransmissionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.5406 sec)\n", + "\n", + "Declaring shared potential constraint...\n", + "\t\t(0.0019 sec)\n", + "\n", + "Declaring linked component quantity constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring commodity balances...\n", + "\t\t(0.2854 sec)\n", + "\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring objective function...\n", + "\t\t(1.8950 sec)\n", + "\n", + "Either solver not selected or specified solver not available.gurobi is set as solver.\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", + "Academic license - for non-commercial use only\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp05rx5ww1.pyomo.lp\n", + "Reading time = 0.17 seconds\n", + "x1: 64257 rows, 33215 columns, 188107 nonzeros\n", + "Changed value of parameter QCPDual to 1\n", + " Prev: 0 Min: 0 Max: 1 Default: 0\n", + "Changed value of parameter Threads to 3\n", + " Prev: 0 Min: 0 Max: 1024 Default: 0\n", + "Parameter logfile unchanged\n", + " Value: Default: \n", + "Changed value of parameter OptimalityTol to 0.001\n", + " Prev: 1e-06 Min: 1e-09 Max: 0.01 Default: 1e-06\n", + "Changed value of parameter method to 2\n", + " Prev: -1 Min: -1 Max: 5 Default: -1\n", + "Changed value of parameter cuts to 0\n", + " Prev: -1 Min: -1 Max: 3 Default: -1\n", + "Changed value of parameter MIPGap to 0.005\n", + " Prev: 0.0001 Min: 0.0 Max: inf Default: 0.0001\n", + "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", + "Optimize a model with 64257 rows, 33215 columns and 188107 nonzeros\n", + "Model fingerprint: 0x76b4a38f\n", + "Variable types: 33163 continuous, 52 integer (52 binary)\n", + "Coefficient statistics:\n", + " Matrix range [2e-08, 5e+02]\n", + " Objective range [1e-05, 3e-01]\n", + " Bounds range [4e-02, 1e+05]\n", + " RHS range [4e-02, 3e+01]\n", + "Presolve removed 23828 rows and 11190 columns\n", + "Presolve time: 0.19s\n", + "Presolved: 40429 rows, 22025 columns, 139070 nonzeros\n", + "Variable types: 21999 continuous, 26 integer (26 binary)\n", + "Root barrier log...\n", + "\n", + "Ordering time: 0.43s\n", + "\n", + "Barrier statistics:\n", + " Dense cols : 126\n", + " Free vars : 616\n", + " AA' NZ : 1.127e+06\n", + " Factor NZ : 4.436e+06 (roughly 60 MBytes of memory)\n", + " Factor Ops : 1.017e+09 (less than 1 second per iteration)\n", + " Threads : 3\n", + "\n", + " Objective Residual\n", + "Iter Primal Dual Primal Dual Compl Time\n", + " 0 1.70245333e+05 -1.98438218e+06 2.38e+05 4.70e-03 2.36e+03 1s\n", + " 1 1.61017694e+05 -1.76741064e+06 1.67e+05 1.75e-01 1.55e+03 1s\n", + " 2 9.08350009e+04 -1.59403953e+06 8.21e+04 9.49e-02 8.02e+02 1s\n", + " 3 6.43834737e+04 -9.56256637e+05 4.24e+04 1.55e-02 3.93e+02 1s\n", + " 4 2.66104383e+04 -6.14576300e+05 5.87e+02 3.17e-03 4.29e+01 1s\n", + " 5 1.64055310e+04 -1.72901116e+05 9.22e+01 1.04e-03 9.60e+00 1s\n", + " 6 8.47701014e+03 -1.35369052e+05 2.79e+01 1.01e-03 4.36e+00 1s\n", + " 7 3.91895765e+03 -3.90425100e+04 7.24e+00 2.64e-04 1.06e+00 1s\n", + " 8 1.91216213e+03 -1.38197716e+04 2.13e+00 2.81e-04 3.09e-01 1s\n", + " 9 6.89612377e+02 -3.80956453e+03 2.23e-01 1.54e-04 6.87e-02 1s\n", + " 10 3.47510776e+02 -9.06889051e+02 6.72e-02 1.80e-04 1.82e-02 1s\n", + " 11 2.26324283e+02 -6.81756851e+02 3.30e-02 1.49e-04 1.30e-02 1s\n", + " 12 1.95482279e+02 -3.63824814e+02 2.54e-02 9.65e-05 7.99e-03 1s\n", + " 13 1.37030202e+02 -9.77947048e+01 1.40e-02 5.53e-05 3.33e-03 1s\n", + " 14 8.52540823e+01 -4.51098467e+00 4.65e-03 2.70e-05 1.27e-03 1s\n", + " 15 7.46233798e+01 1.46989530e+01 3.05e-03 1.83e-05 8.47e-04 2s\n", + " 16 7.02640625e+01 2.16082012e+01 2.43e-03 1.54e-05 6.88e-04 2s\n", + " 17 6.33379296e+01 3.48832479e+01 1.38e-03 9.61e-06 4.02e-04 2s\n", + " 18 5.90463355e+01 4.26342378e+01 8.00e-04 5.72e-06 2.32e-04 2s\n", + " 19 5.66958331e+01 4.71445965e+01 4.47e-04 3.33e-06 1.35e-04 2s\n", + " 20 5.56290248e+01 4.84708832e+01 2.97e-04 2.66e-06 1.01e-04 2s\n", + " 21 5.45583784e+01 4.98489026e+01 1.53e-04 2.10e-06 6.64e-05 2s\n", + " 22 5.40738926e+01 5.08784005e+01 9.44e-05 1.46e-06 4.50e-05 2s\n", + " 23 5.36687124e+01 5.22741940e+01 4.62e-05 6.03e-07 1.96e-05 2s\n", + " 24 5.34398890e+01 5.26819369e+01 2.53e-05 3.39e-07 1.07e-05 2s\n", + " 25 5.33723780e+01 5.28701377e+01 1.83e-05 2.19e-07 7.08e-06 2s\n", + " 26 5.33292228e+01 5.29645904e+01 1.41e-05 1.57e-07 5.14e-06 2s\n", + " 27 5.32785020e+01 5.30407197e+01 8.74e-06 1.04e-07 3.35e-06 2s\n", + " 28 5.32544517e+01 5.30979617e+01 6.14e-06 7.69e-08 2.21e-06 2s\n", + " 29 5.32383010e+01 5.31183715e+01 4.50e-06 7.42e-08 1.69e-06 2s\n", + " 30 5.32370238e+01 5.31262700e+01 4.35e-06 6.77e-08 1.56e-06 2s\n", + " 31 5.32240997e+01 5.31397655e+01 2.97e-06 5.81e-08 1.19e-06 2s\n", + " 32 5.32177106e+01 5.31556168e+01 2.33e-06 4.37e-08 8.75e-07 2s\n", + " 33 5.32119670e+01 5.31694350e+01 1.71e-06 3.03e-08 6.00e-07 2s\n", + " 34 5.32071138e+01 5.31828772e+01 1.18e-06 1.49e-08 3.42e-07 2s\n", + " 35 5.32025777e+01 5.31856061e+01 7.08e-07 1.20e-08 2.39e-07 2s\n", + " 36 5.32009752e+01 5.31905974e+01 5.46e-07 5.82e-09 1.46e-07 2s\n", + " 37 5.31977818e+01 5.31934582e+01 2.23e-07 2.78e-09 6.10e-08 2s\n", + " 38 5.31957855e+01 5.31948098e+01 2.70e-08 1.14e-09 1.38e-08 2s\n", + " 39 5.31955553e+01 5.31952520e+01 1.66e-08 4.79e-10 4.28e-09 3s\n", + " 40 5.31955405e+01 5.31952915e+01 1.06e-08 4.08e-10 3.51e-09 3s\n", + " 41 5.31955324e+01 5.31954236e+01 8.10e-09 1.69e-10 1.53e-09 3s\n", + " 42 5.31955269e+01 5.31954780e+01 3.85e-09 7.37e-11 6.90e-10 3s\n", + " 43 5.31955235e+01 5.31954961e+01 4.74e-08 3.26e-10 3.99e-10 3s\n", + " 44 5.31955220e+01 5.31955084e+01 3.06e-08 4.76e-08 2.01e-10 3s\n", + " 45 5.31955209e+01 5.31955176e+01 1.59e-07 3.21e-07 5.25e-11 3s\n", + " 46 5.31955209e+01 5.31955176e+01 5.72e-07 3.29e-07 5.19e-11 3s\n", + " 47 5.31955207e+01 5.31955190e+01 4.24e-07 6.85e-08 2.95e-11 3s\n", + " 48 5.31955204e+01 5.31955194e+01 2.13e-07 7.39e-08 1.82e-11 3s\n", + " 49 5.31955202e+01 5.31955199e+01 1.23e-07 3.53e-08 9.46e-12 3s\n", + "\n", + "Barrier solved model in 49 iterations and 3.06 seconds\n", + "Optimal objective 5.31955202e+01\n", + "\n", + "\n", + "Restart crossover...\n", + "\n", + "Extra one simplex iteration after uncrush\n", + "\n", + "Root relaxation: objective 5.319554e+01, 12367 iterations, 4.75 seconds\n", + "Total elapsed time = 5.09s\n", + "\n", + " Nodes | Current Node | Objective Bounds | Work\n", + " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", + "\n", + " 0 0 53.19554 0 12 - 53.19554 - - 5s\n", + "H 0 0 53.3089815 53.19554 0.21% - 7s\n", + "\n", + "Explored 1 nodes (12367 simplex iterations) in 7.33 seconds\n", + "Thread count was 3 (of 8 available processors)\n", + "\n", + "Solution count 1: 53.309 \n", + "\n", + "Optimal solution found (tolerance 5.00e-03)\n", + "Best objective 5.330898148655e+01, best bound 5.319554378114e+01, gap 0.2128%\n", + "\n", + "Status: ok\n", + "Return code: 0\n", + "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Termination condition: optimal\n", + "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Wall time: 7.332256317138672\n", + "Error rc: 0\n", + "Time: 8.129884481430054\n", + "\n", + "\n", + "Name: x1\n", + "Lower bound: 53.195543781136635\n", + "Upper bound: 53.30898148655321\n", + "Number of objectives: 1\n", + "Number of constraints: 64257\n", + "Number of variables: 33215\n", + "Number of binary variables: 52\n", + "Number of integer variables: 52\n", + "Number of continuous variables: 33163\n", + "Number of nonzeros: 188107\n", + "Sense: minimize\n", + "\n", + "Solve time: 10.693563461303711 sec.\n", + "\n", + "Processing optimization output...\n", + "for SourceSinkModel ... (1.7002sec)\n", + "for ConversionModel ... (0.6534sec)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Li-ion batteries\n", + " warnings.warn(\n", + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Pumped hydro storage\n", + " warnings.warn(\n", + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Salt caverns (biogas)\n", + " warnings.warn(\n", + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for StorageModel ... (1.5021sec)\n", + "for TransmissionModel ... (1.5339sec)\n", + "\t\t(5.4766 sec)\n", + "\n" + ] + } + ], + "source": [ + "# The `optimizationSpecs` only work with the Gurobi solver. If you are using another solver you need to choose\n", + "# specs spcecific to this solver or no specs.\n", + "esM.optimize(\n", + " timeSeriesAggregation=True,\n", + " optimizationSpecs=\"OptimalityTol=1e-3 method=2 cuts=0 MIPGap=5e-3\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 9. Selected results output\n", + "\n", + "Plot locations (GeoPandas required)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "# Import the geopandas package for plotting the locations\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [], + "source": [ + "locFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"clusteredRegions.shp\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotLocations(locFilePath, plotLocNames=True, indexColumn=\"index\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sources and Sink\n", + "\n", + "Show optimization summary" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
ComponentPropertyUnit
Biogas purchaseNPVcontribution[1e9 Euro]0.2642230.2537510.1767490.2441620.3514530.1555940.1648730.065985
TAC[1e9 Euro/a]0.2642230.2537510.1767490.2441620.3514530.1555940.1648730.065985
commodCosts[1e9 Euro/a]0.2642230.2537510.1767490.2441620.3514530.1555940.1648730.065985
operation[GW$_{biogas,LHV}$*h/a]4884.8793014691.2815273267.6747584513.999496497.5511122876.5800983048.125821219.907894
[GW$_{biogas,LHV}$*h]4884.8793014691.2815273267.6747584513.999496497.5511122876.5800983048.125821219.907894
Electricity demandoperation[GW$_{el}$*h/a]133963.45143366115.987455124796.75946840441.20326677229.28116541413.40915528088.76622315851.141245
[GW$_{el}$*h]133963.45143366115.987455124796.75946840441.20326677229.28116541413.40915528088.76622315851.141245
Existing run-of-river plantsNPVcontribution[1e9 Euro]0.1443160.0077910.007950.0090630.556096000.067472
TAC[1e9 Euro/a]0.1443160.0077910.007950.0090630.556096000.067472
capacity[GW$_{el}$]0.6938280.0374560.0382210.0435722.673537NaNNaN0.324386
commissioning[GW$_{el}$]0.6938280.0374560.0382210.0435722.673537NaNNaN0.324386
operation[GW$_{el}$*h/a]3167.329183170.988002174.477553198.9044112204.710271NaNNaN1480.823705
[GW$_{el}$*h]3167.329183170.988002174.477553198.9044112204.710271NaNNaN1480.823705
opexCap[1e9 Euro/a]0.1443160.0077910.007950.0090630.556096NaNNaN0.067472
Hydrogen demandoperation[GW$_{H_{2},LHV}$*h/a]15855.22909111007.05688216452.5949445264.13186410078.2675255747.4235723710.0505141453.124166
[GW$_{H_{2},LHV}$*h]15855.22909111007.05688216452.5949445264.13186410078.2675255747.4235723710.0505141453.124166
PVNPVcontribution[1e9 Euro]3.2005541.9099672.4910891.4497022.1940131.4697670.9337030.366282
TAC[1e9 Euro/a]3.2005541.9099672.4910891.4497022.1940131.4697670.9337030.366282
capacity[GW$_{el}$]43.31441425.84836533.71292519.61940829.69247519.89095412.6361884.957044
capexCap[1e9 Euro/a]2.6374671.5739382.0528211.1946491.8080111.2111840.7694330.30184
commissioning[GW$_{el}$]43.31441425.84836533.71292519.61940829.69247519.89095412.6361884.957044
invest[1e9 Euro]28.15436916.80143721.91340112.75261519.30010912.929128.2135223.222079
operation[GW$_{el}$*h/a]43273.49080326000.81251933120.40115720388.46919630221.74816719357.8270213518.7416164756.911042
[GW$_{el}$*h]43273.49080326000.81251933120.40115720388.46919630221.74816719357.8270213518.7416164756.911042
opexCap[1e9 Euro/a]0.5630870.3360290.4382680.2550520.3860020.2585820.164270.064442
Wind (offshore)NPVcontribution[1e9 Euro]01.1210414.8537841.68156005.0446810
TAC[1e9 Euro/a]01.1210414.8537841.68156005.0446810
capacity[GW$_{el}$]NaN4.053.06.0NaNNaN18.0NaN
capexCap[1e9 Euro/a]NaN0.9370412.4157841.40556NaNNaN4.216681NaN
commissioning[GW$_{el}$]NaN4.053.06.0NaNNaN18.0NaN
invest[1e9 Euro]NaN9.2121.913.8NaNNaN41.4NaN
operation[GW$_{el}$*h/a]NaN14910.149667223985.93649421541.298959NaNNaN82532.968552NaN
[GW$_{el}$*h]NaN14910.149667223985.93649421541.298959NaNNaN82532.968552NaN
opexCap[1e9 Euro/a]NaN0.1842.4380.276NaNNaN0.828NaN
Wind (onshore)NPVcontribution[1e9 Euro]0.00.01.8484090.00.1115870.02.8969020.0
TAC[1e9 Euro/a]0.00.01.8484090.00.1115870.02.8969020.0
capacity[GW$_{el}$]0.00.013.7902480.00.8325090.021.6126370.0
capexCap[1e9 Euro/a]0.00.01.5450240.00.0932720.02.4214240.0
commissioning[GW$_{el}$]0.00.013.7902480.00.8325090.021.6126370.0
invest[1e9 Euro]0.00.015.1692730.00.915760.023.7739010.0
operation[GW$_{el}$*h/a]0.00.023175.0839510.01132.3335270.042841.3254570.0
[GW$_{el}$*h]0.00.023175.0839510.01132.3335270.042841.3254570.0
opexCap[1e9 Euro/a]0.00.00.3033850.00.0183150.00.4754780.0
\n", + "
" + ], + "text/plain": [ + " cluster_0 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.264223 \n", + " TAC [1e9 Euro/a] 0.264223 \n", + " commodCosts [1e9 Euro/a] 0.264223 \n", + " operation [GW$_{biogas,LHV}$*h/a] 4884.879301 \n", + " [GW$_{biogas,LHV}$*h] 4884.879301 \n", + "Electricity demand operation [GW$_{el}$*h/a] 133963.451433 \n", + " [GW$_{el}$*h] 133963.451433 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.144316 \n", + " TAC [1e9 Euro/a] 0.144316 \n", + " capacity [GW$_{el}$] 0.693828 \n", + " commissioning [GW$_{el}$] 0.693828 \n", + " operation [GW$_{el}$*h/a] 3167.329183 \n", + " [GW$_{el}$*h] 3167.329183 \n", + " opexCap [1e9 Euro/a] 0.144316 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 15855.229091 \n", + " [GW$_{H_{2},LHV}$*h] 15855.229091 \n", + "PV NPVcontribution [1e9 Euro] 3.200554 \n", + " TAC [1e9 Euro/a] 3.200554 \n", + " capacity [GW$_{el}$] 43.314414 \n", + " capexCap [1e9 Euro/a] 2.637467 \n", + " commissioning [GW$_{el}$] 43.314414 \n", + " invest [1e9 Euro] 28.154369 \n", + " operation [GW$_{el}$*h/a] 43273.490803 \n", + " [GW$_{el}$*h] 43273.490803 \n", + " opexCap [1e9 Euro/a] 0.563087 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " invest [1e9 Euro] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "\n", + " cluster_1 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.253751 \n", + " TAC [1e9 Euro/a] 0.253751 \n", + " commodCosts [1e9 Euro/a] 0.253751 \n", + " operation [GW$_{biogas,LHV}$*h/a] 4691.281527 \n", + " [GW$_{biogas,LHV}$*h] 4691.281527 \n", + "Electricity demand operation [GW$_{el}$*h/a] 66115.987455 \n", + " [GW$_{el}$*h] 66115.987455 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.007791 \n", + " TAC [1e9 Euro/a] 0.007791 \n", + " capacity [GW$_{el}$] 0.037456 \n", + " commissioning [GW$_{el}$] 0.037456 \n", + " operation [GW$_{el}$*h/a] 170.988002 \n", + " [GW$_{el}$*h] 170.988002 \n", + " opexCap [1e9 Euro/a] 0.007791 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 11007.056882 \n", + " [GW$_{H_{2},LHV}$*h] 11007.056882 \n", + "PV NPVcontribution [1e9 Euro] 1.909967 \n", + " TAC [1e9 Euro/a] 1.909967 \n", + " capacity [GW$_{el}$] 25.848365 \n", + " capexCap [1e9 Euro/a] 1.573938 \n", + " commissioning [GW$_{el}$] 25.848365 \n", + " invest [1e9 Euro] 16.801437 \n", + " operation [GW$_{el}$*h/a] 26000.812519 \n", + " [GW$_{el}$*h] 26000.812519 \n", + " opexCap [1e9 Euro/a] 0.336029 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 1.12104 \n", + " TAC [1e9 Euro/a] 1.12104 \n", + " capacity [GW$_{el}$] 4.0 \n", + " capexCap [1e9 Euro/a] 0.93704 \n", + " commissioning [GW$_{el}$] 4.0 \n", + " invest [1e9 Euro] 9.2 \n", + " operation [GW$_{el}$*h/a] 14910.149667 \n", + " [GW$_{el}$*h] 14910.149667 \n", + " opexCap [1e9 Euro/a] 0.184 \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "\n", + " cluster_2 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.176749 \n", + " TAC [1e9 Euro/a] 0.176749 \n", + " commodCosts [1e9 Euro/a] 0.176749 \n", + " operation [GW$_{biogas,LHV}$*h/a] 3267.674758 \n", + " [GW$_{biogas,LHV}$*h] 3267.674758 \n", + "Electricity demand operation [GW$_{el}$*h/a] 124796.759468 \n", + " [GW$_{el}$*h] 124796.759468 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.00795 \n", + " TAC [1e9 Euro/a] 0.00795 \n", + " capacity [GW$_{el}$] 0.038221 \n", + " commissioning [GW$_{el}$] 0.038221 \n", + " operation [GW$_{el}$*h/a] 174.477553 \n", + " [GW$_{el}$*h] 174.477553 \n", + " opexCap [1e9 Euro/a] 0.00795 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 16452.594944 \n", + " [GW$_{H_{2},LHV}$*h] 16452.594944 \n", + "PV NPVcontribution [1e9 Euro] 2.491089 \n", + " TAC [1e9 Euro/a] 2.491089 \n", + " capacity [GW$_{el}$] 33.712925 \n", + " capexCap [1e9 Euro/a] 2.052821 \n", + " commissioning [GW$_{el}$] 33.712925 \n", + " invest [1e9 Euro] 21.913401 \n", + " operation [GW$_{el}$*h/a] 33120.401157 \n", + " [GW$_{el}$*h] 33120.401157 \n", + " opexCap [1e9 Euro/a] 0.438268 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 14.853784 \n", + " TAC [1e9 Euro/a] 14.853784 \n", + " capacity [GW$_{el}$] 53.0 \n", + " capexCap [1e9 Euro/a] 12.415784 \n", + " commissioning [GW$_{el}$] 53.0 \n", + " invest [1e9 Euro] 121.9 \n", + " operation [GW$_{el}$*h/a] 223985.936494 \n", + " [GW$_{el}$*h] 223985.936494 \n", + " opexCap [1e9 Euro/a] 2.438 \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 1.848409 \n", + " TAC [1e9 Euro/a] 1.848409 \n", + " capacity [GW$_{el}$] 13.790248 \n", + " capexCap [1e9 Euro/a] 1.545024 \n", + " commissioning [GW$_{el}$] 13.790248 \n", + " invest [1e9 Euro] 15.169273 \n", + " operation [GW$_{el}$*h/a] 23175.083951 \n", + " [GW$_{el}$*h] 23175.083951 \n", + " opexCap [1e9 Euro/a] 0.303385 \n", + "\n", + " cluster_3 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.244162 \n", + " TAC [1e9 Euro/a] 0.244162 \n", + " commodCosts [1e9 Euro/a] 0.244162 \n", + " operation [GW$_{biogas,LHV}$*h/a] 4513.99949 \n", + " [GW$_{biogas,LHV}$*h] 4513.99949 \n", + "Electricity demand operation [GW$_{el}$*h/a] 40441.203266 \n", + " [GW$_{el}$*h] 40441.203266 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.009063 \n", + " TAC [1e9 Euro/a] 0.009063 \n", + " capacity [GW$_{el}$] 0.043572 \n", + " commissioning [GW$_{el}$] 0.043572 \n", + " operation [GW$_{el}$*h/a] 198.90441 \n", + " [GW$_{el}$*h] 198.90441 \n", + " opexCap [1e9 Euro/a] 0.009063 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 5264.131864 \n", + " [GW$_{H_{2},LHV}$*h] 5264.131864 \n", + "PV NPVcontribution [1e9 Euro] 1.449702 \n", + " TAC [1e9 Euro/a] 1.449702 \n", + " capacity [GW$_{el}$] 19.619408 \n", + " capexCap [1e9 Euro/a] 1.194649 \n", + " commissioning [GW$_{el}$] 19.619408 \n", + " invest [1e9 Euro] 12.752615 \n", + " operation [GW$_{el}$*h/a] 20388.469196 \n", + " [GW$_{el}$*h] 20388.469196 \n", + " opexCap [1e9 Euro/a] 0.255052 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 1.68156 \n", + " TAC [1e9 Euro/a] 1.68156 \n", + " capacity [GW$_{el}$] 6.0 \n", + " capexCap [1e9 Euro/a] 1.40556 \n", + " commissioning [GW$_{el}$] 6.0 \n", + " invest [1e9 Euro] 13.8 \n", + " operation [GW$_{el}$*h/a] 21541.298959 \n", + " [GW$_{el}$*h] 21541.298959 \n", + " opexCap [1e9 Euro/a] 0.276 \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "\n", + " cluster_4 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.351453 \n", + " TAC [1e9 Euro/a] 0.351453 \n", + " commodCosts [1e9 Euro/a] 0.351453 \n", + " operation [GW$_{biogas,LHV}$*h/a] 6497.551112 \n", + " [GW$_{biogas,LHV}$*h] 6497.551112 \n", + "Electricity demand operation [GW$_{el}$*h/a] 77229.281165 \n", + " [GW$_{el}$*h] 77229.281165 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.556096 \n", + " TAC [1e9 Euro/a] 0.556096 \n", + " capacity [GW$_{el}$] 2.673537 \n", + " commissioning [GW$_{el}$] 2.673537 \n", + " operation [GW$_{el}$*h/a] 12204.710271 \n", + " [GW$_{el}$*h] 12204.710271 \n", + " opexCap [1e9 Euro/a] 0.556096 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 10078.267525 \n", + " [GW$_{H_{2},LHV}$*h] 10078.267525 \n", + "PV NPVcontribution [1e9 Euro] 2.194013 \n", + " TAC [1e9 Euro/a] 2.194013 \n", + " capacity [GW$_{el}$] 29.692475 \n", + " capexCap [1e9 Euro/a] 1.808011 \n", + " commissioning [GW$_{el}$] 29.692475 \n", + " invest [1e9 Euro] 19.300109 \n", + " operation [GW$_{el}$*h/a] 30221.748167 \n", + " [GW$_{el}$*h] 30221.748167 \n", + " opexCap [1e9 Euro/a] 0.386002 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " invest [1e9 Euro] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.111587 \n", + " TAC [1e9 Euro/a] 0.111587 \n", + " capacity [GW$_{el}$] 0.832509 \n", + " capexCap [1e9 Euro/a] 0.093272 \n", + " commissioning [GW$_{el}$] 0.832509 \n", + " invest [1e9 Euro] 0.91576 \n", + " operation [GW$_{el}$*h/a] 1132.333527 \n", + " [GW$_{el}$*h] 1132.333527 \n", + " opexCap [1e9 Euro/a] 0.018315 \n", + "\n", + " cluster_5 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.155594 \n", + " TAC [1e9 Euro/a] 0.155594 \n", + " commodCosts [1e9 Euro/a] 0.155594 \n", + " operation [GW$_{biogas,LHV}$*h/a] 2876.580098 \n", + " [GW$_{biogas,LHV}$*h] 2876.580098 \n", + "Electricity demand operation [GW$_{el}$*h/a] 41413.409155 \n", + " [GW$_{el}$*h] 41413.409155 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 5747.423572 \n", + " [GW$_{H_{2},LHV}$*h] 5747.423572 \n", + "PV NPVcontribution [1e9 Euro] 1.469767 \n", + " TAC [1e9 Euro/a] 1.469767 \n", + " capacity [GW$_{el}$] 19.890954 \n", + " capexCap [1e9 Euro/a] 1.211184 \n", + " commissioning [GW$_{el}$] 19.890954 \n", + " invest [1e9 Euro] 12.92912 \n", + " operation [GW$_{el}$*h/a] 19357.82702 \n", + " [GW$_{el}$*h] 19357.82702 \n", + " opexCap [1e9 Euro/a] 0.258582 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " invest [1e9 Euro] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "\n", + " cluster_6 \\\n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.164873 \n", + " TAC [1e9 Euro/a] 0.164873 \n", + " commodCosts [1e9 Euro/a] 0.164873 \n", + " operation [GW$_{biogas,LHV}$*h/a] 3048.12582 \n", + " [GW$_{biogas,LHV}$*h] 3048.12582 \n", + "Electricity demand operation [GW$_{el}$*h/a] 28088.766223 \n", + " [GW$_{el}$*h] 28088.766223 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 3710.050514 \n", + " [GW$_{H_{2},LHV}$*h] 3710.050514 \n", + "PV NPVcontribution [1e9 Euro] 0.933703 \n", + " TAC [1e9 Euro/a] 0.933703 \n", + " capacity [GW$_{el}$] 12.636188 \n", + " capexCap [1e9 Euro/a] 0.769433 \n", + " commissioning [GW$_{el}$] 12.636188 \n", + " invest [1e9 Euro] 8.213522 \n", + " operation [GW$_{el}$*h/a] 13518.741616 \n", + " [GW$_{el}$*h] 13518.741616 \n", + " opexCap [1e9 Euro/a] 0.16427 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 5.044681 \n", + " TAC [1e9 Euro/a] 5.044681 \n", + " capacity [GW$_{el}$] 18.0 \n", + " capexCap [1e9 Euro/a] 4.216681 \n", + " commissioning [GW$_{el}$] 18.0 \n", + " invest [1e9 Euro] 41.4 \n", + " operation [GW$_{el}$*h/a] 82532.968552 \n", + " [GW$_{el}$*h] 82532.968552 \n", + " opexCap [1e9 Euro/a] 0.828 \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 2.896902 \n", + " TAC [1e9 Euro/a] 2.896902 \n", + " capacity [GW$_{el}$] 21.612637 \n", + " capexCap [1e9 Euro/a] 2.421424 \n", + " commissioning [GW$_{el}$] 21.612637 \n", + " invest [1e9 Euro] 23.773901 \n", + " operation [GW$_{el}$*h/a] 42841.325457 \n", + " [GW$_{el}$*h] 42841.325457 \n", + " opexCap [1e9 Euro/a] 0.475478 \n", + "\n", + " cluster_7 \n", + "Component Property Unit \n", + "Biogas purchase NPVcontribution [1e9 Euro] 0.065985 \n", + " TAC [1e9 Euro/a] 0.065985 \n", + " commodCosts [1e9 Euro/a] 0.065985 \n", + " operation [GW$_{biogas,LHV}$*h/a] 1219.907894 \n", + " [GW$_{biogas,LHV}$*h] 1219.907894 \n", + "Electricity demand operation [GW$_{el}$*h/a] 15851.141245 \n", + " [GW$_{el}$*h] 15851.141245 \n", + "Existing run-of-river plants NPVcontribution [1e9 Euro] 0.067472 \n", + " TAC [1e9 Euro/a] 0.067472 \n", + " capacity [GW$_{el}$] 0.324386 \n", + " commissioning [GW$_{el}$] 0.324386 \n", + " operation [GW$_{el}$*h/a] 1480.823705 \n", + " [GW$_{el}$*h] 1480.823705 \n", + " opexCap [1e9 Euro/a] 0.067472 \n", + "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 1453.124166 \n", + " [GW$_{H_{2},LHV}$*h] 1453.124166 \n", + "PV NPVcontribution [1e9 Euro] 0.366282 \n", + " TAC [1e9 Euro/a] 0.366282 \n", + " capacity [GW$_{el}$] 4.957044 \n", + " capexCap [1e9 Euro/a] 0.30184 \n", + " commissioning [GW$_{el}$] 4.957044 \n", + " invest [1e9 Euro] 3.222079 \n", + " operation [GW$_{el}$*h/a] 4756.911042 \n", + " [GW$_{el}$*h] 4756.911042 \n", + " opexCap [1e9 Euro/a] 0.064442 \n", + "Wind (offshore) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{el}$] NaN \n", + " invest [1e9 Euro] NaN \n", + " operation [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Wind (onshore) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot installed capacities" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotLocationalColorMap(\n", + " esM, \"Wind (offshore)\", locFilePath, \"index\", perArea=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotLocationalColorMap(\n", + " esM, \"Wind (onshore)\", locFilePath, \"index\", perArea=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotLocationalColorMap(esM, \"PV\", locFilePath, \"index\", perArea=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot operation time series (either one or two dimensional)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperation(esM, \"Electricity demand\", \"cluster_0\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"cluster_0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conversion\n", + "\n", + "Show optimization summary" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
ComponentPropertyUnit
ElectrolyzerNPVcontribution[1e9 Euro]0.00.2548421.0511310.1520450.0213980.2078850.8298540.030687
TAC[1e9 Euro/a]0.00.2548421.0511310.1520450.0213980.2078850.8298540.030687
capacity[GW$_{el}$]0.02.9287212.0799131.7473530.2459092.3890789.5369310.352659
capexCap[1e9 Euro/a]0.00.2182330.9001320.1302040.0183240.1780220.7106420.026278
commissioning[GW$_{el}$]0.02.9287212.0799131.7473530.2459092.3890789.5369310.352659
invest[1e9 Euro]0.01.464366.0399570.8736760.1229541.1945394.7684650.17633
operation[GW$_{el}$*h/a]0.08751.30708240895.3251984760.062415938.6335088210.60510334873.238406953.511944
[GW$_{el}$*h]0.08751.30708240895.3251984760.062415938.6335088210.60510334873.238406953.511944
opexCap[1e9 Euro/a]0.00.0366090.1509990.0218420.0030740.0298630.1192120.004408
New CCGT plants (biogas)NPVcontribution[1e9 Euro]0.0269180.225070.2068360.1346030.0592630.2156490.00.014352
TAC[1e9 Euro/a]0.0269180.225070.2068360.1346030.0592630.2156490.00.014352
capacity[GW$_{el}$]0.3290892.7515952.5286791.6455890.7245152.636420.00.175466
capexCap[1e9 Euro/a]0.0200070.1672860.1537340.1000450.0440480.1602840.00.010668
commissioning[GW$_{el}$]0.3290892.7515952.5286791.6455890.7245152.636420.00.175466
invest[1e9 Euro]0.2303621.9261171.7700751.1519130.507161.8454940.00.122826
operation[GW$_{el}$*h/a]637.730674875.8266294733.8759572843.8196791473.7527864593.5723850.0371.421895
[GW$_{el}$*h]637.730674875.8266294733.8759572843.8196791473.7527864593.5723850.0371.421895
opexCap[1e9 Euro/a]0.0069110.0577830.0531020.0345570.0152150.0553650.00.003685
\n", + "
" + ], + "text/plain": [ + " cluster_0 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.026918 \n", + " TAC [1e9 Euro/a] 0.026918 \n", + " capacity [GW$_{el}$] 0.329089 \n", + " capexCap [1e9 Euro/a] 0.020007 \n", + " commissioning [GW$_{el}$] 0.329089 \n", + " invest [1e9 Euro] 0.230362 \n", + " operation [GW$_{el}$*h/a] 637.73067 \n", + " [GW$_{el}$*h] 637.73067 \n", + " opexCap [1e9 Euro/a] 0.006911 \n", + "\n", + " cluster_1 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.254842 \n", + " TAC [1e9 Euro/a] 0.254842 \n", + " capacity [GW$_{el}$] 2.92872 \n", + " capexCap [1e9 Euro/a] 0.218233 \n", + " commissioning [GW$_{el}$] 2.92872 \n", + " invest [1e9 Euro] 1.46436 \n", + " operation [GW$_{el}$*h/a] 8751.307082 \n", + " [GW$_{el}$*h] 8751.307082 \n", + " opexCap [1e9 Euro/a] 0.036609 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.22507 \n", + " TAC [1e9 Euro/a] 0.22507 \n", + " capacity [GW$_{el}$] 2.751595 \n", + " capexCap [1e9 Euro/a] 0.167286 \n", + " commissioning [GW$_{el}$] 2.751595 \n", + " invest [1e9 Euro] 1.926117 \n", + " operation [GW$_{el}$*h/a] 4875.826629 \n", + " [GW$_{el}$*h] 4875.826629 \n", + " opexCap [1e9 Euro/a] 0.057783 \n", + "\n", + " cluster_2 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 1.051131 \n", + " TAC [1e9 Euro/a] 1.051131 \n", + " capacity [GW$_{el}$] 12.079913 \n", + " capexCap [1e9 Euro/a] 0.900132 \n", + " commissioning [GW$_{el}$] 12.079913 \n", + " invest [1e9 Euro] 6.039957 \n", + " operation [GW$_{el}$*h/a] 40895.325198 \n", + " [GW$_{el}$*h] 40895.325198 \n", + " opexCap [1e9 Euro/a] 0.150999 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.206836 \n", + " TAC [1e9 Euro/a] 0.206836 \n", + " capacity [GW$_{el}$] 2.528679 \n", + " capexCap [1e9 Euro/a] 0.153734 \n", + " commissioning [GW$_{el}$] 2.528679 \n", + " invest [1e9 Euro] 1.770075 \n", + " operation [GW$_{el}$*h/a] 4733.875957 \n", + " [GW$_{el}$*h] 4733.875957 \n", + " opexCap [1e9 Euro/a] 0.053102 \n", + "\n", + " cluster_3 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.152045 \n", + " TAC [1e9 Euro/a] 0.152045 \n", + " capacity [GW$_{el}$] 1.747353 \n", + " capexCap [1e9 Euro/a] 0.130204 \n", + " commissioning [GW$_{el}$] 1.747353 \n", + " invest [1e9 Euro] 0.873676 \n", + " operation [GW$_{el}$*h/a] 4760.062415 \n", + " [GW$_{el}$*h] 4760.062415 \n", + " opexCap [1e9 Euro/a] 0.021842 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.134603 \n", + " TAC [1e9 Euro/a] 0.134603 \n", + " capacity [GW$_{el}$] 1.645589 \n", + " capexCap [1e9 Euro/a] 0.100045 \n", + " commissioning [GW$_{el}$] 1.645589 \n", + " invest [1e9 Euro] 1.151913 \n", + " operation [GW$_{el}$*h/a] 2843.819679 \n", + " [GW$_{el}$*h] 2843.819679 \n", + " opexCap [1e9 Euro/a] 0.034557 \n", + "\n", + " cluster_4 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.021398 \n", + " TAC [1e9 Euro/a] 0.021398 \n", + " capacity [GW$_{el}$] 0.245909 \n", + " capexCap [1e9 Euro/a] 0.018324 \n", + " commissioning [GW$_{el}$] 0.245909 \n", + " invest [1e9 Euro] 0.122954 \n", + " operation [GW$_{el}$*h/a] 938.633508 \n", + " [GW$_{el}$*h] 938.633508 \n", + " opexCap [1e9 Euro/a] 0.003074 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.059263 \n", + " TAC [1e9 Euro/a] 0.059263 \n", + " capacity [GW$_{el}$] 0.724515 \n", + " capexCap [1e9 Euro/a] 0.044048 \n", + " commissioning [GW$_{el}$] 0.724515 \n", + " invest [1e9 Euro] 0.50716 \n", + " operation [GW$_{el}$*h/a] 1473.752786 \n", + " [GW$_{el}$*h] 1473.752786 \n", + " opexCap [1e9 Euro/a] 0.015215 \n", + "\n", + " cluster_5 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.207885 \n", + " TAC [1e9 Euro/a] 0.207885 \n", + " capacity [GW$_{el}$] 2.389078 \n", + " capexCap [1e9 Euro/a] 0.178022 \n", + " commissioning [GW$_{el}$] 2.389078 \n", + " invest [1e9 Euro] 1.194539 \n", + " operation [GW$_{el}$*h/a] 8210.605103 \n", + " [GW$_{el}$*h] 8210.605103 \n", + " opexCap [1e9 Euro/a] 0.029863 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.215649 \n", + " TAC [1e9 Euro/a] 0.215649 \n", + " capacity [GW$_{el}$] 2.63642 \n", + " capexCap [1e9 Euro/a] 0.160284 \n", + " commissioning [GW$_{el}$] 2.63642 \n", + " invest [1e9 Euro] 1.845494 \n", + " operation [GW$_{el}$*h/a] 4593.572385 \n", + " [GW$_{el}$*h] 4593.572385 \n", + " opexCap [1e9 Euro/a] 0.055365 \n", + "\n", + " cluster_6 \\\n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.829854 \n", + " TAC [1e9 Euro/a] 0.829854 \n", + " capacity [GW$_{el}$] 9.536931 \n", + " capexCap [1e9 Euro/a] 0.710642 \n", + " commissioning [GW$_{el}$] 9.536931 \n", + " invest [1e9 Euro] 4.768465 \n", + " operation [GW$_{el}$*h/a] 34873.238406 \n", + " [GW$_{el}$*h] 34873.238406 \n", + " opexCap [1e9 Euro/a] 0.119212 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operation [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "\n", + " cluster_7 \n", + "Component Property Unit \n", + "Electrolyzer NPVcontribution [1e9 Euro] 0.030687 \n", + " TAC [1e9 Euro/a] 0.030687 \n", + " capacity [GW$_{el}$] 0.352659 \n", + " capexCap [1e9 Euro/a] 0.026278 \n", + " commissioning [GW$_{el}$] 0.352659 \n", + " invest [1e9 Euro] 0.17633 \n", + " operation [GW$_{el}$*h/a] 953.511944 \n", + " [GW$_{el}$*h] 953.511944 \n", + " opexCap [1e9 Euro/a] 0.004408 \n", + "New CCGT plants (biogas) NPVcontribution [1e9 Euro] 0.014352 \n", + " TAC [1e9 Euro/a] 0.014352 \n", + " capacity [GW$_{el}$] 0.175466 \n", + " capexCap [1e9 Euro/a] 0.010668 \n", + " commissioning [GW$_{el}$] 0.175466 \n", + " invest [1e9 Euro] 0.122826 \n", + " operation [GW$_{el}$*h/a] 371.421895 \n", + " [GW$_{el}$*h] 371.421895 \n", + " opexCap [1e9 Euro/a] 0.003685 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true, + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (biogas)\", \"cluster_2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Storage\n", + "\n", + "Show optimization summary" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
ComponentPropertyUnit
Li-ion batteriesNPVcontribution[1e9 Euro]0.235460.3245110.4110320.1604270.1150090.00.832980.072279
TAC[1e9 Euro/a]0.235460.3245110.4110320.1604270.1150090.00.832980.072279
capacity[GW$_{el}$*h]14.01309619.31286124.4620469.5475976.8446250.049.5737554.301578
capexCap[1e9 Euro/a]0.2074340.2858850.3621080.1413320.101320.00.7338320.063676
commissioning[GW$_{el}$*h]14.01309619.31286124.4620469.5475976.8446250.049.5737554.301578
invest[1e9 Euro]2.1159782.9162423.6937691.4416871.0335380.07.4856370.649538
operationCharge[GW$_{el}$*h/a]3815.7312285937.76225411119.1118873323.1983392381.1792610.014292.1189411955.262677
[GW$_{el}$*h]3815.7312285937.76225411119.1118873323.1983392381.1792610.014292.1189411955.262677
operationDischarge[GW$_{el}$*h/a]3440.4625445354.09855310030.2873742997.322148.2287220.012887.7220641764.08572
[GW$_{el}$*h]3440.4625445354.09855310030.2873742997.322148.2287220.012887.7220641764.08572
opexCap[1e9 Euro/a]0.0280260.0386260.0489240.0190950.0136890.00.0991480.008603
Pumped hydro storageNPVcontribution[1e9 Euro]0.0024360.0011860.0001250.0030020.00110.003830.0000920
TAC[1e9 Euro/a]0.0024360.0011860.0001250.0030020.00110.003830.0000920
capacity[GW$_{el}$*h]15.9247.7510.81819.627.1925.0340.6NaN
commissioning[GW$_{el}$*h]15.9247.7510.81819.627.1925.0340.6NaN
operationCharge[GW$_{el}$*h/a]5053.3554572743.770024358.3807127382.4489953004.3585957796.60173237.186928NaN
[GW$_{el}$*h]5053.3554572743.770024358.3807127382.4489953004.3585957796.60173237.186928NaN
operationDischarge[GW$_{el}$*h/a]3913.0182412124.62638277.5131495716.5982012326.4338486037.211511183.658492NaN
[GW$_{el}$*h]3913.0182412124.62638277.5131495716.5982012326.4338486037.211511183.658492NaN
opexCap[1e9 Euro/a]0.0024360.0011860.0001250.0030020.00110.003830.000092NaN
Salt caverns (biogas)NPVcontribution[1e9 Euro]00.0222110.0240970.01336700.0269630.00
TAC[1e9 Euro/a]00.0222110.0240970.01336700.0269630.00
capacity[GW$_{biogas,LHV}$*h]NaN1638.8242921778.004519986.248301NaN1989.4012710.0NaN
capexCap[1e9 Euro/a]NaN0.0058230.0063170.003504NaN0.0070690.0NaN
commissioning[GW$_{biogas,LHV}$*h]NaN1638.8242921778.004519986.248301NaN1989.4012710.0NaN
invest[1e9 Euro]NaN0.0655530.071120.03945NaN0.0795760.0NaN
operationCharge[GW$_{biogas,LHV}$*h/a]NaN6173.8559987117.29001814786.732976NaN7268.7701310.0NaN
[GW$_{biogas,LHV}$*h]NaN6173.8559987117.29001814786.732976NaN7268.7701310.0NaN
operationDischarge[GW$_{biogas,LHV}$*h/a]NaN6173.8559987117.29001814786.732976NaN7268.7701310.0NaN
[GW$_{biogas,LHV}$*h]NaN6173.8559987117.29001814786.732976NaN7268.7701310.0NaN
opexCap[1e9 Euro/a]NaN0.0163880.017780.009862NaN0.0198940.0NaN
Salt caverns (hydrogen)NPVcontribution[1e9 Euro]00.7336381.4410420.33936700.277140.6326080
TAC[1e9 Euro/a]00.7336381.4410420.33936700.277140.6326080
capacity[GW$_{H_{2},LHV}$*h]NaN1265.3933782485.536832585.345915NaN478.0162091091.134846NaN
capexCap[1e9 Euro/a]NaN0.0123640.0242860.005719NaN0.0046710.010661NaN
commissioning[GW$_{H_{2},LHV}$*h]NaN1265.3933782485.536832585.345915NaN478.0162091091.134846NaN
invest[1e9 Euro]NaN0.1391930.2734090.064388NaN0.0525820.120025NaN
operationCharge[GW$_{H_{2},LHV}$*h/a]NaN7510.06847416078.3509117527.457838NaN3401.66751613469.50916NaN
[GW$_{H_{2},LHV}$*h]NaN7510.06847416078.3509117527.457838NaN3401.66751613469.50916NaN
operationDischarge[GW$_{H_{2},LHV}$*h/a]NaN7510.06847416078.3509117527.457838NaN3401.66751613469.50916NaN
[GW$_{H_{2},LHV}$*h]NaN7510.06847416078.3509117527.457838NaN3401.66751613469.50916NaN
opexCap[1e9 Euro/a]NaN0.7212741.4167560.333647NaN0.2724690.621947NaN
\n", + "
" + ], + "text/plain": [ + " cluster_0 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.23546 \n", + " TAC [1e9 Euro/a] 0.23546 \n", + " capacity [GW$_{el}$*h] 14.013096 \n", + " capexCap [1e9 Euro/a] 0.207434 \n", + " commissioning [GW$_{el}$*h] 14.013096 \n", + " invest [1e9 Euro] 2.115978 \n", + " operationCharge [GW$_{el}$*h/a] 3815.731228 \n", + " [GW$_{el}$*h] 3815.731228 \n", + " operationDischarge [GW$_{el}$*h/a] 3440.462544 \n", + " [GW$_{el}$*h] 3440.462544 \n", + " opexCap [1e9 Euro/a] 0.028026 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.002436 \n", + " TAC [1e9 Euro/a] 0.002436 \n", + " capacity [GW$_{el}$*h] 15.924 \n", + " commissioning [GW$_{el}$*h] 15.924 \n", + " operationCharge [GW$_{el}$*h/a] 5053.355457 \n", + " [GW$_{el}$*h] 5053.355457 \n", + " operationDischarge [GW$_{el}$*h/a] 3913.018241 \n", + " [GW$_{el}$*h] 3913.018241 \n", + " opexCap [1e9 Euro/a] 0.002436 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{biogas,LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{biogas,LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{H_{2},LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{H_{2},LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "\n", + " cluster_1 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.324511 \n", + " TAC [1e9 Euro/a] 0.324511 \n", + " capacity [GW$_{el}$*h] 19.312861 \n", + " capexCap [1e9 Euro/a] 0.285885 \n", + " commissioning [GW$_{el}$*h] 19.312861 \n", + " invest [1e9 Euro] 2.916242 \n", + " operationCharge [GW$_{el}$*h/a] 5937.762254 \n", + " [GW$_{el}$*h] 5937.762254 \n", + " operationDischarge [GW$_{el}$*h/a] 5354.098553 \n", + " [GW$_{el}$*h] 5354.098553 \n", + " opexCap [1e9 Euro/a] 0.038626 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.001186 \n", + " TAC [1e9 Euro/a] 0.001186 \n", + " capacity [GW$_{el}$*h] 7.751 \n", + " commissioning [GW$_{el}$*h] 7.751 \n", + " operationCharge [GW$_{el}$*h/a] 2743.770024 \n", + " [GW$_{el}$*h] 2743.770024 \n", + " operationDischarge [GW$_{el}$*h/a] 2124.62638 \n", + " [GW$_{el}$*h] 2124.62638 \n", + " opexCap [1e9 Euro/a] 0.001186 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0.022211 \n", + " TAC [1e9 Euro/a] 0.022211 \n", + " capacity [GW$_{biogas,LHV}$*h] 1638.824292 \n", + " capexCap [1e9 Euro/a] 0.005823 \n", + " commissioning [GW$_{biogas,LHV}$*h] 1638.824292 \n", + " invest [1e9 Euro] 0.065553 \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] 6173.855998 \n", + " [GW$_{biogas,LHV}$*h] 6173.855998 \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] 6173.855998 \n", + " [GW$_{biogas,LHV}$*h] 6173.855998 \n", + " opexCap [1e9 Euro/a] 0.016388 \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0.733638 \n", + " TAC [1e9 Euro/a] 0.733638 \n", + " capacity [GW$_{H_{2},LHV}$*h] 1265.393378 \n", + " capexCap [1e9 Euro/a] 0.012364 \n", + " commissioning [GW$_{H_{2},LHV}$*h] 1265.393378 \n", + " invest [1e9 Euro] 0.139193 \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 7510.068474 \n", + " [GW$_{H_{2},LHV}$*h] 7510.068474 \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 7510.068474 \n", + " [GW$_{H_{2},LHV}$*h] 7510.068474 \n", + " opexCap [1e9 Euro/a] 0.721274 \n", + "\n", + " cluster_2 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.411032 \n", + " TAC [1e9 Euro/a] 0.411032 \n", + " capacity [GW$_{el}$*h] 24.462046 \n", + " capexCap [1e9 Euro/a] 0.362108 \n", + " commissioning [GW$_{el}$*h] 24.462046 \n", + " invest [1e9 Euro] 3.693769 \n", + " operationCharge [GW$_{el}$*h/a] 11119.111887 \n", + " [GW$_{el}$*h] 11119.111887 \n", + " operationDischarge [GW$_{el}$*h/a] 10030.287374 \n", + " [GW$_{el}$*h] 10030.287374 \n", + " opexCap [1e9 Euro/a] 0.048924 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.000125 \n", + " TAC [1e9 Euro/a] 0.000125 \n", + " capacity [GW$_{el}$*h] 0.818 \n", + " commissioning [GW$_{el}$*h] 0.818 \n", + " operationCharge [GW$_{el}$*h/a] 358.380712 \n", + " [GW$_{el}$*h] 358.380712 \n", + " operationDischarge [GW$_{el}$*h/a] 277.513149 \n", + " [GW$_{el}$*h] 277.513149 \n", + " opexCap [1e9 Euro/a] 0.000125 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0.024097 \n", + " TAC [1e9 Euro/a] 0.024097 \n", + " capacity [GW$_{biogas,LHV}$*h] 1778.004519 \n", + " capexCap [1e9 Euro/a] 0.006317 \n", + " commissioning [GW$_{biogas,LHV}$*h] 1778.004519 \n", + " invest [1e9 Euro] 0.07112 \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] 7117.290018 \n", + " [GW$_{biogas,LHV}$*h] 7117.290018 \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] 7117.290018 \n", + " [GW$_{biogas,LHV}$*h] 7117.290018 \n", + " opexCap [1e9 Euro/a] 0.01778 \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 1.441042 \n", + " TAC [1e9 Euro/a] 1.441042 \n", + " capacity [GW$_{H_{2},LHV}$*h] 2485.536832 \n", + " capexCap [1e9 Euro/a] 0.024286 \n", + " commissioning [GW$_{H_{2},LHV}$*h] 2485.536832 \n", + " invest [1e9 Euro] 0.273409 \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 16078.350911 \n", + " [GW$_{H_{2},LHV}$*h] 16078.350911 \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 16078.350911 \n", + " [GW$_{H_{2},LHV}$*h] 16078.350911 \n", + " opexCap [1e9 Euro/a] 1.416756 \n", + "\n", + " cluster_3 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.160427 \n", + " TAC [1e9 Euro/a] 0.160427 \n", + " capacity [GW$_{el}$*h] 9.547597 \n", + " capexCap [1e9 Euro/a] 0.141332 \n", + " commissioning [GW$_{el}$*h] 9.547597 \n", + " invest [1e9 Euro] 1.441687 \n", + " operationCharge [GW$_{el}$*h/a] 3323.198339 \n", + " [GW$_{el}$*h] 3323.198339 \n", + " operationDischarge [GW$_{el}$*h/a] 2997.32 \n", + " [GW$_{el}$*h] 2997.32 \n", + " opexCap [1e9 Euro/a] 0.019095 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.003002 \n", + " TAC [1e9 Euro/a] 0.003002 \n", + " capacity [GW$_{el}$*h] 19.62 \n", + " commissioning [GW$_{el}$*h] 19.62 \n", + " operationCharge [GW$_{el}$*h/a] 7382.448995 \n", + " [GW$_{el}$*h] 7382.448995 \n", + " operationDischarge [GW$_{el}$*h/a] 5716.598201 \n", + " [GW$_{el}$*h] 5716.598201 \n", + " opexCap [1e9 Euro/a] 0.003002 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0.013367 \n", + " TAC [1e9 Euro/a] 0.013367 \n", + " capacity [GW$_{biogas,LHV}$*h] 986.248301 \n", + " capexCap [1e9 Euro/a] 0.003504 \n", + " commissioning [GW$_{biogas,LHV}$*h] 986.248301 \n", + " invest [1e9 Euro] 0.03945 \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] 14786.732976 \n", + " [GW$_{biogas,LHV}$*h] 14786.732976 \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] 14786.732976 \n", + " [GW$_{biogas,LHV}$*h] 14786.732976 \n", + " opexCap [1e9 Euro/a] 0.009862 \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0.339367 \n", + " TAC [1e9 Euro/a] 0.339367 \n", + " capacity [GW$_{H_{2},LHV}$*h] 585.345915 \n", + " capexCap [1e9 Euro/a] 0.005719 \n", + " commissioning [GW$_{H_{2},LHV}$*h] 585.345915 \n", + " invest [1e9 Euro] 0.064388 \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 7527.457838 \n", + " [GW$_{H_{2},LHV}$*h] 7527.457838 \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 7527.457838 \n", + " [GW$_{H_{2},LHV}$*h] 7527.457838 \n", + " opexCap [1e9 Euro/a] 0.333647 \n", + "\n", + " cluster_4 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.115009 \n", + " TAC [1e9 Euro/a] 0.115009 \n", + " capacity [GW$_{el}$*h] 6.844625 \n", + " capexCap [1e9 Euro/a] 0.10132 \n", + " commissioning [GW$_{el}$*h] 6.844625 \n", + " invest [1e9 Euro] 1.033538 \n", + " operationCharge [GW$_{el}$*h/a] 2381.179261 \n", + " [GW$_{el}$*h] 2381.179261 \n", + " operationDischarge [GW$_{el}$*h/a] 2148.228722 \n", + " [GW$_{el}$*h] 2148.228722 \n", + " opexCap [1e9 Euro/a] 0.013689 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.0011 \n", + " TAC [1e9 Euro/a] 0.0011 \n", + " capacity [GW$_{el}$*h] 7.19 \n", + " commissioning [GW$_{el}$*h] 7.19 \n", + " operationCharge [GW$_{el}$*h/a] 3004.358595 \n", + " [GW$_{el}$*h] 3004.358595 \n", + " operationDischarge [GW$_{el}$*h/a] 2326.433848 \n", + " [GW$_{el}$*h] 2326.433848 \n", + " opexCap [1e9 Euro/a] 0.0011 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{biogas,LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{biogas,LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{H_{2},LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{H_{2},LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "\n", + " cluster_5 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{el}$*h] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{el}$*h] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operationCharge [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " operationDischarge [GW$_{el}$*h/a] 0.0 \n", + " [GW$_{el}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.00383 \n", + " TAC [1e9 Euro/a] 0.00383 \n", + " capacity [GW$_{el}$*h] 25.034 \n", + " commissioning [GW$_{el}$*h] 25.034 \n", + " operationCharge [GW$_{el}$*h/a] 7796.60173 \n", + " [GW$_{el}$*h] 7796.60173 \n", + " operationDischarge [GW$_{el}$*h/a] 6037.211511 \n", + " [GW$_{el}$*h] 6037.211511 \n", + " opexCap [1e9 Euro/a] 0.00383 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0.026963 \n", + " TAC [1e9 Euro/a] 0.026963 \n", + " capacity [GW$_{biogas,LHV}$*h] 1989.401271 \n", + " capexCap [1e9 Euro/a] 0.007069 \n", + " commissioning [GW$_{biogas,LHV}$*h] 1989.401271 \n", + " invest [1e9 Euro] 0.079576 \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] 7268.770131 \n", + " [GW$_{biogas,LHV}$*h] 7268.770131 \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] 7268.770131 \n", + " [GW$_{biogas,LHV}$*h] 7268.770131 \n", + " opexCap [1e9 Euro/a] 0.019894 \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0.27714 \n", + " TAC [1e9 Euro/a] 0.27714 \n", + " capacity [GW$_{H_{2},LHV}$*h] 478.016209 \n", + " capexCap [1e9 Euro/a] 0.004671 \n", + " commissioning [GW$_{H_{2},LHV}$*h] 478.016209 \n", + " invest [1e9 Euro] 0.052582 \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 3401.667516 \n", + " [GW$_{H_{2},LHV}$*h] 3401.667516 \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 3401.667516 \n", + " [GW$_{H_{2},LHV}$*h] 3401.667516 \n", + " opexCap [1e9 Euro/a] 0.272469 \n", + "\n", + " cluster_6 \\\n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.83298 \n", + " TAC [1e9 Euro/a] 0.83298 \n", + " capacity [GW$_{el}$*h] 49.573755 \n", + " capexCap [1e9 Euro/a] 0.733832 \n", + " commissioning [GW$_{el}$*h] 49.573755 \n", + " invest [1e9 Euro] 7.485637 \n", + " operationCharge [GW$_{el}$*h/a] 14292.118941 \n", + " [GW$_{el}$*h] 14292.118941 \n", + " operationDischarge [GW$_{el}$*h/a] 12887.722064 \n", + " [GW$_{el}$*h] 12887.722064 \n", + " opexCap [1e9 Euro/a] 0.099148 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0.000092 \n", + " TAC [1e9 Euro/a] 0.000092 \n", + " capacity [GW$_{el}$*h] 0.6 \n", + " commissioning [GW$_{el}$*h] 0.6 \n", + " operationCharge [GW$_{el}$*h/a] 237.186928 \n", + " [GW$_{el}$*h] 237.186928 \n", + " operationDischarge [GW$_{el}$*h/a] 183.658492 \n", + " [GW$_{el}$*h] 183.658492 \n", + " opexCap [1e9 Euro/a] 0.000092 \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0.0 \n", + " TAC [1e9 Euro/a] 0.0 \n", + " capacity [GW$_{biogas,LHV}$*h] 0.0 \n", + " capexCap [1e9 Euro/a] 0.0 \n", + " commissioning [GW$_{biogas,LHV}$*h] 0.0 \n", + " invest [1e9 Euro] 0.0 \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] 0.0 \n", + " [GW$_{biogas,LHV}$*h] 0.0 \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] 0.0 \n", + " [GW$_{biogas,LHV}$*h] 0.0 \n", + " opexCap [1e9 Euro/a] 0.0 \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0.632608 \n", + " TAC [1e9 Euro/a] 0.632608 \n", + " capacity [GW$_{H_{2},LHV}$*h] 1091.134846 \n", + " capexCap [1e9 Euro/a] 0.010661 \n", + " commissioning [GW$_{H_{2},LHV}$*h] 1091.134846 \n", + " invest [1e9 Euro] 0.120025 \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 13469.50916 \n", + " [GW$_{H_{2},LHV}$*h] 13469.50916 \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 13469.50916 \n", + " [GW$_{H_{2},LHV}$*h] 13469.50916 \n", + " opexCap [1e9 Euro/a] 0.621947 \n", + "\n", + " cluster_7 \n", + "Component Property Unit \n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.072279 \n", + " TAC [1e9 Euro/a] 0.072279 \n", + " capacity [GW$_{el}$*h] 4.301578 \n", + " capexCap [1e9 Euro/a] 0.063676 \n", + " commissioning [GW$_{el}$*h] 4.301578 \n", + " invest [1e9 Euro] 0.649538 \n", + " operationCharge [GW$_{el}$*h/a] 1955.262677 \n", + " [GW$_{el}$*h] 1955.262677 \n", + " operationDischarge [GW$_{el}$*h/a] 1764.08572 \n", + " [GW$_{el}$*h] 1764.08572 \n", + " opexCap [1e9 Euro/a] 0.008603 \n", + "Pumped hydro storage NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{el}$*h] NaN \n", + " commissioning [GW$_{el}$*h] NaN \n", + " operationCharge [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " operationDischarge [GW$_{el}$*h/a] NaN \n", + " [GW$_{el}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Salt caverns (biogas) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{biogas,LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{biogas,LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", + " [GW$_{biogas,LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN \n", + "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0 \n", + " TAC [1e9 Euro/a] 0 \n", + " capacity [GW$_{H_{2},LHV}$*h] NaN \n", + " capexCap [1e9 Euro/a] NaN \n", + " commissioning [GW$_{H_{2},LHV}$*h] NaN \n", + " invest [1e9 Euro] NaN \n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", + " [GW$_{H_{2},LHV}$*h] NaN \n", + " opexCap [1e9 Euro/a] NaN " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Li-ion batteries\",\n", + " \"cluster_2\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Pumped hydro storage\",\n", + " \"cluster_2\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Salt caverns (biogas)\",\n", + " \"cluster_2\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(\n", + " esM,\n", + " \"Salt caverns (hydrogen)\",\n", + " \"cluster_2\",\n", + " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transmission\n", + "\n", + "Show optimization summary" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
ComponentPropertyUnitLocationIn
AC cablescapacity[GW$_{el}$]cluster_0NaN7.011.0NaN18.04.0NaN2.5
cluster_17.0NaN10.0NaNNaN3.06.0NaN
cluster_211.010.0NaNNaNNaNNaNNaN3.0
cluster_3NaNNaNNaNNaNNaN12.02.4NaN
cluster_418.0NaNNaNNaNNaN4.0NaNNaN
....................................
Pipelines (hydrogen)operation[GW$_{H_{2},LHV}$*h]cluster_211058.484007480.65671NaNNaNNaNNaNNaN1065.218488
cluster_3NaN0.0NaNNaNNaN0.0499.601569NaN
cluster_43.52879NaNNaNNaNNaN0.0NaNNaN
cluster_6NaN19575.251054NaN2431.689743NaNNaNNaNNaN
cluster_70.0NaN279.552682NaNNaNNaNNaNNaN
\n", + "

190 rows × 8 columns

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" + ], + "text/plain": [ + " cluster_0 cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 \\\n", + "cluster_0 NaN 1.678444 2.064235 NaN 1.558781 0.000000 \n", + "cluster_1 1.678444 NaN 0.092552 0.000000 NaN 0.000000 \n", + "cluster_2 2.064235 0.092552 NaN NaN NaN NaN \n", + "cluster_3 NaN 0.000000 NaN NaN NaN 0.000000 \n", + "cluster_4 1.558781 NaN NaN NaN NaN 1.150024 \n", + "cluster_5 0.000000 0.000000 NaN 0.000000 1.150024 NaN \n", + "cluster_6 NaN 3.658333 NaN 0.622219 NaN NaN \n", + "cluster_7 0.000000 NaN 0.305141 NaN NaN NaN \n", + "\n", + " cluster_6 cluster_7 \n", + "cluster_0 NaN 0.000000 \n", + "cluster_1 3.658333 NaN \n", + "cluster_2 NaN 0.305141 \n", + "cluster_3 0.622219 NaN \n", + "cluster_4 NaN NaN \n", + "cluster_5 NaN NaN \n", + "cluster_6 NaN NaN \n", + "cluster_7 NaN NaN " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = esM.componentModelingDict[\"TransmissionModel\"].capacityVariablesOptimum\n", + "df.loc[\"Pipelines (biogas)\"] + df.loc[\"Pipelines (hydrogen)\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot installed transmission capacities" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"AClines.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"AC cables\", transFilePath, loc0=\"bus0\", loc1=\"bus1\", fig=fig, ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"DClines.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"DC cables\", transFilePath, loc0=\"cluster0\", loc1=\"cluster1\", fig=fig, ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
cluster_0NaN1.6784442.064235NaN1.5587810.000000NaN0.000000
cluster_11.678444NaN0.0925520.000000NaN0.0000003.658333NaN
cluster_22.0642350.092552NaNNaNNaNNaNNaN0.305141
cluster_3NaN0.000000NaNNaNNaN0.0000000.622219NaN
cluster_41.558781NaNNaNNaNNaN1.150024NaNNaN
cluster_50.0000000.000000NaN0.0000001.150024NaNNaNNaN
cluster_6NaN3.658333NaN0.622219NaNNaNNaNNaN
cluster_70.000000NaN0.305141NaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " cluster_0 cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 \\\n", + "cluster_0 NaN 1.678444 2.064235 NaN 1.558781 0.000000 \n", + "cluster_1 1.678444 NaN 0.092552 0.000000 NaN 0.000000 \n", + "cluster_2 2.064235 0.092552 NaN NaN NaN NaN \n", + "cluster_3 NaN 0.000000 NaN NaN NaN 0.000000 \n", + "cluster_4 1.558781 NaN NaN NaN NaN 1.150024 \n", + "cluster_5 0.000000 0.000000 NaN 0.000000 1.150024 NaN \n", + "cluster_6 NaN 3.658333 NaN 0.622219 NaN NaN \n", + "cluster_7 0.000000 NaN 0.305141 NaN NaN NaN \n", + "\n", + " cluster_6 cluster_7 \n", + "cluster_0 NaN 0.000000 \n", + "cluster_1 3.658333 NaN \n", + "cluster_2 NaN 0.305141 \n", + "cluster_3 0.622219 NaN \n", + "cluster_4 NaN NaN \n", + "cluster_5 NaN NaN \n", + "cluster_6 NaN NaN \n", + "cluster_7 NaN NaN " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = esM.componentModelingDict[\"TransmissionModel\"].capacityVariablesOptimum\n", + "df.loc[\"Pipelines (biogas)\"] + df.loc[\"Pipelines (hydrogen)\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot installed transmission capacities" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"AClines.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"AC cables\", transFilePath, loc0=\"bus0\", loc1=\"bus1\", fig=fig, ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"DClines.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"DC cables\", transFilePath, loc0=\"cluster0\", loc1=\"cluster1\", fig=fig, ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"transmissionPipeline.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"Pipelines (hydrogen)\", transFilePath, loc0=\"loc1\", loc1=\"loc2\", fig=fig, ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "tags": [ + "nbval-skip" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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y+vTpCo8ruc6ya9cuMnfuXMLhcEjDhg0JAKKmpkZ0dXXJli1byPfv38lvv/3G+kWNPxqpiO7duxMAzM2HPFHyNSYnJ5NFixaR2bNnC4zSS3vUr1+f2NnZEV1dXdKhQ4dSj2nXrh0h5H83PiUfGzZsqFCbv78/MTc3Z87hO554enpK8i2hVAJq5CiV5sOHDwIXg4yMDJKfn1/qsTweTyBeLicnh3h5eZHt27eTOXPmEH9/f6adOXPmSOsllMq7d++ELnQ9e/YkUVFRZXos+vn5EQDk0KFDUlZbjK+vr5Dm5OTkUo8FQNzc3KSssPosXbq0VEP+8OFD5saI/9rNzMzIggULGA/esh58o/fs2TNCSPH38unTp6RPnz7E0NCQ1K9fnwDFjlSikJeXR2JjYwkhhEyZMkWgL77nJYUdqJGjVAkA5MiRI0IjmRkzZlRqtMD2SOhnli9fTtzc3MjRo0dL9ar7mcGDBxPgfy7ohBBmaszV1ZWsXbu20unPKkNBQQHzHi5fvpwAIEuXLi312J07dxJAtr1ES6O0aeFffvmlXCNWcmRlZ2fHPF+5ciVzwwKARERElNkn/zteFSIiIsjbt2/JqVOnCAAyevToKrVDqT6yc3WhyBWOjo4CFxI+HA6HiZvjU1RURF6+fEkmTJhAZs+eTXJzc8mPHz9kzsBVhZLvwcaNG5nn/fv3J+7u7szzevXqSVxHQkICAUDGjx8voA0oztZx4sQJAoD06NFDolrECX8djg+PxyN79+5lXtfPbv87d+4ku3fvFjinsLCQeX9KYmFhQQAQKysrZjo7NjaW+Pn5kWPHjhF9fX0CgJw4caJar8HLy0vod0KRHvJ9haGwSkxMjNC2+Ph4ke6wa4KBI4SQlJQUgddjY2ND2rdvL3DM/v37mf0/fvyQqJ6SQfolH507d5a7933EiBGV+i79/Bg0aBAhhJD79++X+Zor6qOyNyc3btwgw4YNI97e3uTly5fE2dlZIEnCo0ePqv2+UCqHfHzbKXIB/2LCf1y/fp3xrCxJTbqj5Tt0AKUHDfMpmQkDAHny5Em1+t29ezdZt24defLkCQkPDyc5OTkCVR7MzMzIo0ePSEZGhtC58jJdWfL9On78eKlGSEdHh8TGxhIA5OrVqwQA0dfXZ7Lr3L17lwAgTZs2LbOfJk2aVNn4c7lccv78eZFu5uTpBqMmQd9xithYvXq1wA/7/fv3bEuSKCXjrES9ePn6+hJbW1vmnLp165LCwsJK9SvKKEZNTa0qL0lm6NOnj9D7+vONwvDhw8ttg3/cz/GaP2NnZ8d491aWn9/3Vq1albqdGjj2oO86RWwoKSnVqh91yde5ZcuWSp9/+PDhShmlcePGMcfzR2hHjhwhTZs2Jb169RLQU9oIWp74+Tv0+PFj5rmo7vk/rw2Xxd9//12l7yz/85g5cyZZt24dAYqzpRBCiKqqqtBvQR49W2sCNfsqRJEaP99lDxkyhHz79o1tWRJFXMb82rVrFbazbds25oJakR5556+//mJeCz+fKf+5JMJMfv7uitLPhQsXCAAydepUQkhxGrVr164JHPPzFOvVq1fFrp1SMfL/i6DIBK9fv2ZGI/IYcFwVFi5cKDbDwvcSHDt2rNC+Y8eOEaDYeaQ8xo0bx3hWyjM/3zzw84dmZWVJrE++R2bJx8WLF4WO4/F4JDExkQAgpqamFbZbMsEAh8ORhHRKBdB6chSx4OXlhby8PDRu3BjKysoin7dgwQKRqorLIps3bwYA2NraVrstFRUVLFiwACdOnMDu3btx9epVnD17FkZGRhg/fjxUVVXx9OnTctuwtbVFcnJytbWwiZaWFvN/fn4+OBwO7O3tAUCgNqG4qVu3rtC2IUOG4OXLlwCAnJwcpkaioaEhAODmzZsVtnv16lUQQgAAq1atKvO4W7du4cSJE1WRTqkItq0spWawYMEC5o41JCSEEFLseebn50du3LhBioqKSHx8PPn7778ZR4tRo0bJ9RRbs2bNxN4mP1F1ycf+/ftFOvfGjRvEyspK7JqkQUnPUH4Ata6uLgGkF19WMj2XqA9xAYA0btxYbO1R/geHkP+/zaBQqknJEdmmTZuwaNEikc4zNzfHjx8/JCWr1lBQUABVVVXI2096yJAhuHTpEgAgLi4OBgYGApXYg4ODYWNjIxUtHA4H7du3h46ODu7duwcHBwfEx8cjNjZW4LiwsDA0atQIPB5PLDMRXC6XVp+XEHS6kiI2CCHg8XgAwBg4Urzui7i4OGRlZWHfvn1C54WFhUlVZ01FRUWFbQmVIjw8HBwOB5cuXcLChQtBCIGxsTEUFRXh4eHBHGdrawt3d3epaCKE4OXLl7h79y7c3d1haGjIGLiGDRtCVVUVANCoUSMAqLSBy8nJgZ+fn9B2auAkBx3JUaSKnZ0dgoODmee5ublQU1NjUVHNgsPhyMVILjk5GQYGBgBQpt6ioiLs378fqqqqGDVqFNTV1aUpUQAOh4OmTZvi8+fPAIrXkrds2VLpdt6+fQsnJye5+IxqCtTIUaQKh8PBqFGjcOrUKbal1EjkxcjxR0DyoBUArly5gkGDBiEhIYFxPKkqPB4Ply5dQrNmzaQ2DVubodOVFKnDn+qhSAZZX98MCAgAUDyKlxcGDhwIADAyMgKHw2EeorB//37GS3TSpElQVFTEsGHDxOKVS6kYauRqKJMmTQKHw8GmTZuQlpbGthwAxe7UADB16lSWldRcevfuXeqajyzRvHlzAJC7aWptbW2hbc+ePSv3nO7du2PatGkAgKCgIBw5cgT79u3DvXv3JKKRIgydrqyhNGnSBN++fWOey8LH7ODgAGVlZbx9+5ZtKTUSAwMDJCcn48GDB+jRowfbchAeHo45c+YgKysLkZGRCA8PZ/bFx8fDyMiIRXWVZ+vWrViwYAHjWQmgTO/KwsJCWFpaIjY2Frt378asWbME9hNCwOFwcP78eQwdOlQq+msrdCRXQ/n69SvzvywYOADw8/MT8JqjiI/WrVsjOTkZb968YdXAEULQr18/cDgcNGrUCDdu3ECnTp0wefJkgeP4IQPyxO+//w5CCOrVq8dsU1AovoS+fv0anTp1Qv369cHhcKCiooLY2FhcuHABM2fOBCEE//33H3Pe8uXL0aNHDwwbNkxmfp81FmkF5FEohoaGchv4LauUrGe3Y8cOifY1a9Ys4uzsTBo2bEjU1NQIALJr1y6SmJhILl++zBQZBUCWLVsmVF2hZILppKQkiWqVBvycl/xSO507dyb79+8n379/L/OcknlKSyY0r0nlp2QNOl1JkRqZmZnQ0dGhd65iYuDAgcw6p6TfU1dXVzx48ABLliyBk5MTdHV1MW/ePISEhCAnJwdAcXjIpUuX0LRp0zLbuXLlCtzc3OQupq8sSk5VFhYWihTvFhISAmtrawD/m7YEgIULF2LTpk2SEVqbYdXEUmodoDW2xMK0adMIALJo0SKJ97Vo0SICgPz3338S70veePv2LfM9rkxarp+/+y1btmS2mZqaksjISEnIrZXQKwxFqnC5XObHXFhYSACQ6OhotmXJFc+fPycAyB9//CHxvi5fvkwAkDFjxki8L3klIiKC/Pbbb8Tb21uk4zdv3kwAkIkTJzLb0tPT6Q2ghKDTlRRWMTIygq2tLR4/fsy2FJklMzMTampqUFZWRl5eHpP5Q5I/3cWLFwtMnSUmJjIZSijVw9DQEElJScjPz2fShK1btw7Lli1DdHQ0/vjjDxw7dow5nl6iqwf1rqSIhQMHDoDD4WDw4MGVOm/Xrl148uQJ/SGXg46ODlRUVMDhcKRi4BITE4XWht69eyex/moT8+fPR1JSEgAwBg4A/vjjDwDFycqPHj2KJk2aYPny5QAqnx+TIgg1chSxoKmpCQBYtmwZgOI70y9fvlR43ogRIwD8zxWbIkjbtm0BFIeErF27FmvXrpX4DUFp8WtOTk4S7bO2kJiYKPD848ePAIRvWgYMGIA1a9YwzzkcDuPgQ6kcdLqSIhEqk5uwW7du8PHxoaO5n3BwcICfnx/++usvrFy5Umr98j+7ffv2Ydq0afRzETP893fPnj3o3LkzWrRoAQA4d+4cAgICEB8fj40bN+LLly9o164dgoODmRRgz549Q6dOnVjTLpewshJIqfFoamqWWuzz8OHDQovrDRs2pIvspQBAZGcGcfHmzRsCgIwcOZI6P0gQW1tb5v0NCgoS+l3k5+cTQgjp0qULuXLlCiHkfx6ZHz9+ZFO63EHniCgSISsrC1OmTBHY9uDBA0yaNAnz588XqD0XHh6OiRMnsiFT5rlw4YJU+tHW1gaHw2GmJc+cOQMAuHbtmlT6r20EBQUx3387Ozu8efMGenp6zDZVVVVwOBw8efIEeXl5AIpnRZSVleHg4IAPHz6wpl3eoEaOIjX4QcIuLi4A/lcWhhCCw4cPsylNJlm6dCn+/fdfkY718fHBkCFDmOz4DRs2ZP5fu3Ztmef16dMHHA4HWVlZAjlF+amopFWstDbC4XDw+vVrAMWVCtLS0kpd+2zdujXzf0FBAQCgTZs2uHPnjnSEyjusjiMptY46deoQAKSoqIhtKXIB/n+Kqk+fPmTFihXM1BWfvLw8gWnfEydOEHNzc3L+/Hny+fNn0r9/f2bKMSUlReDcly9fEgBk9erVTB+gU5RShcfjEVdXV6Ep/IcPH5LOnTsTACQwMFDoPG1tbQKABAcHs6BavqDfZopU4ef7oxdS0fjw4YPQBfDw4cOEEEJ+++03ZltMTAwhhJCioiJiYGDAnF/y/f755uL27dtMeyWP6dq1q3RfJIXExsYy7/+WLVsqPN7Z2Zm0bduW5rwUATpdSakU48ePR4MGDap8PofDYcIMKBXTqlUrZkqX/L+X440bN2BsbIyDBw/C3d0dhBCYmpoCKJ625P8P/G9KeMGCBQD+F6qxfft2uLq6AiiuPViSbdu2Sfx1UQQp+ZmVl/uTz6pVq5Ceno6ioiJJyqoRUCNHqRTHjx/H6tWrq9XGqlWrAIAJdqWIjru7Oy5fvoyEhATcvHlTyDHk5MmTpZba4XK5sLS0BABMnjwZ8+bNg5KSErhcLp48eYKePXtixowZAIpDFyjSgx9SwI8rdXNzq/Ccbt26wcPDA58/f5aothoBq+NIityRmJhICCHk7Nmz1co72a1bNzplKQEaN25MfH19hbYfPXpUYEqyS5cuAs9VVFTIoUOH6GciZZYsWSLwOfTv35/Zx+PxiIODA1FWVi41ryUAcvr0aRbVywc0GJxSJfh3n+3atcOrV6+q3Ea9evUQFRUlTmm1GkNDQ6GsGnw2bdqEuLg4uLq6om/fviCEICcnB1paWgLH0UuCdHj9+jXat2/PPL98+TIGDhzIPOf/xviliaysrMDhcDB69GjY29tj7dq18PT0hL6+vrSlyxfs2liKvHLu3DnSo0cPkpmZWeU2rl69SgCQ0aNHM9vy8/PpYnoVefv2LbG2tq70ef/88w91BmIBRUVF5n3ftm0buXfvHmnVqpXASO3Lly9sy5R76EiOIjbCwsIwaNAg+Pv7AxBtRHDkyBEhxwc+p06dAo/HA4/Hg4aGBvT09NC5c2eoqamJVXdNYc6cOcjJycGhQ4cqfe7t27cRHR2NyZMnS0AZpTRSUlJQt25doe3z5s2Dp6cns4ZKqR7UyFHEQnx8PExMTAD8r2wIAERERFT4Yy0sLEReXh60tbUBFGeDWLx4Mfr27Yvk5GTk5eXByMgIhYWFuHv3Lho0aIDZs2czOf8oxTRv3hy7du1C165d2ZZCqSQcDgeTJk2q0g0KpXyokaOIBf76wbFjx7B161a8evWKqUzA4/EqVS6kffv2SExMRJs2bZCXlwcul4vs7Gw0atQI7du3R1xcHCIjI6GtrY21a9cKrSnVVgwMDJCQkEArOsghZ8+exciRIwEAjx49Qrdu3VhWVHOgRo5SITNmzEBycjLOnj1b6v7mzZsjICCAmZ6sV68eLl++jGbNmkFDQwMA4O/vj+bNm5d6/tu3bxEZGYmMjAxm6nLixInYuHEj9PX1UVRUhPT0dKSnpyMgIADPnj1DUVERunbtivPnz+PkyZNieZ2hoaEICAhAZmYm+vfvDz09PbG0Kw2+ffsGV1dXREREsC2FUkU+f/6M5s2bg8vlYvXq1VixYgXbkmoGbC0GUuQHlOOUUFBQQAAIuK0fP36cNG/eXOh8UR4eHh4iadq5cyeZO3cuWbJkCRk+fDhJTU2txiskZOXKlQI6Pnz4UK32pM2yZcvIyJEj2ZZBEQMlnU9GjBhR7rEjR44kysrK5NWrV1JSJ39QI1dDKSoqEpuXYnntDBs2rFQDaG5uTt68eSOwLT8/n+Tl5RFvb28ybdo0cvfuXZKTk1NlXaNHjya+vr7k6dOnBAApKCioUjtcLpeoqqoKGLlbt25VWRcbtGnThly6dIltGRQxkZ+fT1xcXAS+kzdu3CCEFP+2v3//Tjp27Ciw//Pnzyyrlk2okZNTMjMzyT///EMePnxIPn/+TBITE0leXh4hpPSRE4/HI8+ePRP7CAUAGTt2rND2nTt3km7duom1r58JCgoi48aNI/n5+cTZ2ZkkJydXqZ2SSY75j7/++kvMaiWLoaEhKSwsZFsGRQIkJCSQ3r17l/q79vLyIp8+fSIAyN69e9mWKpMoSWIKlCJ5+J6IPxMcHAxFRUUUFRUxfwEIOCMQMS/DjhgxQmibm5sbtm/fLtZ+fsbW1hYKCgrw8/PD06dPsXLlSuzatavS7aiqqsLAwABJSUnMtri4OHFKlShxcXFQUVGBkhL9OddEDA0NmbI6hBBwOBzGmevNmzfMWve0adPYlCmzUDcsGYVfC8zc3Jz5n//Izs5mjuN7FmpqamLu3LmwsbEBl8sFIYT5SwjBzp07mXMqMnKOjo4ieUO+fPkSQOm59ho2bIisrCyRXmt1mDZtGvbv3w+gONarqlhYWAg8t7GxqZYuaXLs2DG0atWKbRkUKcD/XSooKIDD4QhkTKHJmkuHGjkpweVy8fbtW/z48UPAyGRlZSE1NZUxYFpaWkhISGD2x8TEAACcnZ0xduxYqKmpQVVVFfHx8SgoKEBmZiYIIcjKyio3e/zs2bOZ/ysyYBs2bMDMmTMrfE2DBw9mwgR+Rlpu7E5OTsjPz8e8efPw48cPcLncKrXzs5H7/v27OORJhVu3bmHo0KFsy6CwwOTJk6GlpQULCwvs3buXbTmyCXszpbUHlJhDV1JSqtDD8ObNm8z/VV1nKovs7GyB57t37ybu7u7k4MGDlW4LANm/f3+Z+01MTEhubm6l260siYmJpEuXLmT+/Pnk33//rVIbnp6eAp/BkCFDxKxSchgZGUnlfabILk+fPiVjxoxhW4ZMQkdyUobL5aJjx47M827duuHMmTMCNcPc3NyY/+vUqVNqO4QQfP36Fbm5uYiKikL9+vWFpjU5HA60tbWZ/21sbLBnzx6BdmbNmoXr169XuuzNmDFjAABTpkwp85i6devC19e3Uu1WBQMDA/Tq1Qs2Njbw8fHBvHnzEBwcXKk25HUkl5GRAQUFBZrqrJZDCMGPHz/YliGTUCNXBQghcHR0RI8ePWBnZ8fMj5f2AIqnJJOTk5GdnQ1CCJ4/fw4ej4eJEyfC0NAQq1evhoGBAXbt2oUNGzbg2rVrePDggdDU2+3btzF06FBwOBwoKCigadOm0NDQgIWFBX78+IGZM2fi48ePyMnJYYwkfzozKSkJzZs3x8KFCwXaXLNmDQYPHlxpR4uTJ0/Cycmp3GPq16+P9+/fV6rdqrJ48WJcuXIF58+fR/369TF16tRKGe6fU4/JS1D1qVOnYG9vz7YMCss4OzuXuXRQ26EZTyqJmZkZ4uLiynTeGDlyJNq1awdbW1s0bNgQTZo0Edh/7do1eHh4MM8nTJiA1q1b48OHD8jPz4eWlhbq1KmDffv2IS0tDQDQoEEDgYtuixYt4OvrW+l1r5UrV2L16tVi8a7kcDjgcrlQVFQs85g5c+agsLBQamsFBQUFcHFxYUr/GBsbIygoqMzRcEl+LnsCADk5OVBXV5eIVnHRu3dvuLm5wdPTk20pFJZZsGABDAwM8Pvvv0NZWZltObKD9GdI5RuUsoYmKk2aNCEASNOmTUU6/sWLF2Tjxo1k8eLFJCAggBBCyIgRI6ocw5WVlSWWcio8Hk+kdvbv30969uxZ7f4qQ0FBAenZsydZvnw5AUA2bdpU4Tk8Ho+MGjVK6HONiIiQguLqYWJiIvZ1W4p8wuPxyNChQ8mzZ8/YliJT0OlKESH/P/qxs7MT2P7XX3+JdD6Hw8HXr1+xY8cOkUvWd+jQAYsWLcKGDRuYKSl1dXXk5OSILrwE/OmM5OTkKp1fWRwdHaW+TqCsrAwvLy8mvGHRokUVnrN161acPn1aYJuBgQGMjY0lolFcFBQUoKioSKSRKqXmw+Fw4ODgUO7sSm2EGrmfOHToEDgcDlRUVDBo0CBmbY2/7vbmzRsQQrBjxw7Mnz8ff/75Z4Vt8tfmkpOTMWfOnGrpU1dXR25ubrXaMDAwYF5XXl5epc/nv5709PRyj2vRogVSU1OrpLE6GBgYoGHDhhgyZAhUVFTKPfb06dNC65QKCgo4evSozDtznD9/Ho0bN2ZbBkWGKCwspFOVP0GN3E/ExsYCKP6yGBsbY+nSpRgwYADq1asHoDjTSGpqKmxtbbF169YKY874mexTUlLEcsddXSNHCEFBQQHz3MvLq8pt8evHlYWSkpLYs6uIytChQ8HlclFQUICjR4+WeszDhw8xfvx4oe179+5F//79Jayw+ly8eLHUQHxK7UVNTQ0pKSlsy5At2J0tlU34yXpzcnKY9Zn9+/eTc+fOES6XS9atWyfSmhT/3N27d4tN2/Lly8no0aOr3Q5/Xa2017Fv3z6BtamSFQb4vHnzRqT3wMjIiBQVFVVbb2UpKCggXbt2JSoqKqXq9PX1Jdra2kLrcH/++afUtVaVevXqkaioKLZlUGSI3r17k9jYWLZlyBR0JFcK/Ck2fi00oHiKbtiwYVBUVMTSpUsrHKFcvXoVAJCXlydS9hBR0dDQqNIU489wOBwYGBigUaNGAtvfvXuHadOmwdTUlHmNpaWMatu2LQBg6dKl5fajq6tb6Zg1caCsrIzZs2dDW1sbpqam8PHxYfZFRkaib9++yMzMFDhn4sSJIq+xsg2Px0NeXh4zw0ChAMD48eNx4MABtmXIFNTIlYK6ujoIIQLTXJWdauS795dM+isOxGXkAGDz5s0ICwsTmHLlGy9+OjEejwegbAeODRs2lNuHubk53rx5Iw65lWbw4ME4d+4cYmNjMWvWLAwdOhShoaEYNmyYUFygm5sb9u/fX6kK5mxy584d1K9fn20ZFBmjd+/erP3eZBVq5Mph/PjxTFD1kCFDRDonOTkZ4eHhcHd3B1BcJfvLly9i06SpqYn8/HyxtFVyParkyLTkmh+Hw8H27duxefNmKCoqIiQkhNnHv2Msb1RrZWWFT58+iUVvVejevTvWrVuHz58/Q1dXFwMHDsTbt28Fjmnbti3OnTsnV1n8T58+DVdXV7ZlUGQMfX19aGho4Pr162xLkRmokRMz/ClADoeDhg0bAgATnCwOxGnkAODDhw8AijOY8PnZq9DT0xMvXrwAj8eDtbU1OBwOBg0axKT0Ks/Dz87ODl+/fhWb3qqwdOlSFBYWwsrKSiikQVVVFaNHjxbb6FhavHjxolSnGQpl3759OHbsGIKCgtiWIhNQIydmjIyMmP/Dw8Nx8OBBjBs3Tmzti9vIOTg4ACg2bOWNyDp06ABCCLKzszFr1iyBVFJhYWFlpu9q3bq1TOSB5HA4WLZsGQYMGCCwPT8/H3PnzsXw4cPB4XDw66+/4saNG3j37h38/f1Z8w6tiMzMTNja2rItgyKD1KlTB25ubtTI8WHL46WmgkpmQaksDx8+JA4ODmJrLywsjAAgWVlZ5O3bt5XK5lLSQ/P8+fOlHpOZmUlMTU3Fpre6nDlzRuD1aWhokK5duxJ1dXXStGlTJisN/8Gvti5LPHv2jNja2rItgyLDvHjxgnTo0IEUFBSwLYV15GcRQg7gj7D8/f0l1oe2trZAnFtV4HA40NXVZYK5VVVVoampCRsbG3h4eCA+Ph5ubm7lBroTQgRyZ5a1ZqmlpSVTxRz5jjV8cnJyMH/+fGRlZcHGxkYuio/+999/6Nq1K9syKDJMhw4dYGpqirS0NBgaGrIth1WokRMjX79+RZ06dZhy9JKgqkVBf+bvv/+GlZWVQDCxlpYWrly5wjyvyMjxOXz4sNx4JTZq1AgODg74+PEjs+3SpUtlBozLIs+fP8euXbvYlkGRcTQ1NeXmdylJ6JqcGNm2bZvQSEHcPHr0CDY2NtVuZ8aMGdXKlqGgoMB4nk6cOLHcY9XV1WViXY7PsGHDBJ5fuXKl2qNjaaKhoYGMjAy2ZVBknNatWwt5EtdGqJETI0ePHq3wgl9dXrx4AWdn52q3o6SkBB6PV+bFkojR4cLMzEymYnd+NnJpaWm4f/8+S2oqj5WVFeMVS6GURd++fYUSj9dGqJETMz9fQMVNcHAw+vXrV602QkNDAQCKiorQ1dUtNdv+z5Wyq0PDhg3h5+cntvaqi7W1NeNVyufcuXPsiKkCzZs3R0BAANsyKDJO06ZNoaioiPDwcLalsAo1cmJGnCOg0sjOzoa1tXW12mjUqBHy8/NRVFSEN2/eICEhAcuWLRM45sePH3j8+HG1+uFja2srcnkhafHzzcjVq1fFGpohSRwdHREWFsa2DIoc4OLiUutH/dTIiQm+catste7KIq6FZBUVFSgoKKBt27b45ZdfsH79embf6NGjAQBdunQRS18ODg4yd1EePny4wPP09HQ8ePCAJTWVo0OHDkJpySiU0uBwOHj+/DnbMliFGjkxsXfvXrYlVBn+vD2/xtzp06dx6NAhsbXfs2dPxMXFVZjMWZo0btxYKFxAXqYsZS0sgyK7jBs3Dp8/f652DUp5hoYQiIlr165JpR9JTYfyeDycOHECUVFR6NChA3r06CG2ttXU1PDt2zc0b94cSUlJOHjwoNjarg7Dhw+Hr68v8/zKlSvIz8+Hqqoqi6pEh8fjSXzmgCLfcDgcuLi44NmzZ7U21yn9hYgJfX19tiVUCw6Hg3HjxmH58uViNXB8NDQ0EBwcjFevXomc7PpneDwe5s2bBz09Pdy+fbvamn5el8vIyJCbKUsdHR2xJv6m1Fzi4+Pl5sZNElAjJyb4Fbb5U37dunWDnZ0dJk+ezGzbsWNHmTkeRUVVVVVu12NUVFTg5+eH2NhYdOvWjSnjIwr+/v5o0KABPn78iNu3b2P06NG4detWtfRYWVkJTVnevHmzWm1KCwsLC7Em/qbUXH755Re5uXmTBNTIiRF+cHRKSgpOnDiBc+fOYfbs2Th79ixTbNXR0RHR0dFV7sPc3BzPnj0To2rpoqCggBcvXkBDQwNt2rSpMIMLj8fDlClT0K1bN/zzzz/w9vZGhw4d4OPjwyRTrg4/J2y+ffu2zCZlLkmTJk0EploplLJo2bIlLl26hPnz57MthRWokZMA+vr6qF+/Ppo1a4aWLVti+PDhaNiwITZv3ozly5fD3Ny8ym03adKkRmQxuHnzJpo3bw47O7syy9y8ffsW9evXR2RkJKKjozF06FBmX4sWLfDkyROMHTu2yuuhsbGxuHv3rsC2iIgIuZgGdHBwkAudFPbhz6A8efKEbSmsQI2clLCxscHMmTPx999/V6udli1b1pgSGidOnED//v3RpEkTpKWlMdt5PB5+/fVXplr33bt3hWrcAUCzZs3w9OlTjB8/HpcvX65U39euXYOdnV2pU378YHlZpl27doiMjGRbBkVOuHfvHgYPHsy2DFagRk5KqKmpicWNt0OHDjUqg8E///yDKVOmwMbGBjExMfDx8YGZmRmys7MRHR2N/v37l3u+vb09nj9/jkmTJuHixYsV9sfj8fDXX3/Bw8NDwLDy0dHRQbt27ar6cqRGixYtkJqayrYMihyQk5ODTZs2YcKECWxLYQUaQiAlzMzMEBMTAysrq2q106ZNGyQlJYlJlWywfPly1K1bFzY2NlBXV4eXlxe6d+8u8vm2trZ4+fIlOnbsiKKiIqFAbz4ZGRn49ddfcf36daF9HA4HHh4eWL9+PQwMDKr8WqQFDR2giMqbN2/g6uoKU1NTtqWwAjVyUsLc3FwsRk5FRUUuHCMqy/Tp09GnTx/Ur18fSkqV/1o2bdoUr169Qvv27UEIwYgRIwT2p6amomvXrqXW+uvXrx+2bt2Kpk2bVlk/GygoKCAvL6/UqVwKBQAePnyI1atXy0xsKhvQ20EpoaWlJbY1FP7FrabRsGHDKhk4PtbW1njz5g1mzJiBU6dOMdt5PB7GjBkjZOA4HA5Wr16Na9euyZ2BAwBDQ0OZqu5AkT3evHmDVatWoUmTJmxLYQ1q5KREo0aNEBwcLJa26MWtbKysrPDu3Tt4enri5MmTAIDNmzcLxb/p6uri2rVrWLFihdxO/TVq1AivX79mWwZFhvHz80OLFi3YlsEqdLpSSlhYWIgt3yD/4iauBMo1jYYNG+L9+/do06YNgoODsXHjRoH9+vr6eP78OWxtbVlSKB5sbW1pyR1KuTRr1gwvX76sdnkueYYaOSlx//79ak3FlcTe3l6m6rPJIpaWlrh79y7atm0rtIZ5/PhxuTdwQLETkrjKIVFqJp6enujYsWOtNnLyOU8jZ+Tn58PLywuJiYliac/R0RHfvn0TS1s1FS6XiyVLlggZuIULF8Ld3Z0lVeKlZ8+eNSqchCJ+tLW1a61XJR9q5KTAxYsXMW7cOOzfv18s7Tk7OyMmJkYsbdVE+KnAfs7XZ2VlhbVr17KkSvzo6emBx+PVSCckinhIS0uDhoYG2zJYhRo5KXDt2jWxrp8ZGBigoKBAbO3VJAghmDt3Lo4ePSqwvU6dOkhNTcWRI0dYUiYZmjZtyiQHp1B+xt/fH05OTmzLYBVq5KTA48ePYWNjI/Z2K5PFv7awYsUK7Nq1S2CbkpISzp8/j0+fPmHx4sVISEhgSZ34cXd3x4ULF9iWQZFREhMToaWlxbYMVqFGTgosXrwYS5YsEWubWlpacpFjUZps3rxZaDqSw+Hg5MmT6N69O8zMzNCvXz9s2LCBJYXiZ8KECbQaAaVMLl26VGtzVvKhRk4K/PLLL0hOThZrm5aWlnJdckfc7Nu3D4sWLRLafvDgQYHsJ8uXL69RIx8DAwNwuVw6fU0RIioqCgkJCahXrx7bUliFGjkpEBcXJ3YPJxsbG3oH///s3r0b06dPF9q+bds2TJo0SWCbra0tCgsL5bbwbGnY2tri8OHDbMugyBCfP3/GyJEjsXfvXralsA41clJATU0N+fn5Ym2zZcuWYsugIq8QQrBmzRrMnj1baN+qVaswd+7cUs/r1atXjfKy/PPPP7Fjxw62ZVBkhLS0NEybNg1eXl5o3Lgx23JYhxo5KWBgYCD2ygEhISEwMzMTa5vyBCEECxcuxIoVK4T2/f7776Vu57N8+XJcuXJFguqkS48ePZCamlqjRqeUqpGYmAgPDw9s3LgRFhYWbMuRCaiRkwL6+vpir/11//59/Prrr2JtU14oKirClClTsHXrVqF9ixcvxubNm8HhcMo839raGkVFRfjx44ckZUqVQYMGYenSpWzLoLAEl8vFzZs3MWzYMOzYsUMuaiJKC2rkpICCgoLYy+PExMSgR48eYm1THigoKMAvv/yCQ4cOCe3bsGEDNmzYUK6B4+Pm5lajpizXrFkjlISaUjv49u0bPDw88OHDB1y6dAkODg5sS5IpqJGTEuJK6QUA4eHhUFdXl9vs+VUlOzsbHh4eOH/+vMB2DoeDvXv3YvHixSK39ccff5RaPFVeMTAwgKGhIW7cuMG2FIoUefPmDcaNG4ctW7ZgxYoVqFOnDtuSZI7adZVkiW/fvok1IfDRo0dr3XREcnIyevTogTt37ghsV1JSwsmTJzFt2rRKtdewYUMQQsRW408WWLx4MVatWsW2DIoUmT9/Pk6ePFkjEo5LCmrkpMCFCxfEun52584docrXNZnv37+jc+fOQrXTVFVVcfnyZYwaNapK7bq7u+Pvv/8Wh0SZ4Ndff0VYWBiys7PZlkKREhoaGmjYsCHbMmQaauSkwJMnT8SauzIiIgIeHh5ia0+WCQwMRMeOHfH582eB7dra2rhz5w769+9f5baXL1+OW7duVVeizKCgoAAXFxcsX76cbSkUKWFlZYWvX7+yLUOmoUZOwoSHh8Pc3FxsteTi4uKgoqIitvZkmefPn6Nz586Ijo4W2G5sbIzHjx+ja9eu1Wq/Xr16UFRUrDFli3g8Hr5+/Yr9+/fTuLlagpubm1C1DYog1MhJmBUrVmDGjBlia+/EiRNo1aqV2NqTVa5fv46ePXsiLS1NYLuVlRWeP38utvdg2LBhcHZ2Rv/+/bF48WJ8/PhRLO1KGx6PBycnJ9jY2CAsLAzbt2/HoEGDaBLvGk6bNm3w8OFDtmXINNTISZCzZ8+icePGaN26tdjavH79OoYOHSq29mSRffv2YeDAgUJ10lq1aoXnz5/DyspKbH1t2bIFV69eRc+ePREXF4fu3bsjKytLbO1LAx6Ph06dOsHMzAwXLlyAiYkJQkNDkZ+fD1dXV7blUSSImZkZXYOtAA4RdwAXBUCxN+CIESNw69YtqKioiK1dY2NjREZGQk1NTWxtygqFhYWYN28e/v33X6F93bt3x+XLl6GjoyNRDXPmzMGXL19w9+5difYjTlxcXKCuri7kecrlcmFubo74+HiWlFEkzenTp/HlyxfqVVsO1MhJiAkTJmD69OliL1hobGxcIy9aKSkpGDZsGB49eiS0b9iwYfjvv/+gqqoqcR08Hg/m5uY4e/asWJ2FJEXPnj3B4/FKfd+Amvt9oQCZmZnw8PDAvXv3asUafVWh05US4NatWzAwMKj1FXlFJSgoCE5OTqVeqD09PeHl5SUVAwcUeygeO3YMo0aNkvn1LDc3N+Tl5ZW7JqOkpISUlBQpqqJIi8zMTGhrayM9PV3sGZVqEtTIiZmYmBj8888/dPpARG7evIn27dsLFYBVUlLCvn37sH37digqKkpVU+/evWFlZYU//vhDqv1WhoEDByIlJQVPnjwpN42Zubk5nj59KkVlFGlhZmaGQYMGYfr06ejfvz/69u2LzZs3sy1L5qBGTsyEh4ejQ4cO0NDQYFuKTMPj8bBx40a4u7sjMzNTYF/dunXx4MEDTJ06lSV1wNWrV7F//36ZzOw/fPhwREVF4cWLFxWmdrOxsREKoqfUHMaPH49z587h5s2buH37NkJCQmhtwZ+gRk7MXL58Gb169ZJY+woKCnJfBTohIQHu7u5YsmSJ0DRLs2bN8PbtW7i4uLCkrhg9PT3Mnz8fAwYMYFXHz/z666/48uUL3r59K1Lu0tatW8Pf318KyiiywN69e/H06VPs3LmTTmH+P9TIiZH8/HwEBgbC2dlZYn2oqqoiISFBYu1LmgcPHqBly5alZhrx8PDAixcvZCZN0fLly5GQkIDLly+zLQVAsTOTr68vPnz4IHJy7k6dOiEsLEzCyiiygoKCAo4cOYL09HT07dsXhw8flvub4upCjZwYiY6OhrW1tUT70NDQkEsjV1hYiGXLlqFXr16lTgEuW7YMly5dgra2NgvqyubChQuYOnUqioqKWNUxffp0vHz5En5+fpVao2zVqhWSk5MlqIwiaygoKGDFihU4c+YMuFwuXF1d8f37d7ZlsQY1cmIkNjZW4tW6NTQ0xFq2RxqEh4fD2dkZ69evF5pCqVOnDq5evYq1a9fKZOkgR0dHdOzYEVOmTGFNw5w5c/DgwQP4+/tX2lWcupbXXvT09DB16lTs3bsXv/zyC8LDw9mWxAqyd1WRY+Li4mBsbCzRPrS0tJCUlCTRPsTJxYsX4eDgUKrzg4uLC/z8/GRu3etnzpw5gytXrrCS43LhwoW4ceMGAgICqpxUQFlZWSg9GqX2YGdnh+PHj2P8+PFyd4MsDqiREyPx8fEwMTGRaB9aWlpyE/d06NAhDB06FBkZGQLbFRQUsGrVKjx8+BD16tVjSZ3oqKmpYf369VKv/LBs2TKcO3cOgYGB1YoTNDMzw5MnT8SojCJvNG7cGFu3bsXkyZORm5vLthypQo2cGImLi5O4kdPR0ZELI3f69OlSp/gsLCzw+PFj/Pnnn1KPf6sOU6ZMAYfDwT///COV/latWoX//vsPQUFBUFdXr1ZbNjY2ePXqlZiUUeQVR0dHeHh4YPXq1cjPz2dbjtSgRk6MREdHS3xNTldXV+annq5evYqxY8cKrb8NHjwYHz9+ROfOnVlSVj0ePnyINWvWSDzubP369Thw4AACAwOhqalZ7fYcHBzw6dMnMSijyDtjxoyBuro6Jk6cyLYUqUGNnBhJTEyEoaGhRPvQ09OTaSN3//59DB8+XMgb0dPTExcuXIC+vj5LyqqPiYkJLly4ADc3N4mNprdu3YqdO3ciMDBQbMmonZ2dhTLKUGonysrK+PPPP2FsbIzffvutVnjeUiMnRng8nsQ9BBs2bIg3b95ItI+q8vz5cwwcOFAoLmfy5MnYtm1buemn5IXu3btjwYIFaNeundhzW+7atQubN29GYGAg9PT0xNZuq1at5DLshCI5/vnnH4wfPx6DBg3C6tWrhdbNaxLUyIkJaWUXmDJlCoyNjWWuTtiHDx/g5uaGnJwcge0jR47Evn37aoSB47N06VI0bdoUQ4YMEVub+/fvx5o1a+Dv7486deqIrV2gOIzA0NAQPj4+Ym2XIt906tQJPj4+sLOzw7Bhw7B27VpwuVy2ZYkdWmqnDPbt24fr168jNzcXbdq0QWZmJjQ1NaGmpobhw4ejZcuWjGHjcDhIS0vDzJkzcerUKYlr4/F4cHZ2hr6+Pm7cuCHx/ioiKCgIXbp0EZr6cHd3x8WLF6GsrMySMsnB4/FgY2ODZs2a4dy5c9WKRzt69ChTlVxSa7rr1q3D8+fPcfPmTYm0T5F/jhw5gkuXLuHatWsyGbNaVaiR+4mioiIsWLAA6enpmDx5MhwdHfHy5Uvo6ekhJycHPB4P8+fPh62tLb5+/QqgOGGuvr4+vn37hjVr1khFJ4/HQ7t27WBhYYGLFy9Kpc/SCA8PR+fOnRETEyOwvXv37rh582aNLO7Kh8fjYeTIkXj27Blu3boFBweHSrdx6tQpeHp64sOHD7CwsBC/yP8nJycHlpaWtTJOiiI6/KxEXbt2ZVuK+CAUAXJyckjDhg1JUlJSmcfweDwSFRVF8vLySEFBATl+/Dj57bffyj1HEhQVFREHBwcyatQoqfbLJzo6mjRq1IgAEHh06NCBZGZmsqKJDa5cuUL09fWJm5sbefbsWYXHR0VFkXnz5hErKytiampKwsLCpKCSEGtra/LixQup9EWRTxISEoi7uzu5desW21LERs0Zk4oJdXV1ODk5lbuGxOFwUK9ePaiqqkJZWRljx47FgQMHULduXSkqLQ6qfv/+Pfz8/KTuEpycnIxevXoJJf91cHDArVu3oKWlJVU9bOLh4YEfP36gQ4cOmDBhAoyMjDBo0CA8evQIoaGhSEpKgo+PD4YMGQJTU1O0b98eSUlJOHfuHGJiYqSWkHrUqFHYtGmTVPqiyCe6urrg8XhiXxdmEzpdWQpOTk548uSJ3Ey1cblcNGvWDBYWFrhw4YLYXM/LIj09HT169MD79+8Ftjdt2hRPnz6VeBiFrJORkYHNmzfj6tWryMnJQUFBAbS1tTFkyBDMmTMHBgYGrOlq3Lgx9bSklMmHDx9w9uxZbNy4kW0pYoMauVK4d+8edu/ejdOnT8vNiITH42HevHn477//MHPmTKxatUoii8fp6emlurdbWFjg2bNnqF+/vtj7pIiPRo0a4dy5c3B0dGRbCkUGWbduHdq2bStz3tvVgU5XliAoKAivXr3Cxo0b4e3tjT///JNtSSKjoKCAHTt24OvXr3j69Cnq1asnEc/L0tzmjY2N8eDBA2rg5IARI0bUqLt0injp1q0bbt++XaMKrlIjV4IHDx7Aw8MDffr0QXJystTyFIoTAwMD+Pj44Pz585g1axZat26NyMhIsbVfWhzNzZs3JV5HjyIeFi5cSJM1U8qE748gj9e+sqBGrgSTJk1CmzZtsHDhwiqXNZEVOnXqhIiICIwbNw7NmzdHYGCgWNpdtWqV0LZmzZqJpW2K5KlTpw7U1dUREBDAthSKDKKoqAhra2u5Sp5eEdTIlUBTUxOEkBo1VPf09ISlpSWysrLE0l7Tpk2FttXmqsPyyODBg7F+/Xq2ZVBkEB6PBy8vL8yaNYttKWKDGrkSEEKgoKBQo1JQAUBmZqbY1suMjY2FvE5ra8VheWXJkiW4cuUKbt26xbYUioyxfv16jB49ukZVlKdGrgQFBQWIiYlBcHAw21LESl5entjq3HE4HFhaWgpsi4iIEEvbFOlgZGQEZWVlzJs3D/Xq1cOuXbvEnmyaIl/k5uZi6dKl8Pf3x+TJk9mWI1aokSuBqqoqrly5giVLluDgwYMoLCws9/i4uDiZLntTEnGGE/wcvEyNnPyhpqaGL1++4Pr16/Dy8oKRkRHmz5+PvLw8tqVRWODIkSNo3LgxTp06VaPyVgLUyAlhaWmJS5cuIT8/H0OHDsWAAQPg7u6OevXqCQTRFhQUoG/fvli0aBGLatmhQYMGAs+pkZM/lJSUkJGRgVatWuHFixf49OkTIiIiYGpqiqFDhyIuLo5tiRQpQgiBvr5+jZqm5EONXCkoKipi1qxZuHr1Kq5du4br169j6dKlmDNnDn755RfcunULf/zxB2bPno3v37/XKEcVUaBGTv7R1dXFly9fmOempqa4dOkSYmNjUa9ePTRr1gydOnWCv78/iyop0uLu3bvo2bMn2zIkAjVyIjJz5kycOXMGmzdvRnBwMBo0aIAJEybA0tISnz9/ZltemfB4PLEbYWrk5J+6desiJCREaLuamhq2b9+OhIQEDB8+HG5ubrCxscG1a9dYUEmRFn379sWJEyfYliERaFqvahIREYHff/+d1XI35ZGUlITWrVuL1c3/9evXaN++vcC23Nxcucn1SQGGDRuGVq1aYdmyZRUee+PGDSxcuBAZGRlYuHAh5syZU+PWbWo7hBD079+/RtYbpN/UatKgQQNoampi+vTpMjltGRERIfb8mz+P5AAaKydvmJmZISoqSqRj+/fvj+DgYNy+fRsXLlyAkZERPD09qZNKDYLD4aCwsLBGVganRk4MHD9+HIQQvH37lm0pQkRFRUFXV1esbRoZGUFdXV1gG52ylC8sLS0RGxtbqXNatGiBZ8+eISgoCDExMTAzM8PgwYOFCuZS5BNXV1fcvn2bbRlihxo5McDhcDB8+HA8fvyYbSlCxMbGllo1oDrQWDn5p2HDhlUuuWNkZITz588jLi4ODRs2RIsWLdChQwd8+PBBzCop0qRr167YsmULioqK2JYiVqiRExPKysoyOdSPjY2VSDFXGisn3zRp0gTJycnVakNFRQVbt25FQkICxowZAw8PDzRp0gRXr14Vk0qKNGnbti26dOmCu3fvsi1FrFAjJyZ2794NNzc3tmUIER8fDyMjI7G3Sz0s5Rtra2tkZmaKpS0FBQXMmDEDUVFR2LFjB5YuXQozMzNs3bqVZlKRMz5//lyjqoID1MiJBX9/f2hra6Nly5ZsSxEiOTlZKkaO5q+UL1RUVCRigPr27YugoCDcv38f165dg6GhIWbOnImcnByx90URP1u2bMG8efPw119/ISwsTCZnpyoLNXJiYNy4cRg7dizbMkolJSUFZmZmYm+XGjlKedjb2+Px48f48uULUlJSUK9ePQwcOJA6qcg4lpaWuHr1Knr27In169djwIABGDJkCKKjo9mWVmWokasmkZGRaNy4Mbp06cK2lFJJS0uDubm52Nv92cjFx8cjNzdX7P1QJAOXy5VKtQ0DAwN4eXkhLi4OTZs2hYODA9q1a4d3795JvG9K1TAyMkLnzp1x8OBB3Lp1C8uXL8esWbNkMkRKFKiRqyZfv36Fk5MT2zLKJDMzExYWFmJvt7RYOXFWIKdIlujoaGhoaEitPxUVFWzcuBEJCQmYOHEievbsiYcPH0qtf0rVadWqlVzHRFIjV03s7OxkMj6OT3Z2tthqyZXE0NCQxsrJMZGRkdDW1mal76lTp6J///7w8fFhpX9K5UhPTweHw5HbOpvUyFUTc3NzaGtry+wPlsfjSSTdFofDoR6WckxUVJTY4ycrg729PYKCgljrnyI6U6dOxcqVK9mWUWWokasG4eHhiImJwZgxY/DXX3+xLUfqNG7cWOA5zVgvP8TExEBfX5+1/h0dHelNkZwwcOBAnDlzhm0ZVYYauSpy9+5djBs3DmPGjMHNmzfx33//sS1J6rRq1UrguSxP21IESUhIgIGBAWv9t2vXjtaskxMGDx4s16PumlchT0ocPnwYx48fF8r8UZto27atwHM/Pz/k5+dDVVWVJUUUUUlISEC9evVY619HR6dGxGDVBlavXo3ffvuNbRlVho7kKgmPx8OFCxdgbm4u8wYuJycHioqKEmv/ZyNXWFhIpyzlhKSkJJiamrItgyIH+Pn5YeDAgWzLqDLUyFWC5cuXw9XVFU+fPsXq1avZllMhkZGRYi+zUxJjY2Oh8IQ3b95IrD+K+EhNTZVI/GRl0NDQoGEncoClpaVcr59SIyciy5YtQ3JyMu7fv48dO3aw5n5dGb5//y5xnT+P5ui6nHyQnp7O6nQlUOyZLKvFhin/o0OHDjh8+DBSU1PZllIlqJETEV9fX+zevVuuKiLHxMRI3E2cGjn5JDMzU6hckrTZunUrdu7ciebNm9Npbhlm1KhRsLa2xtixY7F792625VQa6nhSCh8/fkRYWBh0dXWhrq6Ojx8/om7duhJd35IEMTExEs8o/rORCw4ORmZmplyMdGszBQUFrHpXAsUelhERETh69Ch69eoFa2treHl5sT7CpAjC4XAwceJETJgwASNHjoSzs7NMJqMvC/kZlkiRJUuWICkpCZ8+fYKPjw9UVVVx7NgxtmVVmvj4eIlfyNq0aSPwnBCC9+/fS7RPSvXhcDgyMysxYcIExMTEoE+fPmjZsiUGDhyIjIwMtmVRfoLD4cDe3h5+fn5sS6kUsvEtlyFevnyJZ8+eYcqUKZg7dy6WLVuGSZMmQUlJ/ga9SUlJMDY2lmgfurq6sLGxEdhGk+/KPrKWbFdBQQF//PEH4uPjUb9+fVhaWmLKlCkoKChgWxqlBJGRkWjXrh3bMioFNXL/T2JiIv777z9s27YNu3btYluOWEhOTpaKm/jPU5aS9rA8fPhwjfmMKIIoKSlh165diIqKQmpqKkxMTPDXX3/R4qsyQlhYmFQTe4sDauT+nz///BNfvnzB0qVLMWHCBLbliIXU1FRWjJyknE9yc3MxadIkTJ48GfPmzcPTp08l0k9NRx4MhpaWFs6fP4+AgAA8fvwYpqamOHDgANuyajU+Pj5o2LChRBK+SxJq5P4fHx8fDB06FA4ODmxLERuSKrPzMz+XGoqIiIC3t7dY+wgLC0PHjh1x5MgRAEBRURFGjBhBU0NVgYSEBIkk7ZYEZmZm8Pb2xqNHj7Bnzx7Ur18f165dY1tWrWT79u1yOQCgRu7/+fPPP3Hy5En069cPHz58YFuOWCgoKMCRI0ckvq7RsmVLqKioCGwbNGiQ2BxQrl+/jjZt2uDjx48C22NjY+Hl5SWWPmoTbJbZqSr29vb4+PEj/vvvP8ybNw82NjZ4/fo127JqDYQQFBUVyWeWHEIRIDExkbi4uJDHjx+zLaXaREZGEg8PD6Kvr088PDxIVFSUxPqaMWMGASDw0NfXJx8/fqxym1wulyxbtkyoXQBEVVWVHDp0SIyvoPZw/vx50qlTJ7ZlVIszZ84QU1NT0rZtWxISEsK2nBoPl8sl7dq1Y1tGlaAjuZ8wMDDAzZs3sX37dpw7d45tOdXCwsICV65cQUxMDKytrdGyZUts375dIn1t2bIFLi4uAttSU1PRs2dPBAYGVrq9hIQE9O7dG+vWrRPa16hRI7x8+RKTJk2qst7aTHR0NKu15MTBiBEjEBMTg5EjR6Jdu3bo27cvkpKS2JZVY1FUVETbtm1x69YttqVUGmrkSkFTUxNnz56Fp6cnDh8+zLacaqOmpobNmzfj1atX2LRpk0T6UFdXx40bN9CpUyeB7UlJSejRowe+fv0qclsvX75E69at8fDhQ6F97u7uePfunVCZH4roxMXFsR4ILi7mz5+PhIQE2NnZMVk58vLy2JZVI1FXV0d+fj7bMioNNXJloKysjPv37yM4OJhtKWLD2toaurq6Ersb09LSwq1bt4QcUeLj49G3b1+kpaWVez4hBLt27UKXLl0QHR0tsE9BQQHr1q3DlStXWC32WRNITEyEkZER2zLEhoKCArZu3Yro6GjweDyYmppi0aJFcuFFKk+8ePFCLqsRUCNXBufOncPo0aOFpuDknb///huLFy+WWPs6Ojq4c+eO0EgrLCwM48aNKzMIOSsrC6NGjcKcOXOE6owZGhri3r17WLp0qcxk6ZBnkpKSYGJiwrYMsaOhoYGTJ0/i27dv8PPzg5GRkcSm52sbfn5+aNy4MTgcDttSKg/bi4KyyvDhw0laWhrbMiSCkZERiYiIkGgfSUlJxN7eXshhZMOGDULHBgcHE1tb21IdTDp06CBRh5naSPfu3cmxY8fYliFxvn79SoyNjcmlS5fYliLXREREkGbNmpFbt26xLaVKyM1t8Y8fP/Dp0yc8efJE7CmJsrOzUVhYyDz/+PEjPn36BF1dXbH2IytMnDgRc+bMkWgfdevWxdWrV4Xew2XLlsHHx4d5fu7cObRt27bUaeE5c+bAx8eHJuwVM3l5eRKtMygrWFtbo3v37nKXa1HW8PT0xOrVq9G3b1+2pVQJuUjI+O3bNzRp0gSurq6IjY2Fk5MTDh06VK2hM4/Hw8ePH3H69Gn4+vri2bNniIuLw5EjR3Dz5s1SnR5qCqtWrYKJiQkKCgqE4tvEiZWVFU6cOAEPDw9mG4/Hw8iRI/HmzRts27at1OkkTU1NHDp0CCNHjpSYttpMfn4+dHR02JYhFRo0aIDw8HC2ZcglPB4PFy9eRFFREQYNGsS2nCojFyM5KysrBAYGIjc3F+/fvxcIcM7PzxfZ46egoABeXl6YPn06+vXrh0OHDmHQoEF48OABrly5gjZt2iA5ORl3796Vz6BHEVFRUYGFhYVU0mINGDAAixYtEtgWHx8PS0vLUg2cjY0N3rx5Qw2cBKlNRs7a2ho/fvxgW4ZcMmXKFBw8eBCXLl1iW0r1YHu+tDyePHlC6tSpQ8LDwwkhhCxfvpwsXryY3Lp1i3Tr1o306tWLtG7dmjg5OZGAgADmvBUrVpDhw4eTsLAwkpWVRQgh5Pjx46R58+ZEU1OTPHnypNT+ioqKJP6aZIURI0aQv/76Syp9FRYWEhcXl1LX3Eo+hg8fTjIyMqSiqTZjbW1NgoKC2JYhFV69ekXs7e3ZliF3JCQkEADk4cOHbEupNjI1XZmVlYXs7GwYGBhg9uzZ8PHxwf79+zF9+nTo6enBxsYG586dw4YNG5j5YUIIHj58iBkzZuCXX36Bm5sbYmJiEBcXh/bt28PMzAzOzs74+PEj7ty5AzMzszL7r02ee61bt8bLly+l0peSkhLOnDmDVq1alZprUklJCVu2bMGcOXPk03tLztDV1a01YRjNmjWDsrIy2zLkDkIIhg8fju7du7MtpdrIjJHj8XhwdXWFhoYGFBQUMHjwYKxduxb6+voYOnQoMjMz4evrK/SF5XA46Nq1KwoKClBYWIgBAwZg48aNWL58OeLj4xEQEID8/Hzs2LGDXkBL4OzsjBMnTkitPxMTE5w9exbdu3dHUVERs93MzAznzp0TCiKnSI6FCxfWmGDwitDU1MSSJUvYliF3GBoaYubMmfD390e9evVQp04dtiVVGZkxcgDw6tUrREREwNLSUmiftrY2unTpgi5dugjtU1JSgpubG4Di0dj27duRkpICoPhOjiJM27ZtkZiYKNU+u3Tpgv3792PatGngcrno1asXTpw4IfHCrpT/UVRUhPj4eCgqKrItRWrwrwUU0eFwOGjXrh1CQkJw8+ZNeHh4yO06rswYOQUFBTx//hyDBg2qVhUAd3d3uLu7y0Tl49TUVAQFBSEtLQ0aGhro2LEjVFVV2ZYFAKxVOp80aRLc3d2Rnp4Oa2trVjTIA4QQcLlcsU+1XbhwAebm5rVqViM7OxuEkFr1msWBqqoq7O3toaioiKCgILRv355tSVVCphahOnToAF9fX7x7967abbH1hT506BBcXV1hZmaGOnXqoHPnzujfvz+6d+8OCwsLmSr8qKKiwkpSWyMjI2rgyiEuLg5bt27F1q1bcebMGbHlYoyMjMT379/l2h28KqioqNDRXDXgGzl5RaaMHIfDgZOTk1DeQnkiJCQEDx48QGxsrNC+hIQETJ06FXv27GFBmTAWFhYCgdkU9nn48CGOHz+O/v37Y9GiRdDR0cH27dtx+fJlgYQFlYUQgvPnz2PIkCG1bkSjoaGBhIQEtmXILY0aNarWd49tZMbIFRUVwcPDAx06dECvXr3YllNl7O3tKzxm2bJlyMrKkoKa8rGzs5OahyWlfHJycrBnzx6EhITA09MTNjY2UFBQgJubG37//XcAwNatW3Hz5s0qJR6+du0azMzM0KhRI3FLl3m0tbWRnJzMtgy5RUFBATo6OnI7+JAZI0cIgbe3N759+4ZLly7JxJpaVbCzs6vwmPT0dJkIsGzbti0+ffrEtoxaT0BAAHbt2oVmzZph6tSpUFNTE9ivrKyMQYMGYe7cucjKysLmzZvx4MEDkX8jcXFx+PLlC4YPHy4J+TKPnp4eUlNT2ZYht3A4HLi7u+PevXsVVhKRRWTGyCkpKSEjIwNXr15FaGgomjdvjtzcXLZlVZomTZqIdNyLFy8krKRiunXrhrCwMLZl1GouX76Mu3fvYuLEiaV6DpdETU0NI0aMwMyZMxEbG4vNmzfj6dOn5Ro7Qgi8vLzg7u7OmrMR29SpUwfp6elsy5BrtLS04O7ujhs3biAoKEiuyhhxiAwOmdLT06Gnp4cXL16gQ4cObMupNKKuecjCW29sbIz4+Hi2ZdQ68vPzcejQIWhqamLs2LFVSkSQlpaGS5cuITk5GV27dkXbtm2Fjrl79y5iY2Mxfvx4MaiWTxISEnDq1CnMmzePVR0ZGRkICAhAx44dWdVRHbhcLs6dO4fGjRsL1Y2UWdhIs1IRaWlppH///mzLqDKoIH0V//H333+zLZUYGxuT1NRUtmXUKkxMTIiamhrR0NAg2tra1X5oaWkRNTU1oqqqSjQ1NQW2q6qqiqUPbW1tYmZmVuFry8jIIAsXLiSurq7EwMCAACArV64U6X2pzLne3t5l/q5evnwpdPyOHTtISEiI0HY/Pz8yceJE0qhRI6KmpkbU1NRI48aNyZQpU8jbt2+Z486fP08AkDNnzgi10aJFCwKA3LlzR2hfo0aNSKtWrUhkZCTZsGED2bBhAwkMDBTp/ZBVcnJyyMGDB0leXh7bUkRCJucvCCGIjo6W29gWdXV1kaZaV6xYAR6PhxUrVrD2Ort3746VK1dix44drPRf2zh27BjS0tLEFhbwM6UlKxc1gbk4SE5OxoEDB9CyZUsMHDgQhw4dkui569atQ7du3QS2lZYAomfPnrhz5w5mzpzJbNu/fz9mzZqFpk2bwtPTE/b29uBwOAgODoaXlxfatm2LkJAQWFlZoWvXruBwOPD29saIESOYNlJSUvDp0ydoamrC29sbvXv3Zvb9+PEDYWFhGDVqFLy8vPDLL79AVVUVx44dg5WVlczEzFYWdXV1aGtrIy8vTz5eA9tWtjR4PB5Zt24d6dq1K1m+fDn59u0b25IqxYwZM0QezQEgS5cuJTwejxWtERERxNTUlJW+axNFRUVk6NChxNHRkWhra1fq+yErD21t7QpfJ4/HY77LiYmJlRrJVeZc/kju/PnzIrVNCCFbtmwh8fHxhBBCnj17RhQUFIi7uzvJz88v9fhz586R6Oho5nnz5s1J06ZNBY65dOkSUVZWJnPmzCFOTk4C+06cOEEAkEmTJgkkHr937x45ePCgyLplkXv37pGYmBi2ZYiEzDielITD4WDp0qW4dOkSFBQUYG1tLVdxLr/99luljl+/fj1+//13VtboLC0toaamhrdv30q979pCSkoKbGxskJmZidevX7MtR6JwOJwqz0pU51xR6NixI27cuAGgeASoqKiI/fv3l1lTcdiwYQIJ3bt164YvX74IxMD6+Pigbdu2cHNzw/v375GZmQmgOBfvgQMHoKCggE2bNkFbW5s5x9XVFbm5uXjz5o0kXqZUMDExwbdv39iWIRIyaeT46OvrY9asWejRoweMjIzYliMyDg4OmDt3bqXO2bZtG2bOnMmK19KUKVPwxx9/SL3f2sDz589hbW2NcePG4c6dO7Wq0oU0mDlzJpSUlKCjo4PevXvj2bNnZR7bvn17JCUlIT09Hd7e3nB0dKxU3Uj+tGjJBAre3t5wcXFBp06dwOFw8PTpU2RnZ2PHjh349u0b2rRpU2py47Fjx+LRo0eMUZQ36tevj5iYGLZliITM/+LmzJmDqVOnsi2j0qxatQq2traVOmfv3r2YPHmyQJZ+aTB//ny8e/dOrtyC5YHIyEi4ubnh4sWL9CZCzOjq6sLT0xP79++Ht7c3duzYgaioKHTt2hV3794t9RwOh4OWLVvizJkzyM3NLTURfFFREbhcLvMoObvi4uICBQUFxsglJycjICAALi4u0NLSQuvWrXHt2jXs3r0bjRo1Qnx8vMB6ISEEOjo6SExMhK6uLrp27Yrjx48z+/Pz82FiYiJT4Q4hISFQUlISWEPOy8vDgQMHMGrUKGRnZwsc7+TkhFOnTiE/Px9KSkpCAeRcLhc2NjY4evSoVPQDcmDkYmNj4eLiwraMSqOjo4OHDx9W2tAdPXoU06dPl5Cq0lFRUYG9vb3MpBurKaxatQqjRo1C165d2ZZS42jVqhW2b9+OgQMHwtnZGRMmTMCLFy9gamoqVIm+JD179kRUVFSZ+9u0aQNlZWXmsXXrVmafvr4+WrZsyRi5x48fQ1FRkSkTZWNjgxs3bmD48OGMoSpp5EJCQlC3bl0YGhoCKB5Zqqio4OHDhwCKEyLHxcVBV1e3am+KBPD390fTpk0FEhR8+vQJysrKsLCwgKampsDxnz9/RsuWLaGqqorGjRsjODhYYP+RI0egoqKCcePGSUU/IAdGbufOnRgyZAgriYSri6mpKXx8fNCiRYtKnXfw4EFcvnxZQqpKZ+7cuVK9u6oNeHt7Y8GCBWzLqDXo6emhf//+8Pf3L9O7WVFREc2bN4eqqioiIyOF9p8+fRpv377FtWvXSj2/W7du+Pr1K2JiYuDt7Y02bdpAS0sLt2/fhpKSEmJjY1GnTh14e3tDSUkJnTt3Zs719fVFy5YtMXHiROjo6MDJyQldunTBu3fvkJCQgB07dmDy5MkAikc8f/75J8zMzFC3bl14enoyo0oul4tFixbBxMQEVlZW2LdvH6ysrJh+oqKi0LdvXxgaGkJPTw9TpkxhZmk+f/6Mnj17ok6dOtDX18fs2bPLfU/9/PzQsmVLgW1v3rxBWlqaUHan79+/Iz8/HzY2NgCKsz+VNHK5ublYtWoVNm7cKNVpe5k3ci1atMCqVauwfPlymQierixGRkbM/H9lWL16tYQUlU5UVBRMTEyk2mdNJzc3Fw0bNmRbRq2Cf40oz4HF3d0dDRs2xLt374QSqdvZ2cHR0RHNmzcv9dyS63I+Pj7o0qULjh07hsjISGzatAkA8OTJE8YhRUtLiznX19cXL1++xJgxY5CUlIQmTZpgzZo16N+/P06ePAk/Pz+m399//x0fP35EQEAAQkND8eTJE3h5eQEoLnobFBSEgIAA+Pj4YO3atQJhE5mZmVi6dCliYmLg5+eH27dvM6PF0aNHY+LEiUhOTkZkZGSFIyo/Pz+hm3QHBwd4e3sLGbmgoCDY29szmXXs7e0FjNyOHTtgY2ODvn37ltunuJF5IwcUx3LZ2dlh/fr1bEupEnXq1MGDBw8qlb3l48ePUl2be/78Odq1aye1/moL1NFEeqSmpuLGjRtwcHAQyv9ZEg0NDQwePBhFRUWYNm1apTLsd+nSBYqKirhw4QICAwORnp4OLS0tTJs2DXXr1oWDgwOOHz+OiIgIofg9X19fLFu2DN26dYOKigrGjh2LwMBA2Nvbw8jICE+fPkWLFi3w48cPHD9+HMePH0edOnWgp6fHeG/GxMTgyJEjOHToEAwMDFC/fn107NhRwMjZ2dmhS5cuUFZWhqWlJdq3b8/k7gwLC0NRURF4PB50dHQqvPn29/cXGsk1adIEX758wZ49e2BgYMA8hg0bJnBsSSOXmpqKzZs3MzcC0kQmg8FLY/bs2XB1dcXixYvlsqqxrq4u7t69C3d3dzx+/Fikc2JjY1GvXj0JKysmMDCQTq1RxMLt27eRnZ3NeA4GBQXhwoULAAA3NzdoaGjg8ePH6NGjB/7880/8+eeflToXAEaNGgULCws4OjrCwMAA3759w9atWxEfH49jx45VqHHevHn4/Pkzrl27htatW2PKlCmwt7eHgoICYmNjcfHiRQAQqoato6OD1q1b48qVK+BwOBgyZIhA1RQXFxds374dAEo1ciXrSSYmJqJu3boAgBEjRmDixInQ09PDkydP0L59e+jr6zPHJicno1GjRnj48CEcHBwEZl1SUlIEjNzp06exY8cOhIaGgsvlIisrC8uWLQMAeHl5Ye3atVi4cCHGjBmDdevWlVmYNyMjAxEREUJGTl1dHYmJiVixYgV0dHTA4XDg7OyMNWvWCBxbcrpy/fr16NOnD9q0aVNqXxKFzSC9ynLo0CEye/Zs1gKnxUF2drbIwbcfPnyQmi5DQ0NSVFQktf5qA8bGxqVur8nB4IQQYmlpWWYb4eHhhJD/BXP/HOwtyrmEELJ+/Xri4OBAdHV1iaKiIjE0NCSDBg0ib968Efnz2bNnDzl37hyZMGECadiwIVFVVWXSeo0dO5Y8fPiw1PMmTJhAAJBmzZoJ7bty5QoBQFRUVEh2djazPTo6mgAgOTk5zLahQ4eSLVu2EEII+fLlCzEyMiKbNm0i27dvJyNGjGCOKygoIJaWluTx48dk27ZtZPjw4cy+lJQUoqWlRfz9/QkhhNy9e5fY2NgQPz8/wuVySUJCAtHU1BQKeI+IiCAWFhbk5s2bZb4/T58+JXXr1hXa/vz5c6Kurk4KCwsJIYSEhYWREydOkPr16xMvLy+Sk5NDioqKSF5eHlFSUiL+/v5EV1dX4POTJnJl5AghZPXq1eTChQtsy6gWjx8/FumiUlouPElQVFREDA0NpdJXbaK2Gjl5ISIigmzfvr1S59y7d49s2rSJJCcnV+q8GzduECUlJXLs2DFSWFhIDh8+TKysrJhMKOfPnye9e/cmly9fJitXriSmpqbkx48fJDU1lUyYMIH07duXEELIrVu3iImJCYmKiiKJiYlk4MCBRElJiTFimzZtIn369CFZWVnk+/fvpFevXqR58+aEEEIuXrxIwsLCCCGE+Pr6EmNjY+b5uHHjyLhx4wQ0//vvv8TFxYXk5uYyj/z8fLJnzx7SoUMHgWNTUlIIh8Mhx44dI8ePHyd79+4lwcHBpGnTpsTBwYHMnz+/Uu+XOJGb6Uo+48ePx9q1azFkyBC2pVSZ0uJzSkNaWV7ev3/PTJtQJE/J7BfyhLzqLgtLS0sQQhAZGVnhb5IQglOnTiE9PR1z584tc4qvLHx9fTFlyhScPXsWnp6eaNOmDe7du8e8p58+fUKLFi3g4eGBsLAwDB06FK1atUJRURGGDh2Kc+fOAQD69OmDvn37wtbWFg0aNMC4ceMQERHBZG0ZPXo0zp8/DyMjIzg5OcHBwYFJpPH48WPMmDEDWVlZaNSoEQ4cOMA4Rv348UMgJydQ7HTy+PFjqKurM9v69+8Pc3NzobU8X19fmJubM44ssbGxeP78ORo0aIDXr1+zGyfKmnmtIiEhIWTatGlsy6gWok5Z8qcyJM2aNWvIkCFDpNJXbSE/P5+YmJiwLYNSAX5+fmTPnj3lHpOXl0d27txJvLy8pLJUEh8fTzZs2EByc3MrPHbx4sVk8eLF1eqvsLCQ2NrakoKCgmq18zMZGRnk8OHDzLQmW8id61ejRo0QGhrKtoxqoaGhIeBaXBbSGsm9evVKLuv2yTJxcXECd8AUYXg8HrKzs5GYmIiQkBB8+fIFWVlZUtXQokULZGdnIzk5udT9CQkJ2LlzJ5o3b46RI0dKpVqIkZERHB0dceLECaF9L168wI8fP8DlcnHx4kUcO3aswli3ilBSUkJQUFClR6cVweFw8P37d6lncPoZuZuuBFAjKhwbGRlV+IOWlpH7/PkzNmzYIJW+agvx8fGMJ2BtpbCwEMnJyUhJSUFycjKSk5ORmpqKzMxM5OTklFluyMjICH369EGjRo2kotPJyQk3btwQihkLDg7G1atX4eHhUenMRdWlR48eCA4OxqtXr9C+fXtm+/v379GvXz8QQtCiRQvcuHED5ubmUtUmKhoaGjA0NGS9HI/cWYv379/LZY25nzEyMkJYWFi5x0jDyN2/fx/fv3+X2R+KvBIfHy+U8qgmUlRUhNTUVMaIlTRqGRkZVWozISEBJ06cQPv27dG7d2+J/96dnZ3x8uVL5ObmMqNvHx8fvH37FpMmTWLScEmbcePG4d9//4WtrS2T6mv27NnVHrlJC0IIEhMTweVyWR2YyJ2RCwkJwciRI9mWUS3y8vLA5XIrPC4+Pl5iGgoKCjBkyBC8e/cOAwcOxIABA/DkyROJ9VfbSExMrBGOGoQQZGZmIi0tDWlpaUhNTUVqairzf0ZGhsQyEb169Qp169ZF27ZtJdI+Hw6Hg2bNmuHmzZsYMmQIzp49i8TERMyZM4fVUYi2tjb09PQQGRlZ6dSAsoCCggLq1q2LnJwcoXhDaSJ3Ru7169eYNm0a2zKqRHR0NDZv3oyjR4+KdJf7c4ZvcXH//n2MGjUK/fv3R3R0NDgcDiwsLJipGUr1SUpKYvWHXRkKCwuRlJQkZMBSU1ORnp4u0g2ZpLh9+zZsbW1FWsOuDr169cI///yDPXv2QFdXF7NmzWJ9xigrKwsZGRllphiTdTgcDrp3747z589j0qRJrOmQOyN3/vx5KCkpYfPmzWxLEZnw8HBs2LABx44dQ0FBAezs7JCdnV1qgtiSiHskwOPxMGHCBNy5cwfXrl0TcDa5dOkS3Nzc0K9fvxqx5sk2KSkpMpVNnk9eXh7i4uIQGxvL/E1KSpLZMks8Hg+BgYESTzmnrKyMFi1aQFlZGT179pRoX6Jy584dNG/enHVjWx0yMjLomlxlWbhwoVxchOPj43H16lVcunQJ9+/fB4/Hg6OjI5YvX44mTZoIJTctDQsLC7HpCQ4ORq9evdC8eXNERUUJVUNu27YtunXrhjFjxjCJYClVJzU1VSAtExtkZmYyhoxv1Pg5DOWJ4OBgqeRVlXbi4PLg8XgIDQ3F/Pnz2ZZSLdq1a4fAwEAQQlgz1rJvLX7CwcEBL1++ZFtGqYSHh+Py5cu4fPkynj9/DkIIFBUV4erqivnz58PV1RWnT58WOX+buDLYr169Gtu2bcOePXvwyy+/lHncqVOnYGZmhnfv3lW6agJFkLS0NKnlHSWEIDU1VWB0FhcXJ3V3fEkRFxfH6kWSDZ48eQJzc3Oxu/VLGwUFBVhYWODatWsYMGAAK5+hXBk5Ho+H69evo0GDBmxLQW5uLoKDgxEYGIhPnz7h/v37+PjxIwBATU0N7u7uGDx4MNzd3VGnTh3k5ORgypQpOHTokMh9VPd1JiUloXv37uBwOAgNDUWdOnXKPV5ZWRkHDhzA4MGD8f3792r1XZspKCjAgwcPsHz5con1kZaWhpCQEHz79g2RkZFluuPXBPLy8pCVlVUjHHlE5d27d5g4cSLbMsRCz549cebMGfj6+qJ169ZS719ujNznz58xbtw4jBw5ErNmzWJFQ3BwMNauXYtXr14hLCxMwKtMR0cHo0aNwuDBg9GnTx8B9/Hg4GAMHz4cAQEBlerP2tq6WnpbtGiBadOmCWR5r4hBgwZh69atWLx4MTZu3Fit/msrI0eORJ8+fQQyw1cXLpeL79+/M4YtMTFRbG3LAwkJCbXGyAUFBUFdXb3Cm1J5QUFBAb1798bFixdhbm4OY2NjqfbPIZLy/xUjhBB06dIFc+bMwaBBg6S+Jsfj8bBz504sWbIE+fn5aNy4MZo1awZ7e3vmYWdnJ6SLx+Nh7969WLhwYZmVistCT08PsbGx5dbFKo+XL19i7Nix+PbtW6XPTUtLQ8OGDREeHg49Pb0q9V9bef78OQYNGoSYmJhqf0/T09Px7ds3fPv2DeHh4SgoKBCTSvmjd+/etSYrz+7du9G7d+9q3+TKGh8+fEB6erpQCSJJIxcjuTt37sDZ2RnDhg2Tet8xMTEYO3YsHj58iAYNGuDEiRNwdnau8LwfP35g4sSJuH//fpX6nTVrVpUNHABs2rSp3PW38tDT04OrqyvWrl0rV16sbMPj8TB8+HAcO3asWgYuKSkJt2/flvv0deKktoxcExMTkZ+fX+MMHABoamqysgwi8yO5qKgojBs3DpcvX5a6S/bXr1/RvXt3REdHY8KECdi+fXuFsU+EEJw+fRqzZs1CWlpalfodNWoU/vvvv2pVlTYyMkJISEiVY7WCg4PRu3dvujZXCebMmYOgoCA8ePCgym08f/4cjx49Yj3fn6xRv359VmOtpMXx48fRqFEjkW6k5Y0DBw6gbt26Uq8gI/MJmv/66y9s3bpV6gYuKCgILi4uiI2NxdGjR3HkyJEKDUZycjJGjBiBX3/9tUoGTlFRERs2bKi2gfvw4QO0tbWrFYxsa2sLLpeL8PDwKrdRmwgNDcXJkydx5cqVKrfBd2CiBk6YxMREiWVWkRXy8/MRFxeHzp07sy1FIqiqqopcZkycyLSRW7NmDczNzdGqVSup9hscHIyuXbsiMTERp0+fxvjx4ys85+bNm2jWrBnOnz9fpT7r1auHx48fY/HixdUycACwdetWsdwtDRo0CCtXrqx2O7UBNzc3bNq0qcqZOdLS0nDjxg0xq6o55OXlITMzk20ZEuX+/fto0qRJjQyV4PF4SEpKEjl8SpzI7JrcrFmzYGJigtWrV0u97+nTpyM5ORkXLlzAoEGDyj02MzMTv//+Ow4ePFjl/vr164fjx4+LrXDpx48fxeKBunLlSrlNKSRNtm7dCjU1NUyePLnKbQQGBiI/P1+MqthHQUEBenp6qFu3LurUqYO6deuibt26OH/+fJVCHhITE+UmVVplIYQgMDAQc+bMYVuKRMjNzUVRURGNk+NDCMG7d++qPCqqDpGRkXj8+DGGDh1aoYF79uwZxo4dW+UpPSUlJaxbtw6///57tUdvJUlKShJLUlsjIyNoaWnh9evXUsk4IY+kpKRgzZo1CAwMrFY7FVWkkFUUFBSgo6MDfX19xoipqqpi//79uH79OlJSUmBjY4MlS5YwGUWsra3x6dMngXauXLkCPz+/MvuZNGkSEhMTYWVlxWx79uwZ1q1bh5cvXyIvLw/16tXD2LFjsWLFCpG0+/v7Y8eOHfDx8UFMTAyA4hmV7t2747fffmMSIly4cAHDhg3DmTNnhKpnt2zZEv7+/rhz5w569+4tsM/Kygq6urr48OFDhVrevHkDAwODGluDsKioiLU0dzJp5MLCwpCZmcmK1T9z5gwAlOuZmJ+fjxUrVmDLli1VXido0KABTp06hY4dO1bp/IoQV5jFhAkTsGrVKty6dUss7dU0+vXrh1mzZsHMzKxa7ZRVtFMW0NLSgp6eHvT19aGvr8/8r6enB11dXaEbtF69euHt27fYsGEDmjRpgtOnT+OXX34Bj8fDqFGjYGRkJNRHly5dSs2y4+XlBUVFRZiZmSE9PZ3Zfvr0aYwZMwbDhw/HiRMnoKWlhdDQUMZYVcT+/fsxa9YsNG3aFJ6enrC3tweHw0FwcDC8vLzQtm1bhISEwMrKCl27dgWHw4G3t7eAkUtJScGnT5+gqakJb29vASP348cPhIWFiZyW68WLF3JfXaU88vLyxDZTVVlk0shpamqiY8eOTFokLpeLgoICqRSh9PLygo6ODtzc3Erdn5OTg4EDB1Y5NAAAJk6ciG3btklk6iUmJkasCVEXLFhAa82Vwblz5xAbG4u///67SucXFhbix48fAIovAmzmlVRVVYWhoSEMDQ2hp6cHbW1t6OrqQkdHB8rKyqhXr55IKaZu3bqF+/fvM4YNALp164bIyEgsXLgQI0aMKPViV6dOHaHg54iICOTk5MDZ2RkKCgooLCwEUFzNY8qUKZg6dSr27NnDHC9q/NXz588xY8YM9OvXDxcuXBDI49q9e3fMnDkT58+fZ0ZVBgYGaNasGXx8fATaefz4MZSUlDBp0iR4e3sL7OM/F0VTREQEFBQUYGpqKpJ+eeTr169SS3P3MzJp5NTV1ZGRkYHc3Fxs3boVZ8+eRUBAAHJzc6sVO1YRQUFB8PPzw/jx48vs5/fff6+ygTMyMsLBgwcxYMCA6sgslwcPHog17Zmamhrq16+PixcvSt31V5YpKCjAjBkz8OjRoyq38ePHD6lVv64uYWFhIuVSvXz5MrS0tIRiWidMmIBRo0bh9evXIieu9vX1BQDG8Yw/Yjx06BCys7OxePHiyrwEhnXr1kFRURH79+8XSlTO52f93bp1w86dOxEbG8sYIx8fH7Rt2xZubm74999/kZmZyWRl8fHxgaKiokihAHfv3kXXrl2r9FrkBWVlZdaSGcikd6Wuri4IIejcuTNycnIQEBCAO3fuSNTAZWRkMKO3sqYqv379igMHDlSp/cGDByMgIECiBg4oLjQpznRSAODp6UmDwn9ixIgR6Nevn1wWs5QkAQEBsLW1FZou579PAQEBIpX1ycvLQ1BQEBo1asQYRb6Re/LkCerUqYPPnz/DwcEBSkpKMDIywrRp0yqs01hUVARvb284OjpWauTEH5GVHM15e3vDxcUFnTp1AofDwdOnTwX2tW7dusJ1qIyMDGRkZKBly5Yia5FHdHV1oaioyErfMmnkgOKpoHfv3mHNmjWYPHkytm3bJtH+Dh48iMjISBgaGqJ79+6lHnPs2LFK193S0dHB8ePHceHCBRgaGopDarkEBASIfZ1v3Lhx+Pr1K6vFM2WJ58+f48WLFzh69CjbUmSO5OTkUnMu8rclJyeLtI4dEBAALpcrED7EN3LR0dHIycnBsGHDMGLECDx48AALFy7EiRMn4ObmVm77SUlJyM3NLTVeq6ioCFwul3mUbMfFxQUKCgqMkUtOTkZAQABcXFygpaWF1q1bM1OUUVFRCA8PF5iqJIRAR0dHKHPL7du30bJlS+Tn58PExERg3ZFtQkJCoKSkJOQJy59e/bmos5OTE06dOoX8/HwoKSkhOjqa2Xf9+nUYGBjAxsZG6r8bmTVyYWFhWLhwIdq0aQMul4stW7ZIrC9CCA4cOABtbW1ERESU6rRBCKl0nbXu3bvj06dPGDt2rNScaCIiIso00lVFQUEBLVq0wL///ivWduURHo+HYcOG4cSJE2L1iK1JlPdd53A4It0o+vr6Ql1dHTY2NkLt8ng85OXlYdmyZVi6dCm6du2KhQsXYv369Xj+/DkePnxYJd1t2rSBsrIy89i6dSuzT19fHy1btmSM3OPHj6GoqIhOnToBKDaCfCNX2npcSEgI6tatK3CjW1RUxPxeVVVVERcXJ1OFdv39/dG0aVOhGTQ/Pz9YWFgIJKEHipPot2zZEqqqqmjcuDGCg4OZfRwOB/fv34eKigrGjRsnFf18ZPJX+vbtW1hZWcHZ2Rnv3r3D0aNHxT4FV5LHjx/j69evmDhxYpnOLa9evUJERIRI7ampqWHHjh24f/++WAufVkRgYCCys7Or7elXGn/88Qf27dsn9nblDb4n3s/u4pRi6tatW6qnaEpKCoDiEV1FRi4+Ph4xMTFo0aKFwA0n/6aC77jy82fAD1Eoz2Wf76YfGRkptO/06dN4+/Ytrl27Vuq53bp1w9evXxETEwNvb2+0adOGCf53cXGBr68v0tPT4e3tDSUlJYHMJb6+vmjZsiUmTpwIHR0dODk54fTp07CwsICSkhJ27NjBxFlyuVz8+eefMDMzQ926deHp6cmMKrlcLhYtWgQTExNYWVlh3759TFhFVFQU+vbtyzgPTZkyReC9/vz5M3r27Ik6depAX18fs2fPLvN9AoqNWWnTqH5+fkJFn79//478/HzmpsTOzk7AyKWmpmLdunXYuHGj1G8OZc7I+fr6Ys6cOfj06RM8PDykMo+7f/9+AMCUKVPKPEbUUZyWlhbzGqT5YRYUFKBnz57Yu3evRNp3dXVFUlJShWseNZlv377h1KlTuHz5MttSZJbmzZsjODhYaGqbHxfXrFmzCh0Q+Ebq59pj/GtBWeugfENQ3u9OUVER3bt3x7t37xAbGyuwz87ODo6OjmUmQCi5Lufj4wMXFxdmH9+gPXnyhHFIKZn9xtfXFy9fvsSYMWOQlJSEJk2aYNu2bYwfgL+/P9Pv77//jo8fPyIgIAChoaF48uQJc/1ZuHAhgoKCEBAQAB8fH6xdu5YZAGRmZmLp0qWIiYmBn58fbt++LTCqHT16NCZOnIjk5GRERkZWOKLy8/Mr9b0uzcgFBQXB3t6euSmxt7dnjFxKSgp8fX1hY2PDSvV1mTFyoaGhaNu2LdasWYNr165JdORWkqSkJFy6dAmdO3cW+uD4cLlcnD17VqT2goKCBKZYpEXfvn3Ru3dvDB8+XGJ9dOnSBevXr5dY+7LOsmXLMHny5Cqn7qoNDBo0CFlZWbh48aLA9uPHj8PMzAzt2rUTWsspCZfLxadPn2Bubi4UT8efNuN7+d6+fVtgPz+Ws3379uVqXLp0KYqKijBt2jQmLEEUunTpAkVFRVy4cAGBgYECHpG6urpwcHDA8ePHERERIRQ64Ovri2XLlqFbt25QUVGBs7MzEhMTmelJf39/tGjRAj9+/MDx48dx/Phx1KlTB3p6enBzc8P79+8RExODI0eO4NChQzAwMED9+vXRsWNH5lppZ2eHLl26QFlZGZaWlmjfvr1AWEpYWBiKiorA4/Ggo6NTalxiSfz9/cscye3duxcGBgbMY9iwYQLHljRy/v7+ePbsGTZt2iTyey1OWAshKCgoQHp6OgwNDfHt2zeMGzcOZ86cEchoIA2OHTuGgoKCckdx3t7eSEhIqLCtbt26oX79+uKUJxJbt25FVFRUtWL3RGHVqlVwc3OrtYaubt26lboo1kb69u0LV1dXTJ8+HRkZGWjcuDG8vLxw584dnDx5EoqKisjOzkZERAROnDgBFxcXgRHR58+fkZubW2q+Wv4aUK9eveDu7o7Vq1eDx+Ohffv2ePfuHVatWoX+/ftXmOC4U6dO+PfffzF79my0bt0aU6ZMgb29PRQUFBAbG8sY6J/jWHV0dNC6dWtcuXIFCgoKzHocHxcXF2zfvh2AcHycr6+vgGf28+fPmaUMHo+HoKAgtGjRAnfv3kX79u0FwiySk5PRqFEjPHz4EA4ODjAxMWH2paSkMEbu9OnT2LFjB0JDQ8HlcpGVlYVly5Yxx3p5eWHt2rVYuHAhfv31V6xdu7bMmNqMjAxEREQIGbmsrCyEhYXh/v37aNq0KbN95syZAseWnK5cvnw5evXqxUreSgAAkTK5ubnkzJkzxM3NjbRq1YoQQoiLiwt58+aNtKUQHo9HrK2tib6+PsnJySnzuAkTJhAAFT4OHjwoRfXF+Pr6En19fRIfHy+V/kxNTUlkZKRU+pI1/v77bzJs2DCxtRcWFibS90oWHmFhYSK/rszMTDJnzhxiYmJCVFRUSIsWLYiXlxez/+zZs2TcuHEEAHFxcSErV65kHo0aNSLKyspkyZIlAttXrlxJQkNDmTZycnLI4sWLSf369YmSkhKxsLAgS5cuJXl5eSLr/PjxI5kwYQJp2LAhUVVVJWpqaqRx48Zk7Nix5OHDh6Wes2jRIgKAODo6Cu27cuUKAUBUVFRIdnY2sz06OpoAYK4xhYWFpHnz5mTLli2EEEK+fPlCTE1NCSGE7Ny5k4wYMYI5t6CggFhaWpLHjx+Tbdu2keHDhzP7UlJSiJaWFvH39yd3794lNjY2xM/Pj3C5XJKQkEA0NTVJfn6+kE4/Pz+ir69PFi9eTL5+/SrwvhYWFpKCggIyc+ZMoq+vL3Tu8+fPibq6OiksLBTY3qRJE/Lo0SPmeV5eHlFSUiLe3t5EVVWVhIeHl/p+SgOp15OLiYmBo6MjHjx4gG3btsHW1hbnzp3Dq1evpCkDQPEIrXv37vD09GTuwEqjXr16Au6wZZGSkiJyoKs4yMvLg6WlpcQDzEsyffp05OXl1Ur3+VOnTuHAgQN4/PixWNormfGk5Lb9+/dXKVzDzMxMYtPVomY8EYWjR4+W6vhREdOmTRMYxcgLN2/exMCBA3Ho0CGMHj0aJ06cwKJFixAeHg5tbW1cuHABhw4dwp07d/Dq1SsMHjwYb9++haamJubPn4+4uDjcunULt2/fxsSJE/H27Vuoqanht99+w40bN5CdnY0dO3bg0aNHuHDhAlJSUjB58mTExsbC398fAHDp0iW0atUKFhYWmDlzJi5evIhjx45BQUEBf//9N0xMTHDo0CFcuXIFGRkZuH//PuLj4xkvUqB4rfPw4cP477//8OLFC+b1paenQ19fH0lJSQLhIzY2NlBQUIC1tTWuXr0q3Te9BFKfrjQzM0PTpk1hZ2eHZcuWYfr06RKPgSsL/vRBeVOVRUVFIhk4LS0tqRo4AOjZsycGDx4sNQMHFFcmqOmBq2XRpEkTJCUlia09ZWXlUrOItGvXDkFBQZVuLzc3l8krKctkZWVV6byfXdblBV9fX0yZMgVnz56Fp6cn2rRpg8mTJzPZUT59+sQ4eLRv3x4zZ85Eq1atUFRUhKFDh+LcuXMAgD59+qBv376wtbVFgwYNMG7cOEREREBFRQWjR4/G+fPnYWRkBCcnJzg4OAisaT5+/BgzZsxAVlYW9PT0sHPnTvTr1w8AsHHjRhgZGeHmzZsYNGgQlJSU4OPjg/fv3wusP/fv3x/m5uZCa3nv37+Hubm5UHykvb09bt++LbI/g6RgpTL4r7/+il69emHs2LHS7pohMTER9erVg5OTk0CmgtKoU6dOhXkFLS0tRQ4xEAdr1qzBqVOnBNx0pUXDhg1x/vz5CheuaxoZGRmws7MTGn2Jm8+fPzOJwitLt27dBNa4ZI2MjAz8888/VTp3xYoVrGXNEDdbtmzBvHnzqvV6lixZAgDYsGGDyOf4+vri1atXmDZtGjgcDrhcLlq0aAE/Pz+hkTqPx8OLFy+gqKiIkJAQDBkypFL5g3k8Ho4cOVKtElTigBXHk4MHD6J169asGrmjR4+ioKAAU6dOrfDYVq1aVZijMCcnB4QQqQR9v379Gtu2bcO3b98k3ldpjB8/Hn/99VetK/Kpo6MjFseToqIi5ObmIicnh3mUfF7VkQ5QHK8ka5T8XVT1pkxDQ6PGGDig2FM0ISGhUqnFXrx4AQsLC5iYmODq1as4duwY3r9/X6l+09LSYGVlhezsbISGhqJ58+ZlzhooKCgwTjxcLhevXr2qVKKJZ8+esVZ5oCSsGLmIiAgkJSVhzJgxePv2LUaNGgUFBQVoampi3rx5Uul/3bp1MDY2xtChQys8vm3bthUaucTERHz//l3i5d1zcnLQv39/nDlzptT0SdJg4cKFrHiRyjKpqan49OkTYmJikJSUhOTkZCQnJwv8n5ycDEVFRSxfvhxJSUnVMmZlIWqpGUmTk5ODsLAwhIaGIiwsDJMmTYKOjk6VpmEBSCUlnjTR1tYWSPYsCu/fv0e/fv1ACEGLFi1w48aNSlcIadmyJby9vXH06FEkJyejXr16IhkiLS2tSsfIRkREwMPDo1LnSAJWjJypqSlmzJiB4cOHw8TEBC9fvoSamhqmT58ucSNXWFiIkSNHIj09HSdPnhQp6bOoBUjfvHkjUSN37do1zJgxA7/++itcXV0l1k9FaGhowNzcHCdPnsSvv/7Kmg5Z4NmzZ9iwYQNu3bolUk5GSTtOsLD6AKB4dPrjxw+EhIQgNDQUsbGxAlpCQ0NhbW1d5ZGmNDMHSQN9fX2hPJYVMXv27AqzlFREnTp1MGTIELx79w4PHz4UOeYzLCysUtec9PR05OXlibXsV1Vhxcjp6elh1apVzPP+/ftj0qRJmD59usT7XrFiBV6/fo25c+eif//+Ip3j5OQk0nFv374VKtFRXXg8HrZu3Yrt27dDR0cH+/btE1m3JDlx4gR69OiBFStWID8/n0kfxOVyMWDAABw6dKhG5nZUVFRETk4OsrOzsWDBApw4cYJtSQLo6elJpR9CCFJSUhAaGorQ0FCEh4eXm8mEH7tVVSPMRoIFSVK3bl1ERUWx1n/Lli2RnJyMY8eOIT8/H5aWlujVq1eplcl//PgBDocDTU1NBAUF4fv37+jTp0+57T979gyurq4SrRwjKjJTT65Pnz6Ii4uTaB/37t3Dxo0b0bp160ot1tarVw/GxsaIj48v97i3b99WVyJDVlYWFixYgPPnz6N58+a4d+8e7O3txdZ+dXFwcEB0dDQSEhJgZGTEfJkLCgrQp08fNG3aFE+fPpVLl+/y0NHRwZYtW7Bz506ZrOYt6TWQxMREvHr1CqGhoUhLSxP5vICAgCpPVerp6UkkHyubmJiYICAggLX+lZWVmdyf8fHx8PHxQWBgYKnOZOHh4cjPz8exY8dgZWVV4XUwICAAhBCx1rWsDjJzq921a1e8e/cORkZGuHXrltirJMfFxWHMmDHQ0tLCmTNnKjWM5nA4Io3m3r17V+lSPD8TGRkJNzc3WFhYIDMzE9++fYOPj49MGTg+ampqsLCwELhbU1FRwaNHjzB9+nTY2dnVqDyP3759Q1xcHFauXCmTBg6A2Mss/UxhYSHev39fKQPHp6q/DVtbW6lV8ZAWZmZm5aY3kybGxsYIDg4uM9tUx44d4ezsjF9//RWtWrVCbm5umSPy5ORkvH//Hr1795aZz0xmjJyhoSGOHz+OgwcPYvjw4WIt0snj8TBmzBgkJCRg3759sLa2rnQboqzLZWVl4fPnz1WRiA8fPqBVq1ZwcnJCmzZtkJCQgFOnTrHmXFJd5s+fjydPnmDGjBkYP358tY0/mxQUFGDNmjVo3ry5TNX7+hk7O7tKOyJUFlNT00q5kYuDsnLKyjPq6uqs/SZiY2MZQ/Xy5Uts3rwZOjo6ZcZXKioqol69elBVVUV4eDg6d+5cpgF79eoV+vTpI7bEAeJAZqYr+QwYMADt27fHmjVrxNbmxo0b8eDBA4wfPx6jR4+uUhuixoS9fPmySj/KiRMnYsSIEVi6dGmlz5VVmjVrxpT/sLGxQVBQUKm1+mSZ58+fY8qUKVWeapMWOjo6TEZ7ScLhcNCoUSOpTbXp6OigXr16UumrppOTk4OjR4/CxMQEycnJUFZWhrm5OX777TehPJ1lUZEDiqGhIWJiYmBsbCwu2dVGZkZyJQkJCRFbItyXL19ixYoVsLGxwe7du6vcjqgelqtXr67SVE5mZiZGjhxZ6fNkHSUlJdy/fx+WlpZivXGRNGlpaZg2bRo6d+4s8wZOSUkJI0aMkFp1BGkmUa+JU5V8OByOVBN+E0JgYmKCIUOG4LfffsPYsWPRq1cv6OnpiewkFhERgS9fvpSZBSogIACNGjUSp+xqI5NGrn79+kyRxeqQmJiIkSNHQklJCWfOnKlWWiADA4NSUzD9zPfv3zFz5sxKt5+TkyPxqSY2OXDggMRq3YkTQgjOnz8PW1tbps6grMNPtyQtpG3kaioaGhpCNe0kRVJSEk6dOsXU6ONwOFUKrp8zZw6A4nI7Bw4cwOnTp5GRkYGioiIUFhZCQ0NDpqqbAzJk5IKCgjBy5EgoKChAR0enUkGSpREfH49u3brh+/fv2L59u1jyLf5cWqMsTp8+jdOnT1eqbUIIVFRUqiJLLmjYsCHq16+PQ4cOsS2lTCIjI9G/f38MHz5c4p6+4oKfp1Ca6OjoSCU4m78WVFPR1tau0FOxugQGBuLIkSN4/vw5PDw8RLpRLw8lJSU4OjrCzc0N48ePh6KiIv7991/8888/2LVrl0zmF5WZBZIFCxZg6dKlWLlyZbUXZGNiYtC9e3d8+fIF69atw7Rp08SiceTIkTh58qRIx06fPh2dOnWSeAYUeWLv3r0YMmQI67nsfobL5WLnzp1YsWIFcnJy2JYjMo0bN2bcwKWNlZVVpYOZK4upqancreFWhqoEhIsCIQSBgYHw9fWFsbExxo4dK5H3UUVFhYkLLioqqvLoUNLIxEiuqKgI1tbW+Pz5M2xtbavlLv/9+3d06dIFX758wdatW8XqyNG3b1+RXbQzMjIwduxYFBUVia1/eadt27ZQU1MTqujMJu/fv0e7du3w+++/y5WBc3BwwC+//MLaRaVx48YS76MmT98DxU4a4giVio+Px8OHD5GdnY34+Hh4e3sjNDQUgwcPRq9evSR6o6CgoAAFBQUoKytDSUlJJtdPWTdyhw8fhrGxMa5du1bt7Onh4eFwcXFBaGgodu/ejfnz54tJZTEKCgo4duyYyC7UT548EWsoRE1g8+bNYv9cqkJWVhbmzZsHJycnfPjwgW05IqGoqAhbW1v8+uuv8PDwYPWu2dLSUuL913QjZ2JiUul8kD/z7NkzPHr0CIQQ/Pfff7h27Ro4HA769Okjk1OHbMD6XECDBg1Qp04d3Lx5s0rxa3y+ffuG7t27Izo6GgcOHMBvv/0mRpX/w9raGtu3by+3Bl1JVqxYAVdX13JLvxcUFMjkHZAk8PDwwMyZM/Hx40epryXxef/+PQYPHsxKxn4lJSWoqamhbt26qFu3LtTV1aGhoQENDQ3m/5+3qaioQFFRUabSpCkrK8PS0hJhYWESaV9BQUHmvPTEjampaZVnD65fvw5NTU1ERERgzJgxUFZWRo8ePQCg1lxLRIV1I9ejRw8cPXoUI0eOxNWrV6u00BwUFISePXsiPj4eR44cwfjx48UvtASTJ0/GzZs3Rap2y+VyMXr0aHz48KHMEWB0dLTUA2zZZMmSJZg+fTpevnwp9b4fPnyIgQMHSqQCQElcXFwwadIkGBgYMAbNwMAAOjo6NeYiZG9vLzEjZ2NjI7WQCLZQVlauUi7P/Px8xMXFwdXVFR06dGACr2vK90rcyMStYadOnaCnp1fpvHs8Hg+7d++Gk5MTEhIScPLkSYkbOKD4y3Tw4EGR8zJ++fIFkydPLtOhJioqqsb/oEsyY8YMhISESLz46M+kpqZi+PDhEjVwo0ePRkJCAnx8fDBmzBj07dsXTk5OsLKygq6ubo26ELVs2bLUKbGCggLcuXMHW7duxZo1a7Bv3z6Rgsfj4uJw+vRpbN++HePGjUOdOnXQoUOHMp29fH19MXDgQJiZmUFDQwM2NjZYvXp1pUZH/v7+mDRpEqysrKCurg51dXVYW1tj6tSpePfuHXPchQsXwOFwSq1y3bJlS3A4HNy9e1don5WVFeO2XxpVMXKJiYkwNjZGgwYNSk2oTBFEJowcUFzReNOmTZU6Z+fOnZg9ezays7Nx584d/PLLLxJSJ4yhoSGOHTsm8vFeXl6YPXt2qV/q6OhomYstkSQKCgqYPHmy2LxeRWXt2rViib8sjUaNGuHevXs4efJkjat9VhZKSkro16+fkOE+e/Ys/Pz84OLigtGjR8PMzAwXL17Ep0+fym0vLy8POjo6+OOPP3Dr1i2cOHECDRo0wJgxY4QSCQQFBaFjx46IiIjA9u3bcePGDYwcORKrV68W+Tqwf/9+tGnTBq9fv4anpydu3LiBmzdvYu7cuQgMDETbtm0RGhoKoDi3LofDgbe3t0AbKSkp+PTpEzQ1NYX2/fjxA2FhYejWrVuZGhQVFZGbmyuSXqB4ZkhdXR1hYWHUqU1EWJ+u5NOhQwcMGTIEc+fOFXlKhx+UOnnyZPTs2VPSEoXo3bs35syZg507d4p0/J49e6Cjo4P169cLbI+NjS0zb1xN5e+//4aRkRGysrKkNoq9cOGC2NtUUlLCggUL8Oeff9bKu2o7Ozv0798fN2/eBI/Hw7dv3xAWFobBgwejefPmAIpjJNPT03H//n3Y29uXubbo7OyMdevWCVQc6N+/P8LDw3HgwAEsX76c2X769Gnk5eXh4sWLzHWge/fuiI2NxYEDB5Camlrub+r58+eYMWMG+vXrhwsXLgjEqHbv3h0zZ87E+fPnmc/UwMAAzZo1g4+Pj0A7jx8/hpKSEiZNmiRk5PjPyzNympqaiImJYV4Dl8uFoqIiOBwOoqOjkZ6ejs+fP0NZWRnt27dHbGwsbt68ie7du8uku74sIjNGTlFREYWFhZg4cSJev34NXV1dfPr0qdzF9n79+sHGxgZnzpzB5s2bpVZLqyQbNmzAw4cPERgYKPLxurq6WLJkCbPt2rVr5U5p1ESUlJTg7u6OuXPnSiVAPCUlBZGRkWJts3379jhw4ABzMa+ttGnTBg0aNMCDBw9w48YNqKioMGFAderUgZWVFbS1tbFgwQL06dMHjo6O+P79O5KTk5Geng59fX2YmZnBzMys1JtbAwMDJCQkCGzjr0P9PAPCT1FVUWKFdevWQVFREfv37y/z2J9rQ3br1g07d+4UqOjt4+ODtm3bws3NDf/++y8yMzOhra3N7FNUVISzs3OZOnR0dBAfHw9LS0vcvHkT6enpUFZWBo/HQ3p6OszNzeHg4IDc3Fzcv38f6urqmDFjRq2a+ak2REaZNWsWefHiRYXHHTp0iAAg69evl4Kq0vn48SNRUVEhAER+7NmzhxBCSN++fYmTkxMpKipiTT9bpKenE319fVJYWCjxvqKioir1+ZT3UFVVJfb29rXyM6uI9u3bEwcHB5KQkEDy8vKY7QEBAQQA2b9/f4VtFBUVkcLCQpKQkED+/fdfoqSkRPbt2ydwTHh4ONHT0yNDhw4loaGhJCMjg1y/fp3o6uqS2bNnl9s+l8sl6urqpEOHDpV6bZcvXyYAyOnTp5ltzZs3J0uXLiWZmZlESUmJ3Lx5k9nXsGFD0rZt2zLbCw0NJf/X3n2HRXG1fwP/Lk1UlF5EBFFBRUCxi4qAKKJGxBoLKrGhj73EkphoEntDE43G3iuKiqBiAQ32KIiNIkVQRAFBetm93z/8uW8ICLvL7s4unM91cT2PuzNzvkt07p0zc85Zv349bd68mQ4dOkTBwcFERJSSkkK3bt2ioqIisfIxFeMRSbhUr4zFx8fD19cXV65cqXS7oqIi4eJ8iYmJnC23vmnTJsyfP1/k7Xk8HmxsbKCjo4ObN28q1OPh8uTh4YGOHTvi119/lWk7SUlJsLa2rnT1alEMGTIEnp6e+OOPP3D//n0ppft81RATEyO140mDtbU1Tp06JfY+zZo1w6VLl8q8npqaClNTU6xatarKCRp8fX2F84ZqaGjAz88P06ZNK7fdy5cv4eXlVWZ5q1mzZsHPz6/S2x1paWkwMTHBt99+i2PHjpV5j8/nl7lv/qXrEPj84JKBgQEmTZqEnTt3IiMjA4aGhggODoa7uzu6dOkCJycnrF+/HsnJyTA3N8f333+PtWvXAvj8kIm2tjZevXqFwsJCnDx5ElOnTgWPx4NAIICGhgYsLCwQHR2tMFdqcXFxaNWqFXJzc8usG5mYmIhmzZohJyenzMNHnTt3xuzZszFs2DDUr18fSUlJZcY7lpaWwtbWFosWLYKPj498PgS3NbZyw4cPp6CgoCq3W716NQGg3bt3yyFVxfh8Pg0fPlysKwIej0fnzp3jLLMiSExMJENDQ5m3c+3atWpdvZmZmQn/W2VlZZGurq5Ur+Ts7e2ldqUprR97e3uxP4eVlRX169ev3Otv374VucclKSmJHjx4QBcvXiRfX19SUVGh9evXl9kmISGBWrRoQd27d6fTp09TWFgYrVu3jho2bEjfffddpcd/9+4dAaBvv/223Htt27Yt8zv4b7sODg5kbW1NRET+/v6kpqZGOTk5RES0cOFC6tChAxERHThwgAAIr86IiGJiYqhp06ZERCQQCOjIkSOUlZVV5e+DS/7+/mRjY1Pu9YCAALKwsCj3eoMGDSgqKoqIiFq2bEkhISFl3t+5cyfZ2dnJtRdEoYvchw8fqHnz5lVu9/HjR9LS0qJWrVpx2oVUWFhI7u7uYnd9Xb16lbPMiqBTp07luqOk7ciRIxKf7GfPnk2fPn0qc7y+ffvS0qVLpZavphS5rl27VthFJ0535X/5+vqSmpoavX//XvjayJEjycjIiHJzc8tsu3fvXgJAoaGhXz1eZd2Vz549owcPHtD58+crLHLz5s0jAPTmzRuaMWMGdenSRfheYGAgqaioUFZWFk2YMKFMASQiOnHiBHl6epKPjw81aNCAzM3N6f79+8L3/fz8aOLEiUREVFJSQsuWLaNGjRqRnp4ezZo1iwQCgfC9hQsXkrGxMTVr1oz+/PNPatasmfA4r1+/pn79+pGBgQFpa2vT5MmThefFFy9eUO/evUlXV5d0dHRoxowZX//FE9FPP/1Eo0aNKvf6ihUryMPDo8xrSUlJpKGhIbz94OXlRVu3bhW+n5+fT6ampiJduEiTQveR6evr49WrV1VOYqqjo4MpU6bg5cuXCAwMlFO68urUqQN/f3/06NFD5H2Kiorg6emJu3fvyjCZYtu+fbvM15qTdFUBDw8P+Pn5CR8m+GLPnj3YsWOHUq94Lgt2dnZ48eIFSktLy7z+ZfiAra2t2Mfs3LkzSktLyww8j4iIgI2NTblxel/WfaxsXJ6qqipcXV3x8OHDckvd2NjYoGPHjl99mOjLk5KhoaEIDQ0tMxXhl3/3N2/eFD6QoqmpKez+fPz4Me7cuQNvb28cOHAA1tbW2LJli3D/J0+eCNudP38+IiIi8PTpU7x69Qo3b94Udq0uXLgQz58/x9OnTxEaGoqVK1eW+b3m5ORgyZIlePv2LSIjIxEcHIxr164B+DyO87vvvkNGRgaSkpIwfvz4r/6egM9L6tjb21f4+n8Xh37+/DnatGkjnCuzTZs2ePHihfD9LVu2oFWrVvDw8Ki0TWlTmKcrK8Lj8aCnpwcjIyMMGjQIEydOhKamJvr27Vtu2zlz5mDr1q1Yv3493NzcEBgYiODgYERFReHDhw8oKioC8LmoDBo0CAcOHJBJ5vr16yMwMBAuLi54/PixSPvk5eWhf//+CA0NrfAvVE3XsWNH1KtXD0FBQTJb3VrSIjdw4MAKXzczM4ODgwNWrFiBFStWVCcagM/3shSNJJm8vLywa9cu+Pv7Y+TIkcLXDxw4AFNTU3Tp0kXsY964caPcNF+mpqZ4+vRpuSEoX2bRqWrmpCVLliA4OBi+vr44ffq08GnNqjg5OUFVVRWnT5/Gs2fPyozt1dbWRrt27XDgwAEkJiZi9OjROHHiBGJjY7Fw4UI8fvwYS5cuhbOzM06fPg0nJyecOXNGuP+TJ08wduxYpKSk4MCBA0hISBAOg+jfvz/++ecfODs7Y+/evYiOjoaBgQEAwNHRscyE2f8uPhYWFujatatwIugv4+sEAgEaNmyIjh07Vvp5nzx5gqlTp5Z7PTIyEpcuXSozVrioqAjDhg0T/rlNmzbCe6sfP37E+vXrq3zGQibket0ogRs3blB8fDzFxsbSli1byNzcvMLt+Hw+NWvWjACQrq4ude3alebNm0dBQUGUmpoq3K6kpIQsLS1pxYoVMs39/v17atWqlVjdQ8bGxhQTEyPTXIrqwoUL1LJlS5kd39vbW6IuO39//68eMykpifT19dlTlv/Rp08f0tXVpb/++ouuX79OkydPJgB0+PBh4TahoaGkqqpa5t/h5MmTaf78+XTixAkKDQ2l06dP08iRIwkALVy4sEwb586dIx6PR127dqUTJ07QtWvXaOXKlaSlpUU2NjYiPZn4559/kpqaGtna2tLWrVvp2rVrdOPGDTp69CgNHTr0q92rnTp1Ih6PR6qqqpSdnV3mvblz5xKPxyMAFBISQleuXKHg4GA6efIkGRkZUVJSEt29e5cCAwNp37591Lt3byL6fP6qV68epaen05EjR8jd3b3McadOnUpr166lgwcPkpOTU5n33NzcyjzxeeTIEercuTPp6+uTtrY2qaqq0qNHj4iIKDg4mHr06EHGxsa0YMECKi4u/urvJzs7m3g8Hr1586bM6zk5OcTj8ejq1auUnJws/Bk0aBBt3rxZuF1kZCQZGxsT0ef7laNHj/5qW7Kk8EXu30aNGkVnz54t9/rTp0+pUaNG5OLiQgDIy8ur0uNkZ2eTkZERHTp0SEZJP0tOTiYLCwuxTqoWFhb0+vVrmeZSVI0bN6Z//vlHJsfu06ePREUuPDy80uM6OzvL/AuTssnJyaFZs2aRiYkJaWhokL29PR07dqzMNjdu3CAA9PPPPwtf27t3L/Xs2ZMMDAxITU2NdHR0qFevXl/9d3r9+nXq27cvmZiYUN26dcna2prmz59P6enpImeNiIggHx8fsrS0pDp16pCmpia1aNGCxo0bR9euXatwn++//54AUMeOHcu9FxAQQABIQ0ODTp48SZs3b6aioiLavHmz8F7hwYMHqbS0lIYNG0YbNmwgIqLo6Ghq1KgRERFt3bqVRo4cKTxmcXExWVhYUFhYGG3evJlGjBghfC8zM5O0tLToyZMnRER0+fJlatWqFUVGRlJpaSm9f/+e6tevX67oJyYmkrm5eZkhD/9169Yt0tfXL/d6eHg41a1bt9zQH2tra7p+/brwz4WFhaSmpkZPnjwhbW1tSkhI+GpbsqQURa6goIB69uxZ4VNTW7duJV1dXTp9+jQREfXr1494PB5FR0dXesz4+HjS1dWV2Un1i9jYWFJXVxfrxGptbU1paWkyzaWItm/fTp07d5bJse3s7CQqcq9evar0uImJiWRgYMCu5hgqLCyk3NxcEggEdObMGQoKChL+vbhw4QKpqanR3LlzKS0tjfbs2UPNmzcXPtB06tQp4dXbnTt3qFGjRpSSkkIfP34kHx8f4UMeQUFBZGJiQsnJyfThwwcaPHgwqampCYvYunXrqF+/fpSbm0uvX7+mvn37kp2dHRF9flIyPj6eiIgeP35MxsbGwj+PHz+exo8fX+bzbNu2jXr16kUFBQXCn6KiItq+fXu5h3aysrKIx+NRRkZGmddbtmxJ7dq1o3nz5knr1yw2hX7wBPg8TqNjx45YsGAB9uzZI3y9tLQU7u7u8PPzw9OnTzF06FAAn2/KEhE2btxY6XEtLS0xa9YsrFq1Sqb5iQhaWlpiTdsVExMDd3d3ZGVlyS6YApo2bRoSEhJksgROQkKCRPtVNQm3hYUFbGxshGOhmNpJIBBgz549OH78OC5fvozi4mLY2toKx79GREQIH46ztrbGkSNHcOXKFeEDTVFRUcL78V27dsX//vc/ODg4oHnz5lBXV8fJkycBAP369YOHhwdat24NFxcXdO/eHba2tsJZW8aMGYOMjAwYGRlh3LhxsLGxQdu2bQF8noKsW7du0NLSwrhx4/DXX3/B0tISwOd5Nrt3717mM0VGRiIsLEw4cXXdunUxdOhQREZGlruX988//6Bx48bQ09Mr83qbNm2QmJiIH374QZq/bvFwVl5FIBAIqGnTpuW6DV6+fEmNGjWiMWPGlPsGLRAIqEOHDlSnTh169+5dpcdPTk4mMzMzqef+tx49etDatWvp7t27pKWlJdZVhKOjY7lHpGu6pUuXlns0ubo+fvwo0VVcgwYNRDp+fHw8GRoasqu5WiwhIYHOnDlDAoGADh48SHfu3JHLjCWLFi2iRYsWVesYJSUl1Lp160rvzykzhS5yRJ8HZ/7999/CP1+4cIF0dHTo+PHjX93nxIkTBIB++OGHKo9vaGgos2ml/tuVdf36dapTp45YJ9q+ffuWmR6ppispKSFdXd1yN/Srw8fHR6IiZ2VlJXIb3bt3p7Vr10otM6Nczp07R7GxsTJvJzw8nJKTk6mkpIROnz5NxsbGlJKSIvN2lZnCF7nFixeTo6MjEX0uGjo6OhQZGVnpPl+eoNTV1S0zGLMinTt3rrRgVoerqystX768zGvnz58nVVVVsU62Q4YMkcv8jorCx8eHJkyYIJVjvXjxgtTU1CQqcj179hS5nZiYGHY1V4v5+/vLpdhs3bqVdHR0SFtbm3r27EkPHjyQeZvKTuGL3Pv370lXV5fCw8PJxMSEdu3aJdJ+f/zxBwEgPz+/Srf79ddfydPTUwpJy0pOTv7q4+VHjx4VPmYs6s/48eNrzQn0y8TN/72JLS6BQEBubm4SFTgAFc70UJlu3brRxo0bq5WZUT45OTl05MgRrmMwX6HwRY7o85iaLyeeRo0aUY8ePWjHjh2VnvRzc3NJX1+fLCwsKu1rTk1NpcaNG0s9s7u7Oy1ZsuSr7+/YsUPsk+7MmTOFU/vUdBs3biR9fX16/PixxMc4deqUxAUOAK1bt06s9l6+fElGRkYS52UUV1FREeXl5RHR5zFt/v7+dP36dcrKyqKAgAC5dFUyklGKIkdEwm/1AoGAPn36RL6+vmRgYFDpPj/99BMBqPJblqGhoVRvEqelpZGenl6VV15r164V+8T7448/Si2nort48SLp6urSuHHjhCcYUeXm5pKZmZnEBU5FRUWicT1dunShLVu2iL0fo9gCAgJo/fr1FBQURPv376f79+/TkSNHyN/fn/bt28eWxVFgSlPk/uv06dNVPlX0/v170tTUpHbt2lV6BeTo6CjVgeGDBg2iuXPnirTt0qVLxT4B/3fS2JosLy+PvL29xXoIhOjzzBPVuYobM2aMRHmfP39OxsbGtaZrubbYv38/FRYWUkZGRo19CrGmUtoiFxsbSw4ODsIZA75m2rRpBICuXLny1W3Wrl1LAwYMkEqujIwMsRYCFQgE9L///U/sk/Bff/0llbzKwsjIiD5+/CjStpcvX65Wgatfvz4lJydLlDMtLY00NTVJT0+PHBwcaP369WJfhTKK5cuwAEY5KW2RI/o8E0plK+8SEcXFxRGPx6M+ffp8dZsPHz4Ip9SprqFDh9L06dPF2ofP54s9tyKPxys3VVJNNnTo0ErvcX4RHx9PDRs2rFaRk3QogEAgICsrK1q/fj3x+XwKCAigfv36kaGhIVlYWJCvr2+VM6gw8peWllZmftv/EggEtH//fjkmYqRJqYtcamoqDR48uMrthg0bRgCEk5RWxNDQkAoKCqqV58tTgZL0z5eUlNDgwYPFOhmrqalRYGBgtTIri3/++afMmlkVSUlJoebNm1erwLVs2VLi+ys+Pj7k4uJS4XsvX76kqVOnUseOHenYsWO1diJuRZKbm0tBQUG0detW2rt3LwUFBVU4JvXNmzfs6UklptRFLigoSKRHtu/du0cAKp0Fu2fPnrR3795q5Rk9erRw0UNJFBQUiP3Iu6amJt24caNauZWFiYlJuRnRv0hNTSVra+tqFbiqurUrc/r0aTIxMRGpQCYmJpaZkZ+RL4FAQMHBwXTs2DF68uSJcJLoV69e0b59+ygiIoJKS0uJ6PNCn/v27WNdzkpMaYscn88nT09PevbsmUjb9+rVi1RVVSkxMbHC9zdu3Ej9+vWTOE9eXh7p6OhU+x9Dbm4udevWTawTs5aWFt27d69a7SqDyZMnV/gl4v3792RjY1PtAjd06FCJcr1584Z0dHTo6dOnIm0vEAhYkZOj9PR0Ki4upnfv3lFRURE9f/6cgoODhe/n5+cL/39paSlduXKFdu/eTYcPH6Y9e/Z89ZzBKAce0f8tW6tkDh06hIsXL+L48eMibX/x4kUMHDgQs2fPhp+fX7n3s7Ky0Lp163IrBYvKx8cHfD4fBw8elGj/f/v48SNcXFwQGRkp8j716tXDX3/9hTFjxlS7fUX17t07tGvXrswCqBkZGXB1dcWTJ0+qdex69erhxYsXMDc3F2s/gUCAFi1aYO7cuZg5c6bI+wUFBcHe3r7KxT0ZyQgEArx69QqxsbHIz88Hj8eDmpoacnJyUFhYiAkTJghXsK5IfHw8bty4AR8fH+Eky4yS4rrKSqqgoICcnJzo4cOHIm3P5/OpTZs2VL9+/a/OpGFkZCTRlVhBQYFIU4iJ4927d2RlZSX21ci0adNq9FyXVlZWdOvWLSL6vJZW+/btq30FB4BWrlwpUZ5Ro0ZJ1ANw/PjxStfyYiSTnJxMp0+fpnPnzlFERARlZGSUGc7x4cMHNnC7llHaIkf0/xdefPnypUjb79+/nwDQb7/9VuH7zs7OtGPHDrFz+Pr60vDhw8XerypJSUnUpEkTsU/YnTt3pqSkJKnnUQQbN24kV1dXysrKos6dO0ulwFlZWUn0xeDw4cNkamoq0byiHz9+/Ooq94xksrOzycvLS6pfNhnlp5RFTiAQCAd3v3jxggYOHCjSfkVFRWRqakpGRkYVPkm5detWcnNzEytLUVER6ejoiDyGS1zR0dFkZGQk9olbX1+fLl26JJNMXCopKSEdHR1ydHSUSoHj8XgUEhIido6kpCTS0dGp1lOSZmZm7H6PFB0/fpw2b97MdQxGwShdZ/O9e/egoqICFRUVbNq0Cd9++63IC5JqaGhg7ty5eP/+fYX3zsaPH4+oqCix8ixcuBC9evWCjo6OWPuJytraGleuXBH7+BkZGfDw8MCKFSsgEAhkko0LJSUlAIDbt29L5Xh+fn5wc3MTax+BQICePXti3bp1sLKykrjt3r17Y9OmTRLvz/x/fn5+mDt3rsT31JkajOsqKy4AFBwcTBs2bCAAdPfuXbH2z8rKooYNG5KVlZXwMeF/MzY2FmstMz09Pfrw4YNYGSRx+/ZtqlevnkRXK+7u7pSeni7zjLJWWFhILi4uUrmCA0CzZ8+WKMeQIUNo0KBB1f48jx8/rnLsH1O1oKAgmjRpEsXGxkp1HUKmZlCqInf37l2qW7dutY+zcOFCAkBnzpwp996kSZNIX1+fevXqRdu2bav0QZQLFy6Qvb19tfOIKiQkhOrWrSvRCd3c3Jzu3Lkjt6yy4OfnJ7UC9+2331b4Jacqe/bsoSZNmkhtbkp9fX02ue//SUtLo5SUFLp58yadPHmSnjx5Uun2AoGA3N3dycDAoNKJHpjaTSmKXGFhIYWGhtLs2bPJx8en2sdLSUkhdXV16tq1a4XvFxQU0K5du8jV1ZWMjIzI1NSUhgwZQufOnStzcuvTp4/c1w+7f/8+mZubS3xynzVrllzzSktBQQHp6+tLpcANGTJEokl24+LiSEdHh+Lj46X2uZydnWn79u1SO54ymzhxIgGg77//nn755RfS1taucg7RXr16yScco7QUvsh9maXfzc2NNmzYILWZByZMmEAAhI+jVyY1NZWWL19O7du3JwMDA2revDn5+vqSlpZWtacCk0R6ejr169dP4pO8qAvPKpIHDx5IpcANGjRI4mnXGjduTAcOHJDq5zp8+DB16dJFqsdUZp06dRL+Gx81ahRdvXq1wu0EAgEFBASQk5OTPOMxSkhhi1xhYSH98MMP1KVLF5mM+3r69CkBoG+++UbsfSMiImjatGlUr149Tooc0edxf8uXLxd7hfEvP9euXeMkt6T27dtX7QLn4eEh8d+l/v37y2SYCJ/PJ319fakfV1n5+fnRTz/9REREhw4dop07dxKfzy/XPXzw4EGaOHEivX37louYjBJR2CK3b98+6tKlC2VmZsqsjQEDBhAAev78uUT7jxw5UuwVB6Tt0qVLEnXj6ejo0IsXLzjNLo4TJ05Uu8j9e/omcWzfvp2aNm0qszXiWrVqRWFhYTI5tjK5e/cudejQQbhe4osXL8jV1ZXatWtHZmZmZcYj9u/f/6vzmDLMvylskUtMTCRbW1uZLj4ZFhZGACSeVDkjI4P09PQkGgwsTUlJSRINjG7WrJlMv0RI06NHj6pd5B48eCB2u8+fPycdHR2J15cTxdKlS2nYsGEyO76yAEDnzp0r9/rMmTMJgPBLWUxMDJmYmLBJkxmRKOw4OQsLC3z48EGmY7x69uyJzp0749ChQ4iOjhZ7fz09PXTp0gU//fSTDNKJztzcHDdv3sT06dPF2i8+Ph7z5s2TUSrpsrOzQ4sWLap1jJEjRyIzM1Pk7UtLS9G7d2/s3LlTpnNMtmjRQqxcNZWfnx82btyIWbNm4fHjx8LXv8zHevXqVQCfx47+e/5ShqkU11W2Irt37xZ2waWlpcm0ratXrxKPxyNTU1OKjo4We/83b96Qvr6+TK84xfH777+LdXXD4/GUptvyy7Rs1fnp06ePyFfebm5u5O3tLeNP9flKTh7tKINHjx7RwIEDCUCZf1Pv3r0jAHTp0iUCINUnXJmaTeGKXHFxMTVs2JC2bt0qt/FDhw4dIhUVFTIxMRF56Z5/c3Jykng1aVkIDg4mS0tLkU/8EyZM4DqySEpLS6lXr17VLnTz5s2rsq05c+ZQq1at5PLlZcyYMfTjjz/KvB1lwefzCQA1aNCgzOBuAHT16lXS0dGh3NxcDhMyykThityXb2xBQUFybff48eOkqqpKhoaGFBUVJda+MTExZGxsLKNkkomNjRX5gRQ1NTWlmdD59evXpKurW+1Cd/Dgwa+2sXHjRmrcuLHcnpx1cXGh/fv3y6UtZSEQCAgALViwoMyf4+LiaNSoURQYGMhxQkZZKFyRKy4uJnNzc4m6Dqvr9OnTpKamRvr6+hQRESHWvh06dKCdO3fKKJlkbt68Serq6iKd9GfMmMF1XJGdOnWq2kWuTp06dP/+/XLHPn78OOnr68tlqrYv2rRpQ7dv35Zbe8oiJyeH2rdvL7ya/vcwEnGn82NqL4Urcn5+fpx23QQEBJC6ujrp6uqK9TTeP//8Q40bN5ZhMsls2rRJpJO+pqYmvXv3juu4IvsyO0Z1fho3bkypqanCY4aFhVV7ZQFJmJqasjkXv8Le3p4SEhKEfy4pKaHY2FiJZqxhaieFe7oyMDBQ5FUFZMHT0xNnz55FXl4e3NzccP/+fZH2a9++PbS1tXHy5EkZJxSPr68vjI2Nq9yusLAQW7ZskUMi6fDz86vWCgAA8ObNGwwdOhRFRUV48eIFPD09ERgYWO3jiqu0tBQNGzaUa5vKIDY2FsnJyWjatKnwNTU1NbRo0QLq6urcBWOUC9dV9r80NDTo6dOnXMegS5cukaamJjVo0IDCw8NF2ufGjRvUtGlTGScT35o1a0S6smnYsKHM1sWThYcPH4rcHVvZz5gxY0hPT49Onz7NyecwMjLipF1Fd/nyZeHsJwwjKYW6kktKSkJxcTEsLS25jgJ3d3dcvHgRpaWlcHd3x82bN6vcx9nZGSoqKrh8+bIcEopu2rRp0NbWrnK7T58+Yfv27XJIJB0dOnTAmjVrqn2cI0eOwMXFBUOHDpVCKvHUpLX+pM3Kykrs9R0Z5r8UqsjFxsYCAOrVq8dxks9cXV0RHBwMIsKAAQPw4cOHKvfZtGkTZs+eLYd0omvYsCFmzJgh0rZ+fn7Iz8+XcSLpmTt3LkaOHFnt45w7dw63bt2SQiLxvHv3DnXr1pV7u8rAwsICubm5MDMzg5+fH9dxGGXF9aXkf3l5edHr16+5jlHGrl27CADt2LFDpO2bNGmicE9/ffjwQeS16LZu3cp1XLHk5uZSu3btqt1taWxsTCkpKXLNfuXKFXJwcJBrm8rmzJkzBICz7mRGuSnUlRwAZGdni/ywh7x4eXlBRUUFFy9eFGn7X3/9FVOnTpVxKvEYGBhgypQpIm27fv16FBcXyziR9NSvXx8BAQEwMDCo1nHS0tIwfPhwuX726OhokR4Mqs08PT1hY2ODYcOGcR2FUUIKV+QKCgqwZ88ermOUoa+vj27duuHq1asoKCiocvvx48cjLS0Nz549k0M60S1YsECkp9KSk5Nx5MgROSSSHgsLC5w+fRpqamrVOs6dO3fk2t2ckJAg03kxlV1xcTHs7e2hpqYGfX19dOnSBd7e3lzHYpSIwhW58ePHIzIykusY5QwcOBAFBQUIDQ0VafvFixdj8uTJsg0lJjMzM4wbN06kbdesWQM+ny/jRNLVq1cvqQyD2LFjB/bt2yeFRFV7/fp1mUfkmf8vMzMTLVq0gKurKyIjI5Geno579+7h5s2b6NSpEzIyMriOyCgDrvtL/6uoqIgGDBhAixYt4jpKGU+ePCEAIq8fx+fzydDQkBITE2WcTDwxMTEiL7R68uRJruOKTSAQ0KRJk6QyI4okS/OIq3v37nTixAmZt6Ns4uLiyNDQkNatW1fuvaioKDp48CA1bNiQQkJC6P379xwkZJSFwhU5IqIDBw6QlZUVXbp0iesoQgKBgMzNzcnCwoIEAoFI+/z888/k5uYm42TiGzFihEgnegcHB5E/qyIpLCwkR0fHahe6Jk2ayPwEam1tLfZcqTXd3bt3SVdXl44ePVrpdiEhIdS0aVPS1NSkffv2yScco3QUssjl5uYKTzSKNBGrr68vARD5pMTn80lPT0/mywWJ6/HjxyKf6OU9Uba0pKamUuPGjatd6Hr37i3TlQhMTEzkttqGMggICCAdHR2RV0oXCAQUHh4unMxg1apV7PfJlKFw9+SAz0/LlZaWYuvWrfDy8uI6jtDAgQMBQOSnLFVUVDBu3Dj4+vrKMpbY2rVrh/79+4u07erVq2WcRjZMTEwQEBCAOnXqVOs4165dk+kAeYFAAA0NDZkdX5n88ccfmDhxIm7fvg0nJyeR9uHxeHB0dIS/vz8SEhJw8+ZNhbsXznCM6ypbmUePHpGOjg7XMYTy8/NJU1OTevbsKfI+RUVFpK+vL8NUkvn7779Fvpq5desW13Eltnfv3mpfzZmbm1NpaalM8rEpvT77/vvvqVGjRmUmzJbEokWL6Ndff5VSKqYmUMgruS/s7e2Rl5enME/51a1bF71798bt27eRmZkp0j7v379H/fr1ZZxMfN27dxf52/KqVatknEZ2fHx8MG3atGod4/Xr1wgJCZFSov+vtLQUPB5P6sdVNqNHj8bZs2cRFxcHExMTiY/z/v17diXHlKPQRU5VVRWzZ8/G77//znUUoQEDBoDP54s8P+X169cVYi7OiixZskSk7YKDgxERESHbMDLk5+eHbt26VesYwcHBUkrz/yUkJEBLS0vqx1UWAoEATk5OSEhIwMuXL6s9nZ+RkRGcnZ3Rrl07TJs2DUFBQVJKyigzhS5yALBu3TrcunULCxYsUIhxMQMGDADweUkgUdy5cwd2dnayjCQxd3d3tG/fXqRtlfXeHABoaGjg9OnT1ZpZ5M6dO1JM9Fl0dDSny0pxqbCwEG3atIGhoSHu3LkDFRXpnIpWrVqF5ORkLFy4EOvWrVOqeVgZ2VD4Isfj8XDixAmYmZmhefPmuHfvHqd5zM3NYWdnh0uXLonUjRoVFQVHR0c5JBMfj8cT+Wru1KlTiImJkXEi2TE1NcXp06ehqqoq0f4REREizXYjjoyMDDRo0ECqx1QG6enpaNGiBfr16wd/f3+pH19NTQ3m5uaoU6eOUk1Px8iGwhc54PNf2jlz5uDs2bPYvn07fvrpJ9y9e5ezPAMGDEBmZqZIGZKSktC7d285pJKMl5cXWrZsWeV2RIS1a9fKIZHs9OjRA+vXr5do35KSEjx69EiqeWpjkYuNjUXr1q0xb948bN68WWbtzJgxA2PHjoWOjo7M2mCUg1IUuS/MzMzg7++P1q1bY/HixcjKyuIkx5ehBKJ0WRYXF8PIyEjWkSSmqqqKxYsXi7TtoUOHkJycLONEsjVnzhyJJ3IWdeiIqLKysmpVkQsPD0eXLl3wxx9/YN68eTJrh4iQmJjI5rhkAChZkbOyssKHDx8watQorFq1ClOmTEFaWhoEAgECAgJQVFQklxxdu3aFvr5+lSe94uJipXh6bsyYMTA3N69yu5KSEmzYsEEOiWSHx+Nh7NixEu27evVqTJo0SWoP4WRlZYm0mG1N4O/vj4EDB+L8+fNSWf+vMl5eXmzFAkZIqYocAOECk46Ojvjuu+/g4uKCFi1awN/fH+7u7hg7diyio6PFOuaNGzewevVqpKeni7S9qqoq+vXrh6ioKLx+/fqr292+fVuhr+K+UFdXx8KFC0XadteuXSItHqvIqvOk5Z49e+Dg4ICePXvixIkTKCkpkfhY2dnZtaI7bcuWLZg6dSru3r2LHj16yLStrVu34ty5c/Dx8ZFpO4wS4XqgnjTdunWLANDNmzfF2m/o0KEEQKzJlI8dO0YAKpxA9ovly5fT8OHDxcrClby8PDIyMhJpcPTSpUu5jlstr1+/rvYA8S8/pqam9Msvv9C7d+/EzjF48OBK//7UBAsWLCBTU1O5TW3n4eFBGRkZcmmLUQ5KdyVXmby8PPTt2xc9e/YUeZ+srCxcuHABPXv2hIWFhcj7DRw4EKampvj555/x/PnzCrdJTU1Vmu6oevXqYc6cOSJtu23bNmRnZ8s2kAw1adIEjRs3lsqx3r59i59++glNmjTB2LFjxXr6NycnB3p6elLJUZXVq1dDT08PTZs2FXtgu0AgwOXLl7Fu3Tqx9hsxYgTOnz+P2NhYufRolJaW1uphGcxXcF1lpSkuLo68vLzE2mfXrl0EgHbu3Cl2e6GhoaSiokJt2rShvLy8cu+/efOG9PX1ZTrBrzRlZWVRw4YNRbqCWbNmDddxq2XYsGFSu5r770+nTp3owIEDVFhYWGmGLl26UEBAgEw/540bN8jMzIy6dOlCb968oStXrpCFhQXZ2dnR06dPv7pfRkYGrVixguzs7EhPT4/s7OzIzs6OunfvXuXfZz6fT927dxdpW2kSCARUr169Kn/vTO1So4rc9OnTaevWrWLt06tXL9LQ0KDMzEyJ2lyxYgUBoEmTJlX4fo8ePWjDhg0SHZsLS5cuFflErsy2bNkisyL35cfQ0JCWLl1KycnJFWaws7Ojv//+WyafLy0tjXr06EGNGjWqcMmq3bt3k5GREbm4uAjniwwLC6Phw4dTo0aNyNjYmDw9PSkoKKhMoRo2bBi1atWKCgoKKmy3oKCAWrZsKfdu+uzsbJo9ezb17t2biouL5do2o9hqVJHr37+/WBPpJiUlEQCxr/7+rbS0lFxcXAgAHTt2rNz7z58/JxMTE4mPL2/v378X+SReUlLCdVyJXb16VeZF7suPqqoqDRs2jMLCwsqsz9eiRQuKiYmR6ufi8/k0Z84c0tHRoWXLllV6JcXn8+nHH38kXV1d0tfXp9atW9PSpUurnCR5zpw5ZGZmRh8+fCjzelpaGjVu3JjmzZsnlc8iqsuXL5OLiwtdu3ZNru0yyqHGFLmSkhICQCtWrBB5n9WrVxMA8vf3r1bbb968IUNDQ2rQoAHFxsaWe9/e3p4OHTpUrTbkycnJSaSTd0VdtMoiPz9f5AdtpPljb29Pf/31F+Xl5VHjxo0pOztbap/p9OnTZGRkRH379hXruHw+X+xuxQ0bNpChoSHFxcUREdHLly9JX1+fNm/eLNZxpGH48OEUHx8v93YZ5VBjilx4eDhZWVlRenq6SNsLBAKysbEhHR2dr3a9iOPSpUsEgNq3b1/unsDff/9NFhYW1W5DXjZt2iTSCfu/3+SVzZo1a+Re5L786OjoUL169SgpKananyM+Pp7s7e3J0tKS7t+/L4XfjGgOHz5Murq6tG3bNtLV1aVTp07Jre1/27dvH3333XfsXhxTIaUvcgKBgObMmUPDhw+nT58+ibzfl9WxJ0+eLLUsixYtIgA0e/bscu+1aNGCrl69KrW2ZOnIkSMinajfvHnDddRqKSkpIU9PT84KHQDS0NCg6dOnS9RtWVRURGPGjCE9PT36/fffZfAbqtq1a9eoSZMmMru3KAo+n09z586lI0eOcJaBUVxKXeQEAgEtXryYVq1aJfa+8+fPJwAUFhYmtTzFxcXUrVs3AlDuqbmZM2fStGnTpNaWLJ08eVKkE3RCQgLXUastPz+fpkyZQjwej9NiB4AcHBzo999/F6mrcceOHaSnp0fffvutVHoilF12djb16NGDdu/eLdUuYEb5KXWRi46OJmNjY4m6KczMzAgAHT58WKwrwKokJiaSjo4O6erqlumK2rFjB/Xt21dq7chSQECASCfliu4/KquHDx/S8OHDSVVVlfNip6WlRdOmTaOoqKhyOR8/fkwtWrQgW1tbqT+0ouxSU1Npz549ZGtrS97e3hX+/pjaR6mLXElJCbm6ukr0lN+IESOEJxVNTU0aPHgwHT16VCoF7+zZswSAHB0dhY8z379/n9q0aVPtY8vDxYsXRToZP3v2jOuoUpeSkkLLli3j5KGUin6cnJzo+PHjlJGRQf379ydDQ0M6evQo178mhZaSkkIHDx6kuXPnch2FUQBKXeT8/f2r9TTXmzdvaMuWLdSjRw9hd5WmpiZ5eXnR0aNHKScnR+Jjz5w5kwDQ4sWLiejz/RNlGUpw5coVkU7Ajx8/5jqqzBQWFtKhQ4eoc+fOnBc6AMTj8ahDhw5SeVClNnj48CGNGjWK6xiMAlDaIpeWlkZOTk4SzRlYkZSUFNqyZQt17969zBXekCFD6NixY2IXvMLCQmrfvj0BEA7GNTY2lkpWWQsNDRXpxPvgwQOuo8rF/fv3ydvbmzQ0NDgvdqqqquTl5UVXr14tM+aOKevAgQNKM28sI1tKWeQCAwOpZ8+eMruSkFbBi42NpQYNGpChoSG9fftWaYpceHi4SCfc8PBwrqPKVVpaGv3222/UuHFjzosdAGrZsiX5+fnRx48fuf7VKJzY2FgCQCEhIWJNEMHUPEpX5DIyMsjMzIwiIyPl0l5KSgr5+flVWPCOHz9eZcH78ji+q6srmZubf3WKJ0Vy//59kU6yN27c4DoqJ0pKSujUqVPUs2dPzgsdAKpXrx5NmjSpRncfS2LgwIFkbm5ea3ocmIopXZE7duwYWVtb0/v37+XednJyMvn5+ZGjo2OZgvfNN9/Q1q1b6fnz5xV2IU2cOJEAkJmZmcwn5JWGiIgIkU6uV65c4Toq5yIjI2nSpElUt25dzosdAOrWrRsdOnSIDYz+P/7+/hINMWJqDqUrckSfp/GR15Xc13wpeN27dycVFRXhScbU1JTGjRtHBw8eFA6WzsvLozZt2hCPxyMfHx9Oc4vi2bNnIp1QAwMDuY6qMDIyMmj9+vXUtGlTzgsdADIyMqLdu3crzQoYslJSUkJdu3blOgbDIR4REZTI6dOncfnyZfz111/g8XhcxwEAfPz4ETdu3MC1a9dw9epVxMTECN9r3bo13NzcYGZmhkWLFkFdXR1v376FgYEBh4krl5iYCEtLyyq3W7JkCVatWiWHRMqDz+cjKCgIv//+u9jrtslC7969ERgYCE1NTa6jcKZ37964du0a1zEYjihdkZs1axZmzpwJKysrrqN81evXr4UF79q1a0hLSyvz/r59+zBhwgRuwomguLgYdevWhUAgqHQ7LS0t5OTkyCmV8nn58iW2bduGAwcOcPp7GjVqFI4cOaIwXwrlbebMmejTpw8GDRrEdRSGA0pX5I4ePQqBQICxY8dyHUUkRISnT58iMDAQt2/fRnh4OFJTU1GnTh2uo1XKysoKcXFxlW7j5uamEFcriu7Tp0/YsWMHli9fjoKCAk4yBAUFwcPDg5O2uZaRkQEDAwOsW7cO5ubmGDlyJNeRGDlS4TqAuHR1dZGdnc11DJHxeDzY2dlhyZIlOHfuHNTV1RW+wAGossABQEJCghySKL+GDRti3Lhx0NDQwC+//MJJL8SuXbvk3qai0NfXx+vXr5GdnY2lS5dCyb7XM9WkdEUuNzcXderUQVZWFn777Tfk5+dzHUlkKioqStNl1KxZsyq3efXqFT5+/CiHNMpvyJAhmDdvHpYtW4bo6GhERUVh/vz50NXVlUv7tf2eVJMmTfDbb7+hRYsWVXbDMzWL0hW5Zs2awdfXF0OGDMHbt2/RpEkT+Pr6Ks23Mx6Ph9LSUq5jVGn+/PkibXfgwAEZJ1F+ISEheP36NX766ScAn/8O2NraYsOGDUhJScGePXvQoUMHmWbIyclhJ3d8/t1/+vSJ6xiMHCldkevQoQNKS0tx/fp1bN++HcnJyWjcuDGGDRuGd+/ecR2vSjo6Onj69CnXMarUpk0bkbY7evSojJMoNyLChAkTcPjw4Qrfr1evHr777js8fPgQ9+7dw/jx42XSnU1ESvHlStZCQ0PB5/O5jsHIkdIVuf+qV68eli1bhl9++QVDhw6Fv78/15Eq1bhxY/zzzz9cx6hSy5YtRdruwYMHMk6i3H7++Wc0b94czs7OVW7buXNn7N+/H2/evMG6detEGsYhKj09PWhoaEjteMrq5MmT2LBhA9cxGDlSuqcrK5OZmQlTU1P8/fff6NixI9dxKjR58mQ0bNgQGzdu5DpKpYhI5Id8atBfIanKyspCs2bNEBMTI9G4SIFAgMuXL2Pbtm0ICgqq1u/Z1tYWUVFREu9fUxARXFxcEBoaynUURk6U/kru39LT01FUVISmTZtyHeWrWrduLdKTi1zj8XgiX80lJSXJOI1yGj58OCZOnCjxwH8VFRV4eHggMDAQ8fHxWLRokcRZGjVqJPG+NQmPx4OmpiYmT56MVatWcTakg5GfGlXkLC0t0a1bNyxfvlxhT7zt2rXD69evuY4hElGLnKJ3EctDaWkp3r9/L/zznTt38PTpU6xdu1Yqx2/atCnWrFkj8YM+pqamUslRE1y6dAlLliyBpqYmPD092VCYGq5GFTl1dXWEhYWhfv36mDt3LtdxKtSxY0d8+PCB6xgiEeU+EsDuywFA165d0bZtWxgbG8PY2Bh9+vTBzp07oaIi3X9imZmZEu3HilxZzZo1w7x587Bjxw5MnDhRYb8UM9VXo4oc8Pk+hqamJs6ePauQY7gaNmyIkpISrmOIZNiwYdDS0qpyO2mfyJXNw4cP8fbtW6SmpiItLQ1paWnIzc2VyTRSqampEu3Huisr1qxZM0yePBk3btzgOgojIzXu7BQTE4NffvkFDg4OchtoW1M1bNgQp0+fhrq6eqXbPXr0SE6JFNOECROwbt06ubT19u1bifZjV3JfZ29vjz179iAlJYXrKIwM1LgiZ2triyZNmlR5YuaSuro6srKyuI4hEnd3d/z9999o27ZtufdsbW0RFBRUq78F37x5E58+fZLbXKqsyElfmzZtMGjQIOzZswfx8fE4f/483r59CzMzMwwaNAjff/899u/fzwbTK6kaV+R4PB5CQkLA4/EUdtCnkZGRUoyV+6Jz5844dOhQudfT09PRr18/mJiYcJBKMUyaNAlbtmyRW3uSFjnWXVm5qVOnomHDhti8eTOOHz+OoUOH4s2bN9ixY4fw4ZS+fftK/PtnuFOjxsn928aNGxEcHIzjx48r3NptgwcPRs+ePUWeOksRCAQCGBkZISMjo8zr0dHRsLa25igVt4KCgjBz5ky8evVKbm02bNhQomV7CgsLlWJicEVSUlJSpkfo8ePH+N///ofjx4/D3Nycw2SMOGrcldwXc+fOhaenJywtLRXukX0rKyu8ePGC6xhiUVFRQc+ePcu9HhYWxkEaxfC///0PO3bskFt72dnZEhU4fX19VuAk8N9bHg4ODli/fj3c3d1rdRe9sqmxRU5FRQUzZ87E9u3bYWtry3WcMmxsbBAfH891DLE5OTmVe622FrmTJ09CU1MTffr0kVubycnJEu3XpEkTKSepvbp3747Zs2crzT11pgYXuS+srKxgbm6uUCtYd+jQQSn79nv16lXutbCwsFo5rdf8+fOxd+9eubYpaZEzMzOTcpLarUGDBjh48CAiIiK4jsKIoMYXOXt7ezRv3hwzZszgOoqQjY2NUn4TbNu2LRo2bFjmtZSUFCQmJnITiCN79uyBnp4eunXrJtd2JS1y7P6RdI0YMQIDBw7E5MmTce/ePa7jMFWo8UWuXr16WLFiBbS1tbmOIqSmpqaUVz+qqqoV3pcLDw/nIA13fvzxxwqfNpU11l2pGNTV1TFx4kSsXr0aM2fOVNinuJnPanyRAz7feA8KCkLLli2Rl5dX5r27d+/C2dkZd+/eRVhYGBwdHfH48WO55FLGcTcVLe4p6SwcymjLli0wNzeHvb293Ntm3ZWKxc3NDf369cOgQYMUcnYl5rNaUeSaNGmC6OhoODg4ICYmpsx7fn5+GDJkCLZt24ajR49i48aNmDFjBkaOHCnT1QK0tLSUcmLYiqYkq1u3LgdJ5E8gEGDlypVfXQBV1lh3peL55ZdfYGJiUutn/VFkalwHkJd//vkHdevWhYODQ5nXly1bhpUrV5ZZ4Xrv3r3Iz8/H8OHDYWhoiC1btqB169ZSzWNqaoqHDx+iefPmUj2urFX0AM9/79PVVKtWrYKNjQ2srKw4aZ91Vyqm2bNnY/78+cjIyMCIESO4jsP8R624kgM+P4ASFxeHixcvIiQkBM+ePQPweQaJgICAMtu2bNkSDg4OuHDhAl6/fl3te04FBQV48OABPn36JHzNwsICT548qdZxuVBRkWvQoAEHSeRLIBDAz88PBw8e5KR9IpK4yDVu3FjKaZh/s7e3R3BwMM6dO4fFixdzHYf5j1pzJaepqYndu3cLxzft2rULWlpaSE5Oxp07dyrcx8zMDIMHD0abNm0kanPBggV48eIFNDU1cebMGZw5cwZGRkawsrJCq1atWJFTIkuWLEGnTp046/rLyMhAYWGh2PuZmJhAQ0NDBomYf1NTU8Phw4fh4eGBoqIiNvhegdSaIgd8vkJbtmyZ8M8PHjxAeHh4pUWsS5cuuHXrFurUqQMnJycMGDAAJ06cqLKt4uJiXL9+XdhX36dPH6xcuRLq6uro1q0bnJ2dceHChep/KDmrjd2VxcXF2LVrF16+fMlZBtZVqfh4PB7y8/PZ05YKptZ0V1akU6dOmDNnDtTUvl7rPT09cf36deTl5aF+/fq4cuUK/vzzTwgEAqSlpZXb/tOnTxgzZgw6d+4MHR0d4eshISF4+PAhZs+ejdOnT6Njx4549+6dLD6WTFVU5ERZc06ZzZ07Fy4uLjAyMuIsAytyymHFihXw8vLC06dPAQAJCQkIDQ1VyiFDNUWtupKTBI/HQ1FREZYtW4YdO3agT58+GDlyJKZPnw7g89WhgYEBeDwe2rZtCx6Ph+HDh+PIkSMV/sXOyspC165dYWpqKlH3E9f+fV/xi5rcXZmfn49jx45xPg0bK3LKwcXFBU2bNsX69evx4sULWFtbQ09PD7t378bmzZthaGjIdcRahxW5KvB4PIwZMwYmJiYYOHAgACAwMBAfP37E27dvERsbCy8vL2RmZmL58uUoKSnB4MGDhfv+19SpUxEaGqqUXZVA7euu9PX1xaBBg8pclXOBFTnlYWlpie3bt5d57datW/D09MSWLVvQqVMnjpLVTjV2qR1FFh8fj8GDB+P9+/dITEyEpqYm15FEpqOjg+zs7DKvlZaWQlVVlaNEspOVlYVmzZrh7du3nP83GjNmTJlhLqI6ceIEe6xdQaSnp8PDwwMBAQHsiVc5qtX35LjSrFkzuLi4IC0tDQcOHEBwcLBSzH4iEAjKXcnVr1+/RhY4AJgwYQK8vb05L3AAu5KrCQwMDLBnzx707t1brmsQ1nbsSo5DW7ZsQVpaGtTU1BAWFoaAgADo6upyHeurbt26VW65HXNzcyQlJXGUSHbevn0LOzs74X8frllaWko0EXZycjKb1kvBhIWFwdnZGSUlJQrxd6umY79hDs2ePVv4/5csWYKAgABMmDABO3fuRIMGDeDo6AgDAwP4+voiPT0dAQEBZabQys7ORnFxsdxuZle0tEyPHj3k0ra8eXt7Y+bMmQpxEhIIBHjz5o3Y+6mqqsLExEQGiZjq6NWrF4YOHYqoqKhyMzAx0sf9v2AGADBp0iRs374dDg4OePnyJbp27YqxY8fCzs4Oc+fOhYmJCZycnPDTTz8hLy8P9+/fx+PHjxEaGoonT57Azs5OpvlycnJw6tSpcq97e3vLtF0uxMbGIioqCiEhIVxHAQC8e/euwjlDq9KoUSOFKNJMeY6Ojnjy5AkrcnLA/gUoiObNm2Pjxo1lXsvOzoa6ujrq1asHAHBwcICjoyPs7e3Rp08faGtrY+3atejUqRPCwsLQvn17qKuryyTfyZMny63gYGZmJteVseVlzJgx+OGHH6Ciohi3rCXtDmYTMyuu7t27Y8+ePRg/fjzXUWo8VuQU2H/XwDMxMcGrV69ARGVOwKtWrcLvv/8Of39/rFq1ClOnThUWRmmpqKtywoQJNe6hk0ePHiE5OblMVzLXJF2UlhU5xVVYWIjCwsJyU4Dl5eWhbt26CvMFqyZgv0klw+Pxyv0DmDdvHg4fPozY2Fjk5OSgX79+Up1hITo6Grdv3y73+oQJE6TWhqIYN24c1q5dy3WMMiQtck2bNpVqDkZ6nJyc0KNHD/Tp0wcPHjwAAKxcuRK9evVCp06dUL9+/TJTEDKSY1dyNYiZmRl++uknaGhowMbGBsOGDcO4ceOqvTTMvn37yr3Wq1cvpVsmqCqhoaH49OkTxo0bx3WUMliRq3l4PB6mTJmCXr164ccff4SJiQnS09MRHBwMfX19EBEcHByQk5MDDw8PuLu7cx1ZabEruRpo8eLFOH/+PDQ1NeHq6gpHR0dYW1vjt99+w969e8W6yistLa1weZnvvvtOmpEVwqRJk/DHH39wHaMcSYuchYWFdIMwUteyZUucPHkSjo6OePz4sfBWhKqqKlauXInjx48Lr/QYybBxcrVAeno6srKyhFd00dHRsLa2FmnfwMBAfPPNN2Vea9CgAVJTU1G/fn2pZ+XKuXPnsHDhwnIrxyuCVq1aITo6Wuz9Xrx4gVatWskgESMvpaWlaN++PcLDw2v0HLGyxK7kagEDAwPExcVh2rRpyMzMFLnAARU/cPLtt9/WqAIHADNnzsTu3bu5jlEOEUn8dCW7klN+ampqmDNnDnbu3Ml1FKXF7snVAkVFRVi/fj3OnDlT7onNynz48AFnz54t97qPj48043HuwIED0NHRKTebiyJIS0uTaLUKY2PjMhMHMMpr3Lhx6NatG+rWrQtra2scOnQIPj4+cHFx4TqaUmBXcrXA0qVLMXPmTLEKHACMHj263GutWrVC165dpRVNISxevLjC+46KgD10wqipqSEwMBBPnjzB33//jRYtWmD58uVKMd+tImBXcjXc7du3kZubK1z+R1QZGRm4ceNGha8TUYXLCCkjgUCAkpIStGvXjusoFWJFjgE+X5n/u8syKSkJz549k/lMRzUBu5KrwYgIK1euxMqVK8XeNzs7G3w+v9zrHz58gJubG1JSUqQRkXNfnmTLz8/nOkqFWJFjKrJgwQIsWLAAL1++5DqKwmNFrga7e/cubG1tYWBgIPa+FhYW6NatW4Xv3bhxA/b29vD3969uRIXQsmVLnDhxgusYFWJFjqlI69at4ebmhtatW+Pjx49cx1ForMjVYAcPHsTEiRMl2ldVVRUBAQHo3bt3he9//PgRw4YNw6RJk5Cbm1udmJxzd3fHuXPnuI5RIfZkJfM1CxcuxJEjR7Bu3Tquoyg0VuRqKIFAgPj4eLGGC/yXkZERrly5grVr13514uc9e/bAwcFBqQesent749GjR1zHqBC7kmMqM2rUKNy4cQNFRUVcR1FYrMjVUM+fP4e9vX21j6OiooLvv/8ed+/e/WrBjIuLg6OjI1atWlXhfTxFZ25ujoKCAoV7Wo2NkWOqQkRITEzEpUuXyq0SwnzGilwN9ddff8HT01Nqx2vfvj0ePXqEKVOmVPh+aWkpfvjhB7i6uuL169dSa1dejI2NcfPmTa5jlPH+/XsUFBSIvZ+RkZHUV6FgFJOKigquXbuGwYMHIz4+nus4CokVuRokNTUVAQEBWLVqFTQ1NaW+anf9+vWxc+dOBAQEQF9fv8Jtbt68CXt7e4V9kONrnJyccPz4ca5jlCHpVRzrqqxdSkpKMHXqVDac4CtYkatBpkyZgtTUVFhaWsp0uRhPT088efIEbm5uFb6fnZ2Nb7/9FuPHj8enT59klkOaxowZo3BXcmxiZkYUd+7cQZMmTbiOobDYYHAlV1hYiAMHDkBXVxcaGhqYNm2aXNo1NTXF5cuX4efnhyVLlqC4uLjcNgcPHsTff/+NI0eOKPwsKd26dUN6ejrXMcp4+PChRPuxK7naIz09HStXrmTj5SrBruSU3MmTJ/H27Vt8+vQJ27Ztk2vbKioqmDdvHu7du4fWrVtXuE18fDx69OiBX375BaWlpXLNJw4VFRXUrVtX4i5CWThz5oxE+1laWko5CaOo6tevD2NjYxgaGiI8PJzrOAqJFTkll56eDmdnZ0yaNAkmJiacZGjXrh0ePnyI6dOnV/g+n8/Hzz//DGdnZ4m74OShY8eOOHDgANcxhDIyMiTaz8zMTMpJGEVVt25d/PzzzygsLISOjg7XcRQSK3JKrLi4GCEhIdVe+Vsa6tWrh23btuHChQswNDSscJvw8HDY29tjwYIFePHihZwTVs3LywtXrlzhOoZQSUmJRPtJ2s3JKKd27dphxIgRaNOmDddRFBIrckqKiDB16lRMnz5dob65Dxw4EE+ePEG/fv0qfD8nJwcbN26EjY0NevTogf379yvM+B5zc3O8evWK6xgAPn+BkfT3cvToUYUb88fIjqamJlRVVbmOobBYkVNSy5cvR6dOncqt2q0ITExMEBQUhC1btqBOnTpf3S48PBw+Pj5o1KgRfH198fDhQ3C1UP3Zs2cxePBghRn6oKGhgbZt20q0b1xcXI2ZV5SpWp06dfD06VOlnIhBHliRU0KHDh1Cbm7uV++BKQIej4dZs2bhwYMHsLW1rXTbnJwc7Ny5E506dYKDgwN+//13uU46u2XLFkyePBl37txRqIVT3d3dJd531qxZEv0Oi4qKsGvXLkyfPh0eHh7w8vLC5s2bFbJ7mflMW1sbNjY2CtMLoXCIURrv37+nBQsW0KRJk6i0tJTrOCIrKCigpUuXkra2NgEQ6adOnTo0evRoun79OvH5fJllmzt3LjVu3JjS0tJk1oaknj17JvLvq6IfHx8fsdo7deoUNW3atMJjqaio0A8//EBFRUUy+rSMpD5+/EgAKD09nesoCokVOSURFRVFrq6udO3aNa6jSCw/P58OHTpEvXr1Eutk3bx5c1q1ahW9fftWqnmGDh1KrVq1ooKCAqkeV5qGDBlSrUIXEhIiUjsnT54U6Xhdu3ZlhU7BLF++nBYtWsR1DIXFuiuVxPHjx7F69Wq4urpyHUVidevWxdixYxEaGoqYmBgsWrQIxsbGVe736tUrLF26FE2aNIGnpyfOnz9frTF3paWl6NKlCz58+IBnz55BU1NT4mPJ2o8//lit/adMmVLlAywPHz6Et7e3SMcbOXIkNDQ0qpWJ+brr169j8eLFyM7OFnmfevXqwdTUVIaplBzXVZap2ps3b6hv374y7bbjSnFxMZ09e5YGDBhAKioqIl+hNGrUiJYsWUJxcXFitZeTk0MtWrSg0aNHy+gTSV///v2rdTU3e/bsrx67tLSUHBwcRDrO1q1b5fehayGBQEA9evSgbdu2kbu7O717945CQ0Pp5cuXFW4fGRlJR48eJVtbW0pISJBvWCXCipwSGDFiBD19+pTrGDKXkpJCv/76K1laWop1Eu/YsSP99ddfVFJSUunx37x5QyYmJrRkyRI5fSLpuH37drWKHI/Ho+vXr1d47J07d4p0jG3btsn5U9ceycnJ5OvrS23btiVbW1sqKiqiJ0+ekJubG7m7u1OfPn3ozz//pJiYmDL7fflv8+rVK46SKwdW5BRcZmYmffvtt1zHkCs+n08hISH07bffkoaGhlgn9OHDh9O+ffsoNTW1zDEjIyNJT0+PduzYwdGnqh4vL69qFTozMzPKzMwsc8zMzEwyMDCocl9luupVRtu3b6dvv/2WfvnllzK9NQKBgPh8PuXl5dGpU6eoZ8+elJWVJXxfX1+fcnJyuIisVFiRU3AlJSU0aNAgrmNwJj09nfz8/MjW1lbsE3v79u3phx9+oM2bN5O2tjZdvHiR648jseTkZGrQoEG1Ct2IESNIIBAIjzlr1qwq9/H29ubwU9cOnz59oo4dO1a53d27d2ngwIE0d+5cunr1KnXq1KnK3guGFTmlMHDgQK4jcE4gENC9e/do8uTJpKWlJfYJvmHDhjRy5Ejav38/vXv3juuPI5E///yzWkUOAO3fv5+IiJ4+fUqqqqqVbmtnZ0ebNm2iY8eOcfzJazY+n0/Ozs70/v17kbY/d+4c9ezZk+7duyfjZDUDK3IKrqioiAYPHsx1DIWSk5NDu3btqtbJvkOHDrRs2TK6c+eO0ow55PP5Yg+/+O9PgwYN6NWrV9S7d2+RtldRUaHIyEiuP3qNN3XqVHr+/DnXMWokHhFH8ygxIhs4cCACAwO5jqFwBAIBAgMDcf78eQQHB+Pt27cSHUdPTw/Ozs7CnzZt2kBFRTFH18TGxsLe3h6FhYVyaW/69OnYtm0biAiZmZlISEhAnTp12CrUUkREcHd3V6jJwWsSxfyXzAD4/Jf/6NGjVU6LVVupqKhg0KBB2L17N1JSUhAREYFVq1ZBW1tbrONkZmbizJkzmDVrFuzt7WFkZIQhQ4Zg69atePLkiUJNdmxlZYUVK1bIpa06derg1atXsLe3R8OGDWFgYIDOnTtLvDoCU7F//vkH7dq14zpGjcWu5BTY3Llz0bBhQyxZskShBywrktGjR+PDhw84deoUrl69iqCgIAQHB+Pdu3cSH1NPTw9OTk7CKz07OztOr/RKS0vRrVs3TpbUmTx5Mv766y+5t1uTLV68GCNHjoSDgwPXUWokVuQU2DfffIMLFy5wHUNpFBcXw8TEBG/fvi3zpUAgECAyMlJY8O7cuVOtqzNdXd0yRc/e3l7uRS86OhoODg4oKCiQW5va2tqIjY396nqBjGQOHTqE9PR0zJ07l+soNRIrcgpswIABuHjxItcxlMb8+fORkJCAM2fOVLrdx48fERISgqCgIFy6dAlpaWnValdHRwddu3aFg4MD2rZtC1tbW1hZWcl8+qudO3fC19dXpm38m5+fH2bPni239mqLT58+wcfHhy2PJCOsyCmouLg4rF27Frt27eI6ilIQCAQwNjZGZGSkWPP4ERFevnyJ0NBQhIaGIiwsrNpFD/h8P6tdu3Zo27at8H/t7e2hpaVV7WN/QUQYPHgwzp8/L7Vjfo2NjQ0iIiKgrq4u87Zqmx07dkBbWxujRo3iOkqNxIqcgpozZw7GjRuH9u3bcx1FKYSEhGDevHmIioqq1nGICNHR0WWKXnXu5/1XixYthEXvy1VfkyZNoKamJtHxPnz4ADs7O6kU5sqEhITAzc1Npm3UVl5eXjh27Bi77y4jrMgpoLy8PAwbNgzBwcFcR1Eay5cvR3R0NI4dOybV4xIRYmJiyhS91NRUqbahqqoKMzMzWFpaomnTpsIfS0tLWFpawtTUFKqqql/d/9KlS/Dw8JBqpn/z8vKqsguYkVz//v0RFBTEdYwaixU5BXT06FEUFRXBx8eH6yhKY8iQIejcuTMWL14s03aICLGxsWWKnqTj80SlpqYGc3NzYeH7dxFs2rQpGjVqhOHDh8ukENWpUwcvXryApaWl1I9d2wkEAqxZswZ5eXlYuXIl13FqLMn6SBiZevbsGUaOHMl1DKUSFxeHOXPmyLwdHo8Ha2trWFtbY8qUKSAivH79GhEREYiIiEBUVBSePn2K6OhoqbVZWlqK+Ph4xMfHV/i+hoYGGjVqBB6PB2l/Z12wYAErcDJQXFwMb29vuLq6YsmSJVzHqdHYlZwC2rBhAzp06AAXFxeuoyiNRo0aISEhQWHua2RnZ+PJkyeIjIxEREQEIiMj8fTpU7nNVCINjRs3RnR0NOrXr891lBpnxYoVsLKywujRo7mOUuOxKzkFZG9vjydPnrAiJwaBQKAwBQ74PKasZ8+e6Nmzp/C10tJSxMTECIteVFQU4uPjkZiYiKKiIg7TVmzDhg2swMnA0aNH8fbtW/z0009cR6kV2JWcAnr//j0WLVqEffv2cR1FKRQWFsLS0lLqD4TIi0AgwLt375CYmIjExEQkJCQI/39iYiKSkpLkPpVW3759cenSJfB4PLm2W5NlZWVhwYIF0NXVxerVqyV+opYRD/stKxiBQIAHDx4o1FWJojtx4gSMjIy4jiExFRUVmJqawtTUFI6OjuXe5/P5ePv2bZnC96UQJiQkIDk5GXw+X2p5bGxscOLECVbgqiErKwtXrlxBUVER6tSpg5ycHBw5cgS//vorunfvznW8WoVdySmQ5ORkTJgwAU5OTsJ5K5mv27dvH1asWAFVVVXs2bMHzs7OXEfiRGlpKd68eYOEhASsWLECoaGhEh/LxsYGQUFBsLCwkF7AWqKoqAilpaU4cuQITp06hdGjR6N+/fooLi6GqqoqPD09Ua9ePa5j1jqsyCmQxYsXY+jQoejUqRPXURRWaWkpVqxYgb/++gtmZmb4448/0K1bN65jKYz4+Hh06tQJmZmZYu2no6ODRYsWYd68eTKfjqymyczMxJQpUyAQCKCmpoa+ffvCx8en0rGNjPyw7koFkpycjObNm3MdQyGVlpbC19cXZ86cQZcuXXD//n12tVGBZs2a4dSpU+jbt69IXZjW1taYPXs2xo8fzx4yEVNmZibmzZuHjx8/YtmyZejYsSPXkZgKsCKnIIgIGRkZ0NPT4zqKwnn48CG++eYbdO3aFYmJiawbtwqurq64desW5s6di3v37lW4jZubG+bMmQMPDw+FXSBW0f3www+YPHkyu8em4FiRUxARERGwt7fnOobCmTdvHg4cOIADBw5g4MCBXMdRGt26dcOdO3dw6dIlREREIC4uDqqqqujRowecnJzQtGlTriMqtRMnTkBFRYUVOCXAipyCCAoKwoABA7iOoTCSkpLQu3dvGBsbIykpSaqz99cWPB4PHh4eMp3XsrYhIly9ehUnT57EiRMnuI7DiID1UyiAf/75B9euXWPfCv/P+vXr0a5dOyxcuBDh4eGswDGcOnr0KDw9PdG/f38MGjQIV65cwYEDB9g4NyXBnq7kWElJCdzd3eHv7w9dXV2u43Du8uXLGDduHCIjI2FiYsJ1HKaWIyK4u7sjKCiIFTUlxa7kOHbo0CEMHz6cFbj/s2XLFsyfP58VOEYhHDt2DC4uLqzAKTF2JcehqKgoLFmyBGfPnmUrLv8fIyMjJCYmskGzDKeISLgayI0bN5R6Rp3ajn094dDixYuxf/9+VuD+T0REBBo0aMAKHMO53377Da9fv8bx48dZgVNyrMhxhIjA5/NhaGjIdRSFsWHDBgwePJjrGEwtlpycjL179+L69esICQlhs7/UAKzIcWTChAkYNWoU1zEUyvXr1/Hw4UOuYzC12IkTJ1BQUICFCxeyAldDsCLHkczMTIwfP57rGAqjuLgYxcXFMDU15ToKU4vdv38fe/fuZcNWahD2dCUHsrOz2TIm/6GhoQEVFRV8+vSJ6yhMLWZjY4Nnz55xHYORIlbkOHD9+nU2RVUFevbsibVr13Idg6nFRo0ahZUrV6KwsJDrKIyUsCLHAR0dHeTm5nIdQ+EsW7YMR48e5ToGU4u1bNkSM2bMgJeXF9LS0riOw0gBK3IcUFNTQ1FREdcxFE67du2Qn5+P9+/fcx2FqcX69u2LTZs2YdSoUexBqBqAFTkOPHnyhK0b9xXu7u5YuXIl1zGYWq5169Y4ffo0fvnlF4SEhHAdh6kGNuMJBzIzMzFs2DCcPHkSBgYGXMdRKLGxsXB1dUVycjLXURgGhYWFGDBgAAIDA1G3bl2u4zASYFdyHNDT04Ofnx+mTJkC9h2jLCsrK/D5fCQlJXEdhWGgqakJd3d33Llzh+sojIRYkeOIvb09WrdujfDwcK6jKBxPT0/8+uuvXMdgGACAs7MzwsLCuI7BSIh1V3Lo2rVriIiIwPz587mOolBSUlLQqVMnpKamch2FYVBaWgo3NzcMHDgQdnZ2cHd35zoSIwZ2Jcchf39/9OjRg+sYCsfMzAzq6upsUC6jENTU1HDu3Dl07NgRP/74I/744w+uIzFiYFdyHNm5cyfi4+PZ4OevWLhwIVJSUnDs2DGuozCMUEhICMaPH49Xr16xB1GUBCtyHEhPT8d3332Hc+fOsem9viIzMxOtW7dmA3IZhXPq1Ck8ffoUK1as4DoKIwLWXcmB9PR0tGzZkhW4Sujp6UFLSwsPHjzgOgrDlDFs2DAkJCQgMjKS6yiMCFiR40BeXh7XEZSCt7c3fvvtN65jMEwZPB4Py5cvx4YNG7iOwoiAFTk5e/nyJb7//nvMmjWL6ygKb8GCBbh9+zbXMRimnGbNmkFTUxP37t3jOgpTBVbk5Cw8PBzTp09HkyZNuI6i8LS0tGBgYIBr165xHYVhylm7di2WLVvGJnRQcKzIyVmXLl3Y1YkYJk2ahDVr1nAdg2HKUVNTQ2FhIStyCo4VOTnLyspCvXr1uI6hNGbOnIlHjx5xHYNhyvnhhx+watUqqKiw06giY/915Gzjxo2YMWMG1zGUhoaGBpo0aYKzZ89yHYVhhD59+oRXr16xyRyUACtycpSWlgZNTU0YGxtzHUWpTJ8+HRs3buQ6BsMA+DzN18yZMzFv3jyuozAiYEVOjnbv3o0JEyZwHUNsL1++xO7duxEXF4fCwkK5tz9p0iS8ePECAoFA7m0zzH/NmjULZmZmcHNz4zoKIwJW5OTozp076NOnD9cxxHbkyBFMnjwZ//vf/+Ds7IxRo0bBy8sLfD5fLu2rqKjAysoKhw8flkt7DFOZP//8k61er0RYkZMjCwsLxMXFcR1DJOHh4fjtt9/g6emJVatWQUtLC8eOHcPHjx+RmJiIrKwsTJs2TW555s6di61bt8qtPYapSElJCaytrZXyy2ptxeaulKPQ0FDcunULy5Yt4zpKpTZt2oRr166hZcuWGD58OOzs7KCuro46deqgSZMmSElJQUpKCtq3b4/Bgwdj8ODB8PDwkGkmgUAAQ0NDpKWlQU1NTaZtMYqPiJCdnQ0dHR25tvvzzz8jKysLmzZtgqqqKgCAz+cL/z+jeNiVnBx16NABT5484TpGpW7duoUbN24gMDAQmzZtQrdu3aClpYU6deoAAI4dO4YnT56gcePGGDhwIJo1a4b+/fvLvPtGRUUFrVu3xqFDh2TaDqMctm/fDisrK3z48EGu7d69exfu7u5ITk6GQCDA0aNHYWBgAFdXV8TGxso1CyMaVuTkpLS0FD/88AOGDRvGdZRKnTp1ChMnTvzq5NE9evSAnZ0dAGDPnj24f/8+/Pz8oKurCz6fDwcHB5kudlq/fn2ZHZtRDrGxsTh//jxsbW2hpaUl17aPHDkCf39/LFiwAG5ubggJCUF8fDxmzZoFX19f5ObmyjUPUzXW7yMns2bNgqOjI0aOHMl1lAoREXbt2oXff/8d48ePr3L7hIQEqKioIDMzE3l5ecjIyECjRo0AAGPGjEFQUBA0NTWlmjE1NRWdOnWS6jEZ5XH79m38+eefSExMxLBhw/D+/Xvhmm5ZWVkoLS2FgYEBgM+ry38ZxybNrkQDAwPs2bOn3OuDBw+GiooKnJ2d8fDhQ6m1x1Qfu5KTkzp16sDe3p7rGF+1Zs0aBAUFIT8/Hx06dKhy+2bNmqFly5ZQU1NDSEgIHj9+jAYNGuDq1au4ceMGjI2NsWrVKqlOeZSbmwsLCwupHY9RHgKBAN988w28vb1x69YtqKiooHnz5li0aBH69esHZ2dnWFhYwNvbG3379sXy5csxbdo03Llz56vHzMzMxOPHj7F37144OjrCyckJ06dPx9atW5GZmSl29+OgQYOgp6dX3Y/KSBkrcnJiZmaGjIwMrmN8VePGjaGrqyvSasfR0dEAPhfuq1evIjU1FX///TcuX74MNzc3pKen49OnT/jhhx+Qnp5ebv+SkhLEx8dLtOQQm0KpdvpyldSsWTMAwHfffYczZ86gXbt2CA4ORkREBHJzczFlyhScPXsWu3fvRv/+/XH+/PkKj3f8+HF4eHhg4cKFCAsLQ2hoKM6fP48pU6YgLi4O+vr6WL16tdg5mzRpglu3blXrszJSRoxc3L9/n7y9vUkgEHAdpULTp0+n2bNnl3s9MzOTduzYQW/evKHvv/+e+Hw+lZSU0OHDhyknJ4fevXtHxcXFdO3aNTIyMqI5c+YQEdH8+fNp0qRJ5Y53+fJlcnV1pZkzZ9KQIUNowIABNHXqVPr7778rzVdUVEQmJiZS+ayMckpOTiYvLy+RtxcIBNSnTx9KTEws8zqfzydXV1cqLCyscL+UlBRq0KABZWZmip0xIiKCxo4dK/Z+jOywIidHO3fuJG9vbyoqKuI6Sjnx8fHk4+NDo0aNohkzZlBJSQnFxsYSABo0aBB17tyZrK2tadGiRV89Rm5uLmVnZwv/7OHhQW/fviWBQEBRUVE0YsQI+v777+nTp0/CbQoKCighIYGGDRtGnTt3Jm9vb4qPj6d3797Rhw8fKDMzkwoLC+nhw4fUunVrmf4OGMX33XffUUREhMjbx8TEkKurK4WGhtLr169p/fr1tHr1apo3b16l+61atYr69etHr169EitfWFgYK3IKho2Tk7O//voLampq+O6777iOUg4R4datW+jVqxcAoE2bNrCxscGJEyeQnp4OIyMjAJ+fFBXlZv7atWtx+vRpGBgYoEWLFpgxYwZatmz51e3z8vJw7tw5jBkzBhMnToRAIEBpaSkyMzORnp6OBg0aICQkRDofllFKkZGROHr0KNauXSvyPtnZ2XBzc4O+vj5mzJiBrKwsODs7w8zMrNL9Hj16hN69e+PMmTNwcXGpsp28vDy4urriypUr0NbWFjkfI1vs6Uo5Gzx4MEaOHIkhQ4bIZSBrYWEhduzYgT59+qBly5ZQUVH56n0tHo8HJycnhIaGwsbGBkZGRkhOTsa5c+fQtm1bAMCFCxdEflpt0aJFWLRokchZ69evj9GjR2P06NHl3jtz5gzWrVuHEydOYMSIEV8d4sAoDyJCUlISPnz4gPDwcAQFBeHFixeYPHkyPn36hMaNG6Nfv37g8Xho0qQJDhw4gKZNm+Lp06ditaOtrY379++L/XcmLi4ONjY2wjGiVbl48SI6dOjACpyCYXfx5czIyAhjx47FqVOnkJ+fj8WLF2P+/Plo06aN1NsqLi7GnDlzoKGhgR49eqBXr15wcHDAlClTUFpa+tX9Tp8+DWtrazg6OuLBgwfw9vbGjRs30KVLFwwcOFDqOUUxZMgQhIWFIT4+Hv3796/0qTlGsYWHh2Pt2rVwdnbGsmXLcP78eRgaGuLHH3/E9evXUVxcjH79+kFdXR3bt2/Hr7/+Ci8vL3z69AkDBgyQaBiJJF+Kdu7ciZ07d8LR0bHKbWNjYzF37txKeyoYjnDbW1o73bt3jxo0aEADBgyg9evXEwA6cuRIue1iY2PJw8ODfv31VxowYABNnjyZ8vLyqjx+amoqLVy4kDp37kzbt28ngUBAERERJBAIqLS0lL755hsqKCiocN+tW7dSixYtqKSkRPiah4cHAaBt27ZJ/qGlKC0tjaZNm0ZTpkyhjx8/ch2H+T/BwcF0+PBhEggE9PjxY7pw4QKFhITQ/v376c8//6SIiAhKSUmhli1b0sWLF6m4uFis4wsEgjJ/L2WppKSEdHR0RH5QLCUlhfT19Sk/P1/GyRhxsSs5DnTu3BlXr17FyJEjhV2WP//8c7nttm/fjkmTJqFdu3YICAjAoEGDsGbNmkqPffHiRfj4+EBbWxv37t3DtGnTwOPx0LZtW/B4PKiqqmLChAkYNWoUioqKQER49+4drly5AiJCr169EBcXJ+yeBIATJ04gKioK06dPl+rvQVJGRkbYvn07vL29MXToUFy+fFmi4xw6dAjW1tZwcnLCgwcPpJzy6woKChAfH49Lly5VekWtTMaNGwcPDw8cOXIEHTt2xK5du/DmzRtERUVBVVUVOjo6OHbsGCZMmABPT0/0798f6urqYrXB4/HkNm+pmpoaJk6ciD/++AMAqlxxo0GDBmjQoAFbDkoBsQdPOBQYGIhvvvkG+/btwzfffAN9fX0QES5fvoyAgABoaGjAz89PeA8tMjIS+/fvx88//4xff/0VzZs3h5aWFlxcXLB161bcu3cP9evXx5YtW2BtbV1p28HBwdi5cyeKi4sRHR2NoUOHIiIiAnw+H3Z2dhg7diw6duwoj19DtRQWFsLX1xcdOnTAzJkzRd7v3bt3sLCwQHx8PPLz8zF06FC5zSvq7OwMIsLNmzcRFxeH5s2bC9/j8/lITU2Fqamp0owJjImJwbx583DmzBloaGhwHUdqSkpKMGfOHLx8+VI4cPxrvv/+e9jZ2cHb21uOCRlRsCLHsS1btiAyMhLu7u44dOgQeDweHBwc8O2338LGxqbMtgUFBfjuu+/w4sULTJw4Ec2bN0dSUhIePXqEQYMGoUuXLnj37p3YM6uUlJRAXV0dJSUlUFNTU7qHOogIq1atQlxcHLZs2YKGDRtWuc/8+fNRr149/Prrr8jOzsagQYMQFhYmh7Sf5wcdMWIEvL29cfDgwTLvff/99zh//jzq1asHNzc3qKqqok2bNhg7dqxcskli4sSJmDlzJtq1a8d1FJkZP348cnJysGbNmgq/QIaGhmLKlCmIiYnhIB1TKc46Shki+nyfITAwkHbs2EFZWVki7cPn82WcSjmFhYWRq6sr7dy5kyIjIyvd1trampKTk4mI6Ny5c8Tj8ejmzZu0f/9+mj9/Pr19+5aIPo9tjI+Pl3pWANSuXTu6cuUKFRUVUWlpKRUWFlLbtm2Jz+fT+/fvKSoqioKDg0lbW5t27dpFf//9d7l7qQKBgNLS0ujDhw9SzyiqYcOGUVpaGmfty8uVK1eoR48eFd53EwgE5OLiwkEqpiqsyDE1SnZ2Np05c4Y6depU7r309HQ6ceIE+fj4UP/+/YWv8/l8unPnDnl6ehIAWrduHXXp0oVMTEwIAD169EiqGd+8eUPe3t6UlZVFP/74Iw0aNIgGDRpE9vb2FT7ck5aWRlu2bKH58+dT69atSSAQ0MOHD8nX15csLS1p5MiR1LZtW7Ef5BBHXl4evX79usxrfD6fYmJiqF27dpSRkSGzthVJSEgIubi40NmzZ0kgEAi/XAQHB5OVlRXH6ZiKsO5Kpkb65ptvcPbsWdy/fx+XLl3Cw4cPoa2tDTc3N/Tt2xdNmjSpcL9nz56hTZs2iImJgZmZGWbNmoWOHTvC19dXatlCQkJw7949/Pjjj2LvO2vWLBw9ehSGhoZYs2YNOnXqhEaNGmHatGnYuXOnVCfE/iI/Px/9+/fHvXv30KtXL/B4PHz8+BF169ZF48aNMW3aNHTv3l3q7Sqqjx8/YvXq1bh58ybu3buHRYsWITg4GFeuXIGxsTHX8Zj/YEWOqZFat26NZs2aoXPnznB3d0enTp0kWnIlPz8fbdq0we7du9G7d2+pZFuwYAH69+8PV1dXifYvKioqM0DZy8sLNjY2mDRpEiwtLaWS8d+mTZuG3r17w9PTE+rq6vj48SOIqNbPuP/hwwfk5+dj+PDh8PPzE2k8HSN/rMgxTBU2bdqElJQUFBQUwMjICCtWrKjW8fz9/fHnn3/Cz88Ptra21c7XqVMn9OjRA7GxsejYsSOWL19e7WN+sWbNGsTFxWHXrl1K90ASwwCsyDFMlYqLi3Hw4EE4ODhg3LhxuHPnjkhPcH4NESEoKAiLFy9GVFRUtfM9fvwYeXl50NfXx7Bhw/D06VOpFaSNGzdi06ZNuHHjRpXDUhhGEbEixzBiuHDhAry9vdGrVy+cO3dO4uPk5+dj4MCBuH79uhTTfZ4A/P79+9W68hIIBJg2bRoKCgqQn5+PBw8eoFu3bjh+/LhUszKMPLAixzBiSklJwfDhw3H79u1qFZJOnTrh4cOHUu8G3LhxI0pLS6ucHLu4uBhHjx7Fp0+fhPOcRkREYO3atWjatCnGjh2L+vXro6ioCABkMr8qw8iackypwDAKxMzMDE2aNMHt27clPsa5c+fA5/NRWFgoxWSfTZs2DXv37kVJSUmF7z99+hSjRo1Cv379kJKSAhUVFaxatQq5ubnw8fGBr68v1q9fj7Zt26JFixZo06YNK3CM0mJL7TCMmHr16gVNTU1069ZNov1DQkKwfv16hISEoG7dulJOB1y9ehW9e/eucG7I8+fP4/fff8euXbvQtGlTAIC3tzdmzJiBBw8ewNDQUGpPkTKMImBFjmHE1KpVK7Rt27bM3JJ3794VTqdWr169Svf/888/sWXLFhgaGsokn62tLbZu3Qo3NzdkZGRAVVUV+vr6AD4v7PnvAgcAderUAREhPDwce/fulUkmhuEKuyfHMGKKjIzEoUOHMGfOHJiamuLQoUOYMGECLCwskJSUhMePH1c4j2Nubi527dqFefPm4fbt2xJfCTIMIzp2JccwYrK3t8eNGzdw/vx5mJmZISsrC1FRUWjevDnGjBnz1QdJ1q9fDy0tLeTk5KB+/fpyTs0wtRMrcgwjJh6PB1tbW0ydOhWdO3eGiooKVFRUcP36dWhra5dZi++Lt2/fwt/fX7gcEsMw8sG6KxlGSrp06QJ/f3+YmZmVeV0gEMDFxQV//vlnueWTGIaRLTaEgGGk5NWrVxXOhLJ371706dOHFTiG4QDrrmQYKbG0tMQ///wDFxcXAMDz58+xceNG3LlzByEhIRynY5jaiV3JMUw1ffr0CXp6ehg4cCBcXFxQXFyM5cuXY+TIkRg3bhzCw8PRuHFjrmMyTK3EihzDVNPDhw/RoEEDJCUl4X//+x+0tbVBRAgNDUWvXr2gq6vLdUSGqbXYgycMU01ZWVnw9/dHt27d8ObNG9ja2qJRo0Zcx2IYBqzIMQzDMDUY665kGIZhaixW5BiGYZgaixU5hmEYpsZiRY5hGIapsViRYxiGYWosVuQYhmGYGosVOYZhGKbGYkWOYRiGqbFYkWMYhmFqLFbkGIZhmBqLFTmGYRimxmJFjmEYhqmxWJFjGIZhaixW5BiGYZgaixU5hmEYpsZiRY5hGIapsViRYxiGYWosVuQYhmGYGosVOYZhGKbGYkWOYRiGqbFYkWMYhmFqLFbkGIZhmBqLFTmGYRimxmJFjmEYhqmxWJFjGIZhaixW5BiGYZgaixU5hmEYpsZiRY5hGIapsViRYxiGYWosVuQYhmGYGosVOYZhGKbGYkWOYRiGqbFYkWMYhmFqLFbkGIZhmBrr/wErwrNqDtVtIAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transFilePath = os.path.join(\n", + " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"transmissionPipeline.shp\"\n", + ")\n", + "\n", + "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", + "fig, ax = fn.plotTransmission(\n", + " esM, \"Pipelines (biogas)\", transFilePath, loc0=\"loc1\", loc1=\"loc2\", fig=fig, ax=ax\n", + ")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "interpreter": { + "hash": "8e874521e84aaa9a25c57584b09ac89843ebb84ca57251a2b3336b66b8670439" + }, + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Multi-regional_Energy_System_Workflow/Performance_Summary_Example.ipynb b/examples/03_Multi-regional_Energy_System_Workflow/03b_Performance_Summary_Example.ipynb similarity index 70% rename from examples/Multi-regional_Energy_System_Workflow/Performance_Summary_Example.ipynb rename to examples/03_Multi-regional_Energy_System_Workflow/03b_Performance_Summary_Example.ipynb index c8b7e8c7..6ddfe5f2 100644 --- a/examples/Multi-regional_Energy_System_Workflow/Performance_Summary_Example.ipynb +++ b/examples/03_Multi-regional_Energy_System_Workflow/03b_Performance_Summary_Example.ipynb @@ -6,6 +6,9 @@ "source": [ "# Performance Summary Usage \n", "\n", + "This example shows how to integrate the performance summary to the optimization to have more information how your optimization performs.\n", + "The summary is created and added to the energy system model (esM) instance if the boolean is set to True in the optimize()-method. \n", + "\n", "**Performance Summary includes:**\n", "- RAM usage\n", "- FINE parameters\n", @@ -37,6 +40,14 @@ "## Load Multi-regional Energy System Example" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To show the functionality of the performance summary, the example of the multi-regional Energy System workflow is used. \n", + "The following code gets the input data and sets up the esM instance. " + ] + }, { "cell_type": "code", "execution_count": 2, @@ -405,25 +416,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "__Description__\n", - "\n", "The function esM.optimizeAndIncludePerformanceSummary is a wrapper around esM.optimize and additionally stores a performance summary (in Dataframe format) as attribute ('esM.performanceSummary') in the esM instance.\n", "\n", "__Usage__\n", "\n", - "Replace *esM.optimize(...)* with *esM.optimizeAndIncludePerformanceSummary(...)* to get a performance summary after optimization. Arguments do not have to be changed.\n", + "Set the boolean *includePerformanceSummary* in *esM.optimize(...)* to True to get a performance summary after optimization. There is no need to change the other arguments.\n", "\n", "__Notes__\n", "\n", "- the following packages are required: psutil, grblogtools\n", "\n", - "- if TSA is used, *storeTSAinstance* should be set to True. Only then the TSA parameters are saved in the ESM instance\n", - "- a log file name should be specified. logFileName='xyz.log' This log file is used by Gurobi to store the log and later used to extract relevant Gurobi parameters\n" + "- if time series aggregation (TSA) is used, *storeTSAinstance* should be set to True so that the TSA parameters are saved in the ESM instance\n", + "- a log file name should be specified. This log file is used by Gurobi to store the log and later used to extract relevant Gurobi parameters\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": true, "tags": [ @@ -436,187 +445,206 @@ "output_type": "stream", "text": [ "\n", - "Clustering time series data with 2 typical periods and 24 time steps per period...\n", - "\t\t(0.7781 sec)\n", + "Clustering time series data with 2 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(5.1264 sec)\n", "\n", "Time series aggregation specifications:\n", - "Number of typical periods:2, number of time steps per period:24\n", + "Number of typical periods:2, number of time steps per period:24, number of segments per period:12\n", "\n", "Declaring sets, variables and constraints for SourceSinkModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.1040 sec)\n", + "\t\t(0.1490 sec)\n", "\n", "Declaring sets, variables and constraints for ConversionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0329 sec)\n", + "\t\t(0.0563 sec)\n", "\n", "Declaring sets, variables and constraints for StorageModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(1.1790 sec)\n", + "\t\t(1.3888 sec)\n", "\n", "Declaring sets, variables and constraints for TransmissionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0736 sec)\n", + "\t\t(0.1051 sec)\n", "\n", "Declaring shared potential constraint...\n", - "\t\t(0.0016 sec)\n", + "\t\t(0.0020 sec)\n", "\n", "Declaring linked component quantity constraint...\n", "\t\t(0.0000 sec)\n", "\n", "Declaring commodity balances...\n", - "\t\t(0.1296 sec)\n", + "\t\t(0.0983 sec)\n", "\n", - "\t\t(0.0002 sec)\n", + "\t\t(0.0000 sec)\n", "\n", "Declaring objective function...\n", - "\t\t(0.0580 sec)\n", + "\t\t(0.8590 sec)\n", "\n", "Either solver not selected or specified solver not available.gurobi is set as solver.\n", - "Set parameter ServerPassword\n", - "Set parameter TSPort to value 41955\n", - "Set parameter GURO_PAR_SPECIAL\n", - "Set parameter TokenServer to value \"192.168.91.1\"\n", - "Read LP format model from file /tmp/tmpf9hjemy8.pyomo.lp\n", - "Reading time = 0.09 seconds\n", - "x34531: 44417 rows, 22723 columns, 130996 nonzeros\n", - "Set parameter QCPDual to value 1\n", - "Set parameter Threads to value 3\n", - "Set parameter LogFile to value \"run.log\"\n", - "Set parameter OptimalityTol to value 0.001\n", - "Set parameter Method to value 2\n", - "Set parameter Cuts to value 0\n", - "Set parameter MIPGap to value 0.005\n", - "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (linux64)\n", - "Thread count: 36 physical cores, 72 logical processors, using up to 3 threads\n", - "Optimize a model with 44417 rows, 22723 columns and 130996 nonzeros\n", - "Model fingerprint: 0xabe2dc66\n", - "Variable types: 22671 continuous, 52 integer (52 binary)\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", + "Academic license - for non-commercial use only\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp18vfvg5m.pyomo.lp\n", + "Reading time = 0.10 seconds\n", + "x1: 44997 rows, 16580 columns, 122638 nonzeros\n", + "Changed value of parameter QCPDual to 1\n", + " Prev: 0 Min: 0 Max: 1 Default: 0\n", + "Changed value of parameter Threads to 3\n", + " Prev: 0 Min: 0 Max: 1024 Default: 0\n", + "Changed value of parameter logfile to run.log\n", + " Prev: Default: \n", + "Changed value of parameter OptimalityTol to 0.001\n", + " Prev: 1e-06 Min: 1e-09 Max: 0.01 Default: 1e-06\n", + "Changed value of parameter method to 2\n", + " Prev: -1 Min: -1 Max: 5 Default: -1\n", + "Changed value of parameter cuts to 0\n", + " Prev: -1 Min: -1 Max: 3 Default: -1\n", + "Changed value of parameter MIPGap to 0.005\n", + " Prev: 0.0001 Min: 0.0 Max: inf Default: 0.0001\n", + "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", + "Optimize a model with 44997 rows, 16580 columns and 122638 nonzeros\n", + "Model fingerprint: 0xca5ad7cf\n", + "Variable types: 16528 continuous, 52 integer (52 binary)\n", "Coefficient statistics:\n", - " Matrix range [1e-05, 5e+02]\n", + " Matrix range [7e-07, 5e+02]\n", " Objective range [1e-05, 3e-01]\n", - " Bounds range [1e+00, 1e+05]\n", + " Bounds range [4e-02, 1e+05]\n", " RHS range [4e-02, 3e+02]\n", - "Presolve removed 11680 rows and 8487 columns\n", - "Presolve time: 0.17s\n", - "Presolved: 32737 rows, 14236 columns, 109458 nonzeros\n", - "Variable types: 14210 continuous, 26 integer (26 binary)\n", + "Presolve removed 18005 rows and 7809 columns\n", + "Presolve time: 0.12s\n", + "Presolved: 26992 rows, 8771 columns, 89294 nonzeros\n", + "Variable types: 8745 continuous, 26 integer (26 binary)\n", "Root barrier log...\n", "\n", - "Ordering time: 0.12s\n", + "Ordering time: 0.01s\n", "\n", "Barrier statistics:\n", - " Dense cols : 176\n", - " Free vars : 368\n", - " AA' NZ : 2.423e+05\n", - " Factor NZ : 1.072e+06 (roughly 30 MB of memory)\n", - " Factor Ops : 1.697e+08 (less than 1 second per iteration)\n", + " Dense cols : 168\n", + " Free vars : 176\n", + " AA' NZ : 1.743e+05\n", + " Factor NZ : 6.563e+05 (roughly 20 MBytes of memory)\n", + " Factor Ops : 7.640e+07 (less than 1 second per iteration)\n", " Threads : 3\n", "\n", " Objective Residual\n", "Iter Primal Dual Primal Dual Compl Time\n", - " 0 6.13104242e+05 -4.23262089e+06 1.27e+06 4.00e-02 5.67e+03 0s\n", - " 1 4.67467179e+05 -3.30877832e+06 7.56e+05 2.38e-01 2.82e+03 0s\n", - " 2 2.40783031e+05 -2.98921420e+06 3.44e+05 1.54e-01 1.41e+03 0s\n", - " 3 1.23240859e+05 -1.47850638e+06 1.31e+05 5.65e-02 4.59e+02 1s\n", - " 4 3.61421735e+04 -8.00787527e+05 8.75e+03 5.95e-02 4.51e+01 1s\n", - " 5 3.02702121e+04 -3.28754883e+05 6.27e+03 5.52e-02 2.46e+01 1s\n", - " 6 8.11729373e+03 -1.64039018e+05 6.52e+02 8.80e-03 5.30e+00 1s\n", - " 7 2.62454397e+03 -3.29796833e+04 4.67e+01 3.35e-04 8.06e-01 1s\n", - " 8 1.01367062e+03 -6.56716578e+03 4.63e+00 6.03e-05 1.53e-01 1s\n", - " 9 5.03675338e+02 -4.18543908e+03 2.42e-02 3.79e-05 9.20e-02 1s\n", - " 10 2.66790160e+02 -1.86380635e+03 3.87e-03 1.66e-05 4.14e-02 1s\n", - " 11 1.45008767e+02 -9.49462918e+02 1.35e-03 8.26e-06 2.12e-02 1s\n", - " 12 8.68022297e+01 -2.16848705e+02 3.03e-04 3.92e-06 5.88e-03 1s\n", - " 13 7.00861359e+01 -7.22992850e+01 1.20e-04 4.06e-06 2.76e-03 1s\n", - " 14 5.85698126e+01 -1.09302304e+01 5.06e-05 1.77e-06 1.35e-03 1s\n", - " 15 5.17171108e+01 2.27108619e+01 2.43e-05 1.95e-06 5.62e-04 1s\n", - " 16 4.62233328e+01 2.67970876e+01 1.07e-05 1.33e-06 3.76e-04 1s\n", - " 17 4.34155599e+01 3.07557429e+01 5.06e-06 5.00e-06 2.45e-04 1s\n", - " 18 4.23830337e+01 3.33790161e+01 3.34e-06 3.51e-06 1.74e-04 1s\n", - " 19 4.13926997e+01 3.58968892e+01 1.93e-06 2.15e-06 1.06e-04 1s\n", - " 20 4.09395506e+01 3.78436565e+01 1.40e-06 1.05e-06 5.99e-05 1s\n", - " 21 4.03890120e+01 3.84222529e+01 8.23e-07 6.73e-07 3.81e-05 1s\n", - " 22 4.00187576e+01 3.86544228e+01 4.90e-07 7.27e-07 2.64e-05 1s\n", - " 23 3.99135897e+01 3.88187316e+01 3.95e-07 5.56e-07 2.12e-05 1s\n", - " 24 3.97048866e+01 3.90933315e+01 2.08e-07 4.10e-07 1.18e-05 1s\n", - " 25 3.96083725e+01 3.92306433e+01 1.34e-07 4.69e-07 7.31e-06 1s\n", - " 26 3.95799273e+01 3.93204805e+01 1.13e-07 3.04e-07 5.02e-06 1s\n", - " 27 3.95385949e+01 3.93350927e+01 8.21e-08 3.11e-07 3.94e-06 1s\n", - " 28 3.94883898e+01 3.93580879e+01 4.63e-08 2.88e-07 2.52e-06 1s\n", - " 29 3.94664207e+01 3.93826246e+01 3.00e-08 1.78e-07 1.62e-06 1s\n", - " 30 3.94387877e+01 3.94074307e+01 9.74e-09 2.03e-07 6.07e-07 1s\n", - " 31 3.94283747e+01 3.94151381e+01 2.67e-09 1.08e-07 2.56e-07 1s\n", - " 32 3.94252074e+01 3.94220165e+01 7.63e-10 3.99e-08 6.17e-08 1s\n", - " 33 3.94247728e+01 3.94225528e+01 5.19e-10 3.23e-08 4.29e-08 1s\n", - " 34 3.94242801e+01 3.94231932e+01 2.55e-10 2.37e-08 2.10e-08 1s\n", - " 35 3.94240358e+01 3.94234084e+01 1.02e-09 1.84e-08 1.21e-08 1s\n", - " 36 3.94238861e+01 3.94236149e+01 4.45e-10 1.22e-08 5.25e-09 1s\n", - " 37 3.94237861e+01 3.94237128e+01 4.79e-10 5.43e-09 1.42e-09 1s\n", - " 38 3.94237692e+01 3.94237544e+01 1.51e-08 1.91e-08 2.83e-10 1s\n", - " 39 3.94237667e+01 3.94237620e+01 2.93e-07 2.19e-08 9.55e-11 1s\n", - " 40 3.94237656e+01 3.94237640e+01 4.16e-07 1.64e-08 3.39e-11 1s\n", - " 41 3.94237652e+01 3.94237647e+01 1.23e-07 3.16e-09 7.30e-12 1s\n", + " 0 3.22081149e+05 -2.92037832e+06 6.80e+05 7.47e-02 4.27e+03 0s\n", + " 1 2.76383617e+05 -1.22928033e+07 4.92e+05 7.28e+03 3.09e+03 0s\n", + " 2 1.67469410e+05 -1.05564170e+07 2.79e+05 5.56e+03 1.91e+03 0s\n", + " 3 1.33437477e+05 -6.45497567e+06 1.93e+05 1.09e+04 1.11e+03 0s\n", + " 4 7.53242751e+04 -3.36063063e+06 8.00e+04 4.89e+03 5.82e+02 0s\n", + " 5 5.55917340e+04 -2.03159500e+06 4.50e+04 5.19e+03 2.36e+02 0s\n", + " 6 3.87721396e+04 -5.22748032e+05 2.06e+04 3.65e+03 1.05e+02 0s\n", + " 7 2.25904487e+04 -2.83566987e+05 6.31e+03 1.26e+03 3.47e+01 0s\n", + " 8 9.69205978e+03 -1.79875756e+05 1.44e+03 6.30e+02 1.19e+01 0s\n", + " 9 5.72185314e+03 -8.25632694e+04 5.88e+02 2.28e+02 4.78e+00 0s\n", + " 10 2.44231217e+03 -3.26369131e+04 1.11e+02 6.84e+01 1.40e+00 0s\n", + " 11 1.35841273e+03 -1.03718531e+04 5.01e+00 1.34e+01 3.54e-01 0s\n", + " 12 6.53464301e+02 -3.05393209e+03 2.79e-02 3.90e+00 1.03e-01 0s\n", + " 13 4.49199640e+02 -1.92992117e+03 1.46e-02 2.48e+00 6.51e-02 0s\n", + " 14 2.75490700e+02 -1.45379073e+03 6.40e-03 1.86e+00 4.67e-02 0s\n", + " 15 1.30191388e+02 -3.07232786e+02 1.43e-03 3.94e-01 1.17e-02 0s\n", + " 16 8.43261262e+01 -6.35030113e+01 5.13e-04 1.25e-01 3.94e-03 0s\n", + " 17 6.48796401e+01 9.85014109e-01 2.23e-04 4.79e-02 1.70e-03 0s\n", + " 18 5.35651687e+01 1.40656253e+01 9.36e-05 3.27e-02 1.05e-03 0s\n", + " 19 4.70392106e+01 2.81618221e+01 3.47e-05 1.59e-02 5.02e-04 0s\n", + " 20 4.54285541e+01 3.00278575e+01 2.37e-05 1.36e-02 4.10e-04 0s\n", + " 21 4.33901840e+01 3.45005500e+01 1.14e-05 8.10e-03 2.36e-04 0s\n", + " 22 4.19986349e+01 3.74337316e+01 5.14e-06 4.16e-03 1.21e-04 0s\n", + " 23 4.12968550e+01 3.88955411e+01 2.72e-06 2.03e-03 6.28e-05 0s\n", + " 24 4.07772614e+01 3.94873652e+01 1.27e-06 1.09e-03 3.33e-05 0s\n", + " 25 4.05524435e+01 3.97532629e+01 7.76e-07 6.53e-04 2.07e-05 0s\n", + " 26 4.04055794e+01 3.99835364e+01 4.66e-07 2.70e-04 1.10e-05 0s\n", + " 27 4.03547163e+01 4.00509942e+01 3.67e-07 1.58e-04 7.85e-06 0s\n", + " 28 4.03181376e+01 4.00816471e+01 2.97e-07 1.14e-04 6.12e-06 1s\n", + " 29 4.02556271e+01 4.01110093e+01 1.69e-07 7.22e-05 3.72e-06 1s\n", + " 30 4.02312354e+01 4.01348744e+01 1.24e-07 3.86e-05 2.50e-06 1s\n", + " 31 4.02144993e+01 4.01448158e+01 9.39e-08 2.29e-05 1.80e-06 1s\n", + " 32 4.02030659e+01 4.01482662e+01 7.35e-08 1.73e-05 1.41e-06 1s\n", + " 33 4.01936736e+01 4.01520096e+01 5.67e-08 1.28e-05 1.08e-06 1s\n", + " 34 4.01911099e+01 4.01532768e+01 5.16e-08 1.14e-05 9.78e-07 1s\n", + " 35 4.01869289e+01 4.01559727e+01 4.36e-08 8.18e-06 8.02e-07 1s\n", + " 36 4.01791482e+01 4.01604511e+01 2.81e-08 3.33e-06 4.90e-07 1s\n", + " 37 4.01688879e+01 4.01623276e+01 8.91e-09 1.28e-06 1.71e-07 1s\n", + " 38 4.01645550e+01 4.01630880e+01 1.78e-09 3.26e-07 3.55e-08 1s\n", + " 39 4.01639083e+01 4.01634549e+01 7.54e-10 9.78e-08 1.08e-08 1s\n", + " 40 4.01637662e+01 4.01635460e+01 4.59e-10 5.25e-08 5.16e-09 1s\n", + " 41 4.01636746e+01 4.01635935e+01 2.18e-09 2.99e-08 1.80e-09 1s\n", + " 42 4.01636547e+01 4.01637063e+01 8.05e-07 4.43e-07 1.22e-09 1s\n", + " 43 4.01636545e+01 4.01637857e+01 7.03e-07 3.49e-07 1.05e-09 1s\n", + " 44 4.01636538e+01 4.01635926e+01 4.17e-07 1.01e-07 2.98e-10 1s\n", + " 45 4.01636536e+01 4.01636220e+01 3.58e-07 2.42e-07 1.81e-10 1s\n", + " 46 4.01636533e+01 4.01639850e+01 2.19e-07 6.67e-07 1.28e-10 1s\n", + " 47 4.01636533e+01 4.01639839e+01 2.15e-07 6.29e-07 1.26e-10 1s\n", + " 48 4.01636533e+01 4.01639791e+01 2.16e-07 6.26e-07 1.25e-10 1s\n", + " 49 4.01636533e+01 4.01640809e+01 2.77e-07 6.57e-07 1.24e-10 1s\n", + " 50 4.01636533e+01 4.01640802e+01 2.76e-07 6.55e-07 1.24e-10 1s\n", + " 51 4.01636533e+01 4.01641728e+01 2.76e-07 6.82e-07 1.23e-10 1s\n", + " 52 4.01636533e+01 4.01641662e+01 2.76e-07 6.82e-07 1.23e-10 1s\n", + " 53 4.01636533e+01 4.01641649e+01 2.76e-07 6.82e-07 1.23e-10 1s\n", + " 54 4.01636533e+01 4.01641433e+01 2.76e-07 6.82e-07 1.23e-10 1s\n", + " 55 4.01636533e+01 4.01641471e+01 2.75e-07 6.74e-07 1.23e-10 1s\n", + " 56 4.01636533e+01 4.01641360e+01 2.75e-07 6.77e-07 1.23e-10 1s\n", "\n", - "Barrier solved model in 41 iterations and 1.24 seconds (1.42 work units)\n", - "Optimal objective 3.94237652e+01\n", + "Barrier solved model in 56 iterations and 0.91 seconds\n", + "Optimal objective 4.01636746e+01\n", "\n", "\n", - "Root relaxation: objective 3.942376e+01, 4514 iterations, 1.38 seconds (1.73 work units)\n", + "Root relaxation: objective 4.016365e+01, 2611 iterations, 0.86 seconds\n", "\n", " Nodes | Current Node | Objective Bounds | Work\n", " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", "\n", - " 0 0 39.42376 0 7 - 39.42376 - - 1s\n", - "H 0 0 39.4723268 39.42376 0.12% - 1s\n", + " 0 0 40.16365 0 7 - 40.16365 - - 1s\n", + "H 0 0 40.2122149 40.16365 0.12% - 1s\n", "\n", - "Explored 1 nodes (4514 simplex iterations) in 1.66 seconds (2.12 work units)\n", - "Thread count was 3 (of 72 available processors)\n", + "Explored 1 nodes (2611 simplex iterations) in 1.05 seconds\n", + "Thread count was 3 (of 8 available processors)\n", "\n", - "Solution count 1: 39.4723 \n", + "Solution count 1: 40.2122 \n", "\n", "Optimal solution found (tolerance 5.00e-03)\n", - "Warning: max constraint violation (2.4136e-06) exceeds tolerance\n", - "Best objective 3.947232680449e+01, best bound 3.942376493850e+01, gap 0.1230%\n", + "Best objective 4.021221485623e+01, best bound 4.016365299024e+01, gap 0.1208%\n", "\n", "Status: ok\n", "Return code: 0\n", "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", "Termination condition: optimal\n", "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", - "Wall time: 1.6618361473083496\n", + "Wall time: 1.052042007446289\n", "Error rc: 0\n", - "Time: 2.0176522731781006\n", + "Time: 1.6672213077545166\n", "\n", "\n", - "Name: x34531\n", - "Lower bound: 39.42376493850343\n", - "Upper bound: 39.47232680449366\n", + "Name: x1\n", + "Lower bound: 40.16365299024388\n", + "Upper bound: 40.21221485623409\n", "Number of objectives: 1\n", - "Number of constraints: 44417\n", - "Number of variables: 22723\n", + "Number of constraints: 44997\n", + "Number of variables: 16580\n", "Number of binary variables: 52\n", "Number of integer variables: 52\n", - "Number of continuous variables: 22671\n", - "Number of nonzeros: 130996\n", + "Number of continuous variables: 16528\n", + "Number of nonzeros: 122638\n", "Sense: minimize\n", "\n", - "Solve time: 3.8591105937957764 sec.\n", + "Solve time: 3.2098329067230225 sec.\n", "\n", "Processing optimization output...\n", - "for SourceSinkModel ... (0.2260sec)\n", - "for ConversionModel ... (0.1863sec)\n", - "for StorageModel ... (0.8118sec)\n", - "for TransmissionModel ... (0.5359sec)\n", - "\t\t(1.7642 sec)\n", + "for SourceSinkModel ... (0.9859sec)\n", + "for ConversionModel ... (0.5402sec)\n", + "for StorageModel ... (1.5049sec)\n", + "for TransmissionModel ... (1.2071sec)\n", + "\t\t(4.2651 sec)\n", "\n" ] } @@ -701,28 +729,28 @@ " \n", " RAMUsage\n", " ramUsageStartGB\n", - " 0.35651\n", + " 0.311729\n", " \n", " \n", " ramUsageEndGB\n", - " 0.418644\n", + " 0.352001\n", " \n", " \n", " ProcessingTimes\n", " buildtime\n", - " 1.580103\n", + " 2.664385\n", " \n", " \n", " tsaBuildTime\n", - " 0.778096\n", + " 5.126427\n", " \n", " \n", " solvetime\n", - " 3.859111\n", + " 3.209833\n", " \n", " \n", " runtime\n", - " 7.203635\n", + " 10.140343\n", " \n", " \n", " TSAParameters\n", @@ -739,11 +767,11 @@ " \n", " \n", " segmentation\n", - " False\n", + " True\n", " \n", " \n", " noSegments\n", - " 10\n", + " 12\n", " \n", " \n", " tsaSolver\n", @@ -755,260 +783,245 @@ " \n", " \n", " tsaBuildTime\n", - " 0.778096\n", - " \n", - " \n", - " GurobiSummary\n", - " Version\n", - " 9.5.1\n", + " 5.126427\n", " \n", " \n", - " BarIterCount\n", - " 41\n", - " \n", - " \n", - " NodeCount\n", - " 1\n", - " \n", - " \n", - " IterCount\n", - " 4514\n", + " GurobiSummary\n", + " Platform\n", + " win64\n", " \n", " \n", - " Runtime\n", - " 1.66\n", + " Time\n", + " 03/21/24 10:37:50\n", " \n", " \n", - " RelaxObj\n", - " 39.42376\n", + " NumConstrs\n", + " 44997\n", " \n", " \n", - " RelaxIterCount\n", - " 4514\n", + " NumVars\n", + " 16580\n", " \n", " \n", - " RelaxTime\n", - " 1.38\n", + " NumNZs\n", + " 122638\n", " \n", " \n", - " SolCount\n", - " 1\n", + " Fingerprint\n", + " 0x24632396\n", " \n", " \n", - " OrderingTime\n", - " 0.12\n", + " PresolvedNumConVars\n", + " 8745\n", " \n", " \n", - " Threads\n", - " 3\n", + " PresolvedNumIntVars\n", + " 26\n", " \n", " \n", - " Cores\n", - " 72\n", + " PresolvedNumBinVars\n", + " 26\n", " \n", " \n", - " PresolveTime\n", - " 0.17\n", + " MinCoeff\n", + " 0.000001\n", " \n", " \n", - " NumConstrs\n", - " 44417\n", + " MaxCoeff\n", + " 500.0\n", " \n", " \n", - " NumVars\n", - " 22723\n", + " MinObjCoeff\n", + " 0.00001\n", " \n", " \n", - " NumNZs\n", - " 130996\n", + " MaxObjCoeff\n", + " 0.3\n", " \n", " \n", - " ObjVal\n", - " 39.472327\n", + " MinBound\n", + " 0.04\n", " \n", " \n", - " PresolvedNumConVars\n", - " 14210\n", + " MaxBound\n", + " 100000.0\n", " \n", " \n", - " PresolvedNumIntVars\n", - " 26\n", + " MinRHS\n", + " 0.04\n", " \n", " \n", - " PresolvedNumBinVars\n", - " 26\n", + " MaxRHS\n", + " 300.0\n", " \n", " \n", - " ObjBound\n", - " 39.423765\n", + " PresolveTime\n", + " 0.15\n", " \n", " \n", - " MIPGap\n", - " 0.00123\n", + " PresolvedNumConstrs\n", + " 26992\n", " \n", " \n", - " Platform\n", - " linux64\n", + " PresolvedNumVars\n", + " 8771\n", " \n", " \n", - " Time\n", - " Thu Aug 25 14:02:19 2022\n", + " PresolvedNumNZs\n", + " 89294\n", " \n", " \n", " Status\n", " OPTIMAL\n", " \n", " \n", - " PresolvedNumConstrs\n", - " 32737\n", + " ObjVal\n", + " 40.212229\n", " \n", " \n", - " PresolvedNumVars\n", - " 14236\n", + " RelaxObj\n", + " 40.16367\n", " \n", " \n", - " PresolvedNumNZs\n", - " 109458\n", + " RelaxIterCount\n", + " 2675\n", " \n", " \n", - " MinCoeff\n", - " 0.00001\n", + " RelaxTime\n", + " 1.03\n", " \n", " \n", - " MaxCoeff\n", - " 500.0\n", + " OrderingTime\n", + " 0.02\n", " \n", " \n", - " MinObjCoeff\n", - " 0.00001\n", + " BarIterCount\n", + " 54\n", " \n", " \n", - " MaxObjCoeff\n", - " 0.3\n", + " Runtime\n", + " 1.26\n", " \n", " \n", - " MinBound\n", - " 1.0\n", + " NodeCount\n", + " 1\n", " \n", " \n", - " MaxBound\n", - " 100000.0\n", + " IterCount\n", + " 2675\n", " \n", " \n", - " MinRHS\n", - " 0.04\n", + " ObjBound\n", + " 40.163667\n", " \n", " \n", - " MaxRHS\n", - " 300.0\n", + " MIPGap\n", + " 0.001208\n", " \n", " \n", - " NumWarnings\n", - " 1\n", + " Threads\n", + " 3\n", " \n", " \n", - " MIPGap (Parameter)\n", - " 0.005\n", + " Cores\n", + " 8\n", " \n", " \n", - " Cuts (Parameter)\n", - " 0\n", + " SolCount\n", + " 1\n", " \n", " \n", - " Method (Parameter)\n", - " 2\n", + " ModelType\n", + " MIP\n", " \n", " \n", - " OptimalityTol (Parameter)\n", - " 0.001\n", + " ChangedParams\n", + " {}\n", " \n", " \n", - " LogFile (Parameter)\n", - " \"run.log\"\n", + " LogFilePath\n", + " C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\f...\n", " \n", " \n", - " ModelType\n", - " MIP\n", + " LogNumber\n", + " 1\n", " \n", " \n", - " LogFilePath\n", - " /storage/internal/home/p-dunkel/Promotion/prog...\n", + " Seed\n", + " 0\n", " \n", " \n", - " Log\n", - " run\n", + " Version\n", + " 9.0.1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Value\n", - "Category Parameter \n", - "FineParameters noOfRegions 8\n", - " numberOfTimeSteps 8760\n", - " hoursPerTimestep 1\n", - " numberOfYears 1.0\n", - " optimizationSpecs OptimalityTol=1e-3 method=2 cuts=0 MIPGap=5e-3\n", - "RAMUsage ramUsageStartGB 0.35651\n", - " ramUsageEndGB 0.418644\n", - "ProcessingTimes buildtime 1.580103\n", - " tsaBuildTime 0.778096\n", - " solvetime 3.859111\n", - " runtime 7.203635\n", - "TSAParameters clusterMethod hierarchical\n", - " noTypicalPeriods 2\n", - " hoursPerPeriod 24\n", - " segmentation False\n", - " noSegments 10\n", - " tsaSolver cbc\n", - " timeStepsPerPeriod 24\n", - " tsaBuildTime 0.778096\n", - "GurobiSummary Version 9.5.1\n", - " BarIterCount 41\n", - " NodeCount 1\n", - " IterCount 4514\n", - " Runtime 1.66\n", - " RelaxObj 39.42376\n", - " RelaxIterCount 4514\n", - " RelaxTime 1.38\n", - " SolCount 1\n", - " OrderingTime 0.12\n", - " Threads 3\n", - " Cores 72\n", - " PresolveTime 0.17\n", - " NumConstrs 44417\n", - " NumVars 22723\n", - " NumNZs 130996\n", - " ObjVal 39.472327\n", - " PresolvedNumConVars 14210\n", - " PresolvedNumIntVars 26\n", - " PresolvedNumBinVars 26\n", - " ObjBound 39.423765\n", - " MIPGap 0.00123\n", - " Platform linux64\n", - " Time Thu Aug 25 14:02:19 2022\n", - " Status OPTIMAL\n", - " PresolvedNumConstrs 32737\n", - " PresolvedNumVars 14236\n", - " PresolvedNumNZs 109458\n", - " MinCoeff 0.00001\n", - " MaxCoeff 500.0\n", - " MinObjCoeff 0.00001\n", - " MaxObjCoeff 0.3\n", - " MinBound 1.0\n", - " MaxBound 100000.0\n", - " MinRHS 0.04\n", - " MaxRHS 300.0\n", - " NumWarnings 1\n", - " MIPGap (Parameter) 0.005\n", - " Cuts (Parameter) 0\n", - " Method (Parameter) 2\n", - " OptimalityTol (Parameter) 0.001\n", - " LogFile (Parameter) \"run.log\"\n", - " ModelType MIP\n", - " LogFilePath /storage/internal/home/p-dunkel/Promotion/prog...\n", - " Log run" + " Value\n", + "Category Parameter \n", + "FineParameters noOfRegions 8\n", + " numberOfTimeSteps 8760\n", + " hoursPerTimestep 1\n", + " numberOfYears 1.0\n", + " optimizationSpecs OptimalityTol=1e-3 method=2 cuts=0 MIPGap=5e-3\n", + "RAMUsage ramUsageStartGB 0.311729\n", + " ramUsageEndGB 0.352001\n", + "ProcessingTimes buildtime 2.664385\n", + " tsaBuildTime 5.126427\n", + " solvetime 3.209833\n", + " runtime 10.140343\n", + "TSAParameters clusterMethod hierarchical\n", + " noTypicalPeriods 2\n", + " hoursPerPeriod 24\n", + " segmentation True\n", + " noSegments 12\n", + " tsaSolver cbc\n", + " timeStepsPerPeriod 24\n", + " tsaBuildTime 5.126427\n", + "GurobiSummary Platform win64\n", + " Time 03/21/24 10:37:50\n", + " NumConstrs 44997\n", + " NumVars 16580\n", + " NumNZs 122638\n", + " Fingerprint 0x24632396\n", + " PresolvedNumConVars 8745\n", + " PresolvedNumIntVars 26\n", + " PresolvedNumBinVars 26\n", + " MinCoeff 0.000001\n", + " MaxCoeff 500.0\n", + " MinObjCoeff 0.00001\n", + " MaxObjCoeff 0.3\n", + " MinBound 0.04\n", + " MaxBound 100000.0\n", + " MinRHS 0.04\n", + " MaxRHS 300.0\n", + " PresolveTime 0.15\n", + " PresolvedNumConstrs 26992\n", + " PresolvedNumVars 8771\n", + " PresolvedNumNZs 89294\n", + " Status OPTIMAL\n", + " ObjVal 40.212229\n", + " RelaxObj 40.16367\n", + " RelaxIterCount 2675\n", + " RelaxTime 1.03\n", + " OrderingTime 0.02\n", + " BarIterCount 54\n", + " Runtime 1.26\n", + " NodeCount 1\n", + " IterCount 2675\n", + " ObjBound 40.163667\n", + " MIPGap 0.001208\n", + " Threads 3\n", + " Cores 8\n", + " SolCount 1\n", + " ModelType MIP\n", + " ChangedParams {}\n", + " LogFilePath C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\f...\n", + " LogNumber 1\n", + " Seed 0\n", + " Version 9.0.1" ] }, "execution_count": 5, @@ -1029,7 +1042,8 @@ "hash": "533c634cfa0e4b268023d4cfe70a60a4978af4bca707f55150aefad8a3e18e2a" }, "kernelspec": { - "display_name": "Python 3.6.13 64-bit ('spielwiese6': conda)", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -1042,10 +1056,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" - }, - "orig_nbformat": 4 + "version": "3.10.13" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx similarity index 100% rename from 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a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.cpg b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.cpg similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.cpg rename to examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.cpg diff --git a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.dbf b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.dbf similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.dbf rename to examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.dbf diff --git a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/ShapeFiles/AClines.prj 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a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOffshore_el___.xlsx b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOffshore_el___.xlsx similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOffshore_el___.xlsx rename to examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOffshore_el___.xlsx diff --git a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx rename to examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx diff --git a/examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el_.xlsx b/examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el_.xlsx similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el_.xlsx rename to examples/03_Multi-regional_Energy_System_Workflow/InputData/SpatialData/Wind/maxOperationRateOnshore_el_.xlsx diff --git a/examples/Multi-regional_Energy_System_Workflow/getData.py b/examples/03_Multi-regional_Energy_System_Workflow/getData.py similarity index 100% rename from examples/Multi-regional_Energy_System_Workflow/getData.py rename to examples/03_Multi-regional_Energy_System_Workflow/getData.py diff --git a/examples/Model_Run_from_Excel/Model_Run_from_Excel.ipynb b/examples/04_Model_Run_from_Excel/04_Model_Run_from_Excel.ipynb similarity index 57% rename from examples/Model_Run_from_Excel/Model_Run_from_Excel.ipynb rename to examples/04_Model_Run_from_Excel/04_Model_Run_from_Excel.ipynb index 1a0171cf..6db925e0 100644 --- a/examples/Model_Run_from_Excel/Model_Run_from_Excel.ipynb +++ b/examples/04_Model_Run_from_Excel/04_Model_Run_from_Excel.ipynb @@ -8,9 +8,9 @@ "\n", "Besides using a python script or a jupyter notebook it is also possible to read and run a model using excel files.\n", "\n", - "The energySystemModelRunFromExcel() function reads a model from excel, optimizes it and stores it to an excel file.\n", + "The *energySystemModelRunFromExcel()* function reads a model from excel, optimizes it and stores it to an excel file.\n", "\n", - "The readEnergySystemModelFromExcel() function reads a model from excel.\n", + "The *readEnergySystemModelFromExcel()* function reads a model from excel.\n", "\n", "The model run can also be started on double-klicking on the run.bat Windows batch script in the folder where this notebook is located (still requires that a Python version, the FINE package and an optmization solver are installed).\n", "\n", @@ -21,7 +21,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Import FINE package\n" + "# Import ETHOS.FINE package\n" ] }, { @@ -66,69 +66,57 @@ "text": [ "The distances of a component are set to a normalized value of 1.\n", "\n", - "Clustering time series data with 3 typical periods and 24 time steps per period...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " C:\\Users\\s.patil\\Anaconda3\\envs\\europeanmodel\\lib\\site-packages\\tsam\\timeseriesaggregation.py:982: UserWarning:Something went wrong: At least one maximal value of the aggregated time series exceeds the maximal value the input time series\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t\t(2.6636 sec)\n", + "Clustering time series data with 3 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(7.6881 sec)\n", "\n", "Time series aggregation specifications:\n", - "Number of typical periods:3, number of time steps per period:24\n", + "Number of typical periods:3, number of time steps per period:24, number of segments per period:12\n", "\n", "Declaring sets, variables and constraints for SourceSinkModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.5242 sec)\n", + "\t\t(0.2727 sec)\n", "\n", - "Declaring sets, variables and constraints for StorageModel\n", + "Declaring sets, variables and constraints for TransmissionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(3.7053 sec)\n", + "\t\t(0.2163 sec)\n", "\n", "Declaring sets, variables and constraints for ConversionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.3695 sec)\n", + "\t\t(0.0470 sec)\n", "\n", - "Declaring sets, variables and constraints for TransmissionModel\n", + "Declaring sets, variables and constraints for StorageModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.3431 sec)\n", + "\t\t(1.4540 sec)\n", "\n", "Declaring shared potential constraint...\n", - "\t\t(0.0000 sec)\n", + "\t\t(0.0020 sec)\n", "\n", "Declaring linked component quantity constraint...\n", "\t\t(0.0000 sec)\n", "\n", "Declaring commodity balances...\n", - "\t\t(0.6251 sec)\n", + "\t\t(0.1118 sec)\n", "\n", "\t\t(0.0000 sec)\n", "\n", "Declaring objective function...\n", - "\t\t(0.2853 sec)\n", + "\t\t(1.0623 sec)\n", "\n", "Either solver not selected or specified solver not available.gurobi is set as solver.\n", - "Using license file C:\\Users\\s.patil\\gurobi.lic\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", "Academic license - for non-commercial use only\n", - "Read LP format model from file C:\\Users\\S9CFE~1.PAT\\AppData\\Local\\Temp\\tmpk6y5w27n.pyomo.lp\n", - "Reading time = 0.45 seconds\n", - "x43032: 50111 rows, 27264 columns, 147376 nonzeros\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmphnbmj2s5.pyomo.lp\n", + "Reading time = 0.12 seconds\n", + "x1: 47438 rows, 18781 columns, 130323 nonzeros\n", "Changed value of parameter QCPDual to 1\n", " Prev: 0 Min: 0 Max: 1 Default: 0\n", "Changed value of parameter Threads to 3\n", @@ -136,116 +124,100 @@ "Parameter logfile unchanged\n", " Value: Default: \n", "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", - "Optimize a model with 50111 rows, 27264 columns and 147376 nonzeros\n", - "Model fingerprint: 0xdaa6fb84\n", - "Variable types: 27226 continuous, 38 integer (28 binary)\n", + "Optimize a model with 47438 rows, 18781 columns and 130323 nonzeros\n", + "Model fingerprint: 0x404aa04c\n", + "Variable types: 18743 continuous, 38 integer (28 binary)\n", "Coefficient statistics:\n", - " Matrix range [1e-06, 5e+02]\n", + " Matrix range [2e-05, 5e+02]\n", " Objective range [2e-06, 3e-01]\n", - " Bounds range [1e+00, 6e+04]\n", + " Bounds range [4e-02, 6e+04]\n", " RHS range [4e-02, 5e+01]\n", - "Presolve removed 13620 rows and 10101 columns\n", - "Presolve time: 0.55s\n", - "Presolved: 36491 rows, 17163 columns, 119873 nonzeros\n", - "Variable types: 17139 continuous, 24 integer (14 binary)\n", - "\n", - "Deterministic concurrent LP optimizer: primal and dual simplex\n", - "Showing first log only...\n", - "\n", - "\n", - "Root simplex log...\n", + "Presolve removed 18590 rows and 8541 columns\n", + "Presolve time: 0.19s\n", + "Presolved: 28848 rows, 10240 columns, 94244 nonzeros\n", + "Variable types: 10216 continuous, 24 integer (14 binary)\n", "\n", - "Iteration Objective Primal Inf. Dual Inf. Time\n", - " 19897 8.2760230e+01 0.000000e+00 9.850258e+04 5s\n", - " 24235 5.3958254e+01 0.000000e+00 2.164024e+03 10s\n", - " 27309 4.8164413e+01 0.000000e+00 9.734999e+03 15s\n", - " 31037 4.5972621e+01 0.000000e+00 1.419317e+03 20s\n", - " 34714 4.5115441e+01 0.000000e+00 1.816553e+03 25s\n", - " 38775 4.4786630e+01 0.000000e+00 1.608113e-01 30s\n", - " 39938 4.4785639e+01 0.000000e+00 0.000000e+00 31s\n", - " 39938 4.4785639e+01 0.000000e+00 0.000000e+00 31s\n", - "Concurrent spin time: 6.70s\n", - "\n", - "Solved with primal simplex\n", - "\n", - "Root relaxation: objective 4.478564e+01, 39938 iterations, 37.38 seconds\n", + "Root relaxation: objective 4.352336e+01, 23603 iterations, 4.48 seconds\n", "\n", " Nodes | Current Node | Objective Bounds | Work\n", " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", "\n", - " 0 0 44.78564 0 14 - 44.78564 - - 38s\n", - "H 0 0 45.5283079 44.78564 1.63% - 40s\n", - " 0 0 44.78579 0 9 45.52831 44.78579 1.63% - 44s\n", - "H 0 0 44.7862290 44.78579 0.00% - 44s\n", + " 0 0 43.52336 0 12 - 43.52336 - - 4s\n", + "H 0 0 44.0501471 43.52336 1.20% - 5s\n", + " 0 0 43.52349 0 4 44.05015 43.52349 1.20% - 5s\n", + "H 0 0 43.6827794 43.52349 0.36% - 6s\n", + " 0 0 43.52351 0 3 43.68278 43.52351 0.36% - 6s\n", + " 0 0 43.52351 0 1 43.68278 43.52351 0.36% - 6s\n", + "H 0 0 43.5235348 43.52351 0.00% - 6s\n", "\n", "Cutting planes:\n", - " Gomory: 4\n", - " MIR: 25\n", - " Flow cover: 718\n", - " Relax-and-lift: 8\n", + " Gomory: 6\n", + " MIR: 14\n", + " Flow cover: 187\n", + " Relax-and-lift: 10\n", "\n", - "Explored 1 nodes (40936 simplex iterations) in 45.03 seconds\n", + "Explored 1 nodes (23834 simplex iterations) in 6.78 seconds\n", "Thread count was 3 (of 8 available processors)\n", "\n", - "Solution count 2: 44.7862 45.5283 \n", + "Solution count 3: 43.5235 43.6828 44.0501 \n", "\n", "Optimal solution found (tolerance 1.00e-04)\n", - "Warning: max constraint violation (3.2480e-06) exceeds tolerance\n", - "Best objective 4.478622904421e+01, best bound 4.478579440539e+01, gap 0.0010%\n", + "Warning: max constraint violation (6.0876e-06) exceeds tolerance\n", + "Best objective 4.352353478585e+01, best bound 4.352351328296e+01, gap 0.0000%\n", "\n", "Status: ok\n", "Return code: 0\n", "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", "Termination condition: optimal\n", "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", - "Wall time: 45.02914047241211\n", + "Wall time: 6.783330917358398\n", "Error rc: 0\n", - "Time: 46.193702697753906\n", + "Time: 7.441400527954102\n", "\n", "\n", - "Name: x43032\n", - "Lower bound: 44.78579440538802\n", - "Upper bound: 44.78622904420734\n", + "Name: x1\n", + "Lower bound: 43.523513282964586\n", + "Upper bound: 43.52353478584742\n", "Number of objectives: 1\n", - "Number of constraints: 50111\n", - "Number of variables: 27264\n", + "Number of constraints: 47438\n", + "Number of variables: 18781\n", "Number of binary variables: 28\n", "Number of integer variables: 38\n", - "Number of continuous variables: 27226\n", - "Number of nonzeros: 147376\n", + "Number of continuous variables: 18743\n", + "Number of nonzeros: 130323\n", "Sense: minimize\n", "\n", - "Solve time: 53.522496461868286 sec.\n", + "Solve time: 9.179068326950073 sec.\n", "\n", "Processing optimization output...\n", - "for SourceSinkModel ... (1.6282sec)\n" + "for SourceSinkModel ... (1.4144sec)\n", + "for TransmissionModel ... (1.1809sec)\n", + "for ConversionModel ... (0.8107sec)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " c:\\users\\s.patil\\documents\\code\\fine\\FINE\\storage.py:1170: UserWarning:Charge and discharge at the same time for component Pumped hydro storage\n", - " c:\\users\\s.patil\\documents\\code\\fine\\FINE\\storage.py:1170: UserWarning:Charge and discharge at the same time for component Salt caverns (hydrogen)\n" + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "for StorageModel ... (4.5870sec)\n", - "for ConversionModel ... (1.0337sec)\n", - "for TransmissionModel ... (3.3035sec)\n", - "\t\t(10.5781 sec)\n", + "for StorageModel ... (1.5466sec)\n", + "\t\t(4.9915 sec)\n", "\n", "\n", "Writing output to Excel... \n", "\tProcessing SourceSinkModel ...\n", - "\tProcessing StorageModel ...\n", - "\tProcessing ConversionModel ...\n", "\tProcessing TransmissionModel ...\n", + "\tProcessing ConversionModel ...\n", + "\tProcessing StorageModel ...\n", "\tSaving file...\n", - "Done. (108.9859 sec)\n" + "Done. (28.1254 sec)\n" ] } ], @@ -292,7 +264,7 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -306,7 +278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/Model_Run_from_Excel/run.bat b/examples/04_Model_Run_from_Excel/run.bat similarity index 100% rename from examples/Model_Run_from_Excel/run.bat rename to examples/04_Model_Run_from_Excel/run.bat diff --git a/examples/Model_Run_from_Excel/scenarioInput.xlsx b/examples/04_Model_Run_from_Excel/scenarioInput.xlsx similarity index 100% rename from examples/Model_Run_from_Excel/scenarioInput.xlsx rename to examples/04_Model_Run_from_Excel/scenarioInput.xlsx diff --git a/examples/Model_Run_from_Excel/scenarioInputWithMacro.xlsm b/examples/04_Model_Run_from_Excel/scenarioInputWithMacro.xlsm similarity index 100% rename from examples/Model_Run_from_Excel/scenarioInputWithMacro.xlsm rename to examples/04_Model_Run_from_Excel/scenarioInputWithMacro.xlsm diff --git a/examples/Model_Run_from_Excel/scenarioInput_gurobi.xlsx b/examples/04_Model_Run_from_Excel/scenarioInput_gurobi.xlsx similarity index 100% rename from examples/Model_Run_from_Excel/scenarioInput_gurobi.xlsx rename to examples/04_Model_Run_from_Excel/scenarioInput_gurobi.xlsx diff --git a/examples/04_Model_Run_from_Excel/scenarioResults.xlsx b/examples/04_Model_Run_from_Excel/scenarioResults.xlsx new file mode 100644 index 00000000..108107f5 Binary files /dev/null and b/examples/04_Model_Run_from_Excel/scenarioResults.xlsx differ diff --git a/examples/05_District_Optimization/05_Urban_District_Optimization_Workflow.ipynb b/examples/05_District_Optimization/05_Urban_District_Optimization_Workflow.ipynb new file mode 100644 index 00000000..024c01c1 --- /dev/null +++ b/examples/05_District_Optimization/05_Urban_District_Optimization_Workflow.ipynb @@ -0,0 +1,2766 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workflow for a district optimization\n", + "\n", + "In this application of the ETHOS.FINE framework, a small district is modeled and optimized.\n", + "\n", + "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", + "\n", + "The workflow is structures as follows:\n", + "1. Required packages are imported and the input data path is set\n", + "2. An energy system model instance is created\n", + "3. Commodity sources are added to the energy system model\n", + "4. Commodity conversion components are added to the energy system model\n", + "5. Commodity storages are added to the energy system model\n", + "6. Commodity transmission components are added to the energy system model\n", + "7. Commodity sinks are added to the energy system model\n", + "8. The energy system model is optimized\n", + "9. Selected optimization results are presented\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Import required packages and set input data path\n", + "\n", + "The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import fine as fn\n", + "from getData import getData\n", + "import pandas as pd\n", + "\n", + "data = getData()\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Create an energy system model instance \n", + "\n", + "The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", + "\n", + "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "locations = data[\"locations\"]\n", + "commodityUnitDict = {\"electricity\": \"kW_el\", \"methane\": \"kW_CH4_LHV\", \"heat\": \"kW_th\"}\n", + "commodities = {\"electricity\", \"methane\", \"heat\"}\n", + "numberOfTimeSteps = 8760\n", + "hoursPerTimeStep = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The modeled locations include 6 buildings (bd1 to bd6), which are connected to different grids (grid1 to grid7), and the location of the transformer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'bd1',\n", + " 'bd2',\n", + " 'bd3',\n", + " 'bd4',\n", + " 'bd5',\n", + " 'bd6',\n", + " 'grid1',\n", + " 'grid2',\n", + " 'grid3',\n", + " 'grid4',\n", + " 'grid5',\n", + " 'grid6',\n", + " 'grid7',\n", + " 'transformer'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "locations" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "esM = fn.EnergySystemModel(\n", + " locations=locations,\n", + " commodities=commodities,\n", + " numberOfTimeSteps=8760,\n", + " commodityUnitsDict=commodityUnitDict,\n", + " hoursPerTimeStep=1,\n", + " costUnit=\"€\",\n", + " lengthUnit=\"m\",\n", + " verboseLogLevel=2,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Add commodity sources to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electricity Purchase" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"Electricity purchase\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=False,\n", + " operationRateMax=data[\"El Purchase, operationRateMax\"],\n", + " commodityCost=0.298,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Natural Gas Purchase" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"NaturalGas purchase\",\n", + " commodity=\"methane\",\n", + " hasCapacityVariable=False,\n", + " operationRateMax=data[\"NG Purchase, operationRateMax\"],\n", + " commodityCost=0.065,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PV" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"PV\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=True,\n", + " hasIsBuiltBinaryVariable=True,\n", + " operationRateMax=data[\"PV, operationRateMax\"],\n", + " capacityMax=data[\"PV, capacityMax\"],\n", + " interestRate=0.04,\n", + " economicLifetime=20,\n", + " investIfBuilt=1000,\n", + " investPerCapacity=1400,\n", + " opexIfBuilt=10,\n", + " bigM=40,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Add conversion components to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Boiler" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"Boiler\",\n", + " physicalUnit=\"kW_th\",\n", + " commodityConversionFactors={\"methane\": -1.1, \"heat\": 1},\n", + " hasIsBuiltBinaryVariable=True,\n", + " hasCapacityVariable=True,\n", + " interestRate=0.04,\n", + " economicLifetime=20,\n", + " investIfBuilt=2800,\n", + " investPerCapacity=100,\n", + " opexIfBuilt=24,\n", + " bigM=200,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Add commodity storages to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal Storage " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Thermal Storage\",\n", + " commodity=\"heat\",\n", + " selfDischarge=0.001,\n", + " hasIsBuiltBinaryVariable=True,\n", + " capacityMax=data[\"TS, capacityMax\"],\n", + " interestRate=0.04,\n", + " economicLifetime=25,\n", + " investIfBuilt=23,\n", + " investPerCapacity=24,\n", + " bigM=250,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Battery Storage" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Battery Storage\",\n", + " commodity=\"electricity\",\n", + " cyclicLifetime=10000,\n", + " chargeEfficiency=0.95,\n", + " dischargeEfficiency=0.95,\n", + " chargeRate=0.5,\n", + " dischargeRate=0.5,\n", + " hasIsBuiltBinaryVariable=True,\n", + " capacityMax=data[\"BS, capacityMax\"],\n", + " interestRate=0.04,\n", + " economicLifetime=12,\n", + " investIfBuilt=2000,\n", + " investPerCapacity=700,\n", + " bigM=110,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 6. Add commodity transmission components to the energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cable Electricty" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"E_Distribution_Grid\",\n", + " commodity=\"electricity\",\n", + " losses=0.00001,\n", + " distances=data[\"cables, distances\"],\n", + " capacityFix=data[\"cables, capacityFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Natural Gas Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"NG_Distribution_Grid\",\n", + " commodity=\"methane\",\n", + " distances=data[\"NG, distances\"],\n", + " capacityFix=data[\"NG, capacityFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 7. Add commodity sinks to the energy system model\n", + "\n", + "### Electricity Demand" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"Electricity demand\",\n", + " commodity=\"electricity\",\n", + " hasCapacityVariable=False,\n", + " operationRateFix=data[\"Electricity demand, operationRateFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Heat Demand" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"BuildingsHeat\",\n", + " commodity=\"heat\",\n", + " hasCapacityVariable=False,\n", + " operationRateFix=data[\"Heat demand, operationRateFix\"],\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 8. Optimize energy system model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "esM.aggregateTemporally(numberOfTypicalPeriods=7)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp43bqanym.glpk.raw --wglp\n", + " C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp3oq8qsv1.glpk.glp --cpxlp C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpkcuvn3tw.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpkcuvn3tw.pyomo.lp'...\n", + "C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpkcuvn3tw.pyomo.lp:201320: warning: lower bound of variable 'x8' redefined\n", + "C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpkcuvn3tw.pyomo.lp:201320: warning: upper bound of variable 'x8' redefined\n", + "32542 rows, 15260 columns, 88192 non-zeros\n", + "32 integer variables, all of which are binary\n", + "201352 lines were read\n", + "Writing problem data to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp3oq8qsv1.glpk.glp'...\n", + "170387 lines were written\n", + "GLPK Integer Optimizer 5.0\n", + "32542 rows, 15260 columns, 88192 non-zeros\n", + "32 integer variables, all of which are binary\n", + "Preprocessing...\n", + "30488 rows, 12838 columns, 78508 non-zeros\n", + "32 integer variables, all of which are binary\n", + "Scaling...\n", + " A: min|aij| = 4.128e-03 max|aij| = 8.333e+02 ratio = 2.019e+05\n", + "GM: min|aij| = 3.663e-01 max|aij| = 2.730e+00 ratio = 7.454e+00\n", + "EQ: min|aij| = 1.342e-01 max|aij| = 1.000e+00 ratio = 7.454e+00\n", + "2N: min|aij| = 1.113e-01 max|aij| = 1.500e+00 ratio = 1.347e+01\n", + "Constructing initial basis...\n", + "Size of triangular part is 25424\n", + "Solving LP relaxation...\n", + "GLPK Simplex Optimizer 5.0\n", + "30488 rows, 12838 columns, 78508 non-zeros\n", + " 0: obj = -1.925690861e+03 inf = 2.383e+10 (10343)\n", + "Perturbing LP to avoid stalling [1001]...\n", + " 8212: obj = 1.902965375e+05 inf = 4.336e-06 (0) 72\n", + "Removing LP perturbation [10910]...\n", + "* 10910: obj = 1.321555860e+05 inf = 5.083e-09 (0) 23\n", + "OPTIMAL LP SOLUTION FOUND\n", + "Integer optimization begins...\n", + "Long-step dual simplex will be used\n", + "+ 10910: mip = not found yet >= -inf (1; 0)\n", + "+ 11844: >>>>> 1.332570326e+05 >= 1.327597180e+05 0.4% (18; 5)\n", + "+ 14302: >>>>> 1.332507742e+05 >= 1.328801486e+05 0.3% (57; 11)\n", + "+ 15103: >>>>> 1.332487877e+05 >= 1.328810415e+05 0.3% (65; 15)\n", + "+ 16659: >>>>> 1.332383678e+05 >= 1.329290858e+05 0.2% (79; 19)\n", + "+ 16680: >>>>> 1.329371437e+05 >= 1.329295671e+05 < 0.1% (77; 28)\n", + "+ 16725: mip = 1.329371437e+05 >= tree is empty 0.0% (0; 199)\n", + "INTEGER OPTIMAL SOLUTION FOUND\n", + "Time used: 11.3 secs\n", + "Memory used: 44.7 Mb (46916100 bytes)\n", + "Writing MIP solution to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmp43bqanym.glpk.raw'...\n", + "47811 lines were written\n" + ] + } + ], + "source": [ + "# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='cuts=0 method=2')\n", + "esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 9. Selected results output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sources and Sink" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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bd1bd2bd3bd4bd5bd6grid1grid2grid3grid4grid5grid6grid7transformer
ComponentPropertyUnit
BuildingsHeatoperation[kW_th*h/a]213008.3393211961.37319212160.201606212865.469106212987.945931213360.620262NaNNaNNaNNaNNaNNaNNaNNaN
[kW_th*h]213008.3393211961.37319212160.201606212865.469106212987.945931213360.620262NaNNaNNaNNaNNaNNaNNaNNaN
Electricity demandoperation[kW_el*h/a]24718.95869719409.33973724892.81862917439.83016423310.01415819718.252364NaNNaNNaNNaNNaNNaNNaNNaN
[kW_el*h]24718.95869719409.33973724892.81862917439.83016423310.01415819718.252364NaNNaNNaNNaNNaNNaNNaNNaN
Electricity purchaseNPVcontribution[€]000000000000030842.495427
TAC[€/a]000000000000030842.495427
commodCosts[€/a]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30842.495427
operation[kW_el*h/a]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN103498.306802
[kW_el*h]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN103498.306802
NaturalGas purchaseNPVcontribution[€]000000000000091270.980537
TAC[€/a]000000000000091270.980537
commodCosts[€/a]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN91270.980537
operation[kW_CH4_LHV*h/a]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1404168.931345
[kW_CH4_LHV*h]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1404168.931345
PVNPVcontribution[€]1926.3986640.01863.3639740.00.00.000000000
TAC[€/a]1916.3986640.01853.3639740.00.00.000000000
capacity[kW_el]17.8889170.017.2770150.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
capexCap[€/a]1842.8169130.01779.7822230.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
capexIfBuilt[€/a]73.581750.073.581750.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
commissioning[kW_el]17.8889170.017.2770150.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
invest[€]26044.4832480.025187.8212380.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
isBuilt[-]1.00.01.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
operation[kW_el*h/a]11358.668160.014752.641990.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
[kW_el*h]11358.668160.014752.641990.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
opexIfBuilt[€/a]10.00.010.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " bd1 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 213008.3393 \n", + " [kW_th*h] 213008.3393 \n", + "Electricity demand operation [kW_el*h/a] 24718.958697 \n", + " [kW_el*h] 24718.958697 \n", + "Electricity purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_CH4_LHV*h/a] NaN \n", + " [kW_CH4_LHV*h] NaN \n", + "PV NPVcontribution [€] 1926.398664 \n", + " TAC [€/a] 1916.398664 \n", + " capacity [kW_el] 17.888917 \n", + " capexCap [€/a] 1842.816913 \n", + " capexIfBuilt [€/a] 73.58175 \n", + " commissioning [kW_el] 17.888917 \n", + " invest [€] 26044.483248 \n", + " isBuilt [-] 1.0 \n", + " operation [kW_el*h/a] 11358.66816 \n", + " [kW_el*h] 11358.66816 \n", + " opexIfBuilt [€/a] 10.0 \n", + "\n", + " bd2 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 211961.37319 \n", + " [kW_th*h] 211961.37319 \n", + "Electricity demand operation [kW_el*h/a] 19409.339737 \n", + " [kW_el*h] 19409.339737 \n", + "Electricity purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_CH4_LHV*h/a] NaN \n", + " [kW_CH4_LHV*h] NaN \n", + "PV NPVcontribution [€] 0.0 \n", + " TAC [€/a] 0.0 \n", + " capacity [kW_el] 0.0 \n", + " capexCap [€/a] 0.0 \n", + " capexIfBuilt [€/a] 0.0 \n", + " commissioning [kW_el] 0.0 \n", + " invest [€] 0.0 \n", + " isBuilt [-] 0.0 \n", + " operation [kW_el*h/a] 0.0 \n", + " [kW_el*h] 0.0 \n", + " opexIfBuilt [€/a] 0.0 \n", + "\n", + " bd3 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 212160.201606 \n", + " [kW_th*h] 212160.201606 \n", + "Electricity demand operation [kW_el*h/a] 24892.818629 \n", + " [kW_el*h] 24892.818629 \n", + "Electricity purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_CH4_LHV*h/a] NaN \n", + " [kW_CH4_LHV*h] NaN \n", + "PV NPVcontribution [€] 1863.363974 \n", + " TAC [€/a] 1853.363974 \n", + " capacity [kW_el] 17.277015 \n", + " capexCap [€/a] 1779.782223 \n", + " capexIfBuilt [€/a] 73.58175 \n", + " commissioning [kW_el] 17.277015 \n", + " invest [€] 25187.821238 \n", + " isBuilt [-] 1.0 \n", + " operation [kW_el*h/a] 14752.64199 \n", + " [kW_el*h] 14752.64199 \n", + " opexIfBuilt [€/a] 10.0 \n", + "\n", + " bd4 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 212865.469106 \n", + " [kW_th*h] 212865.469106 \n", + "Electricity demand operation [kW_el*h/a] 17439.830164 \n", + " [kW_el*h] 17439.830164 \n", + "Electricity purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_CH4_LHV*h/a] NaN \n", + " [kW_CH4_LHV*h] NaN \n", + "PV NPVcontribution [€] 0.0 \n", + " TAC [€/a] 0.0 \n", + " capacity [kW_el] 0.0 \n", + " capexCap [€/a] 0.0 \n", + " capexIfBuilt [€/a] 0.0 \n", + " commissioning [kW_el] 0.0 \n", + " invest [€] 0.0 \n", + " isBuilt [-] 0.0 \n", + " operation [kW_el*h/a] 0.0 \n", + " [kW_el*h] 0.0 \n", + " opexIfBuilt [€/a] 0.0 \n", + "\n", + " bd5 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 212987.945931 \n", + " [kW_th*h] 212987.945931 \n", + "Electricity demand operation [kW_el*h/a] 23310.014158 \n", + " [kW_el*h] 23310.014158 \n", + "Electricity purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " commodCosts [€/a] NaN \n", + " operation [kW_CH4_LHV*h/a] NaN \n", + " [kW_CH4_LHV*h] NaN \n", + "PV NPVcontribution [€] 0.0 \n", + " TAC [€/a] 0.0 \n", + " capacity [kW_el] 0.0 \n", + " capexCap [€/a] 0.0 \n", + " capexIfBuilt [€/a] 0.0 \n", + " commissioning [kW_el] 0.0 \n", + " invest [€] 0.0 \n", + " isBuilt [-] 0.0 \n", + " operation [kW_el*h/a] 0.0 \n", + " [kW_el*h] 0.0 \n", + " opexIfBuilt [€/a] 0.0 \n", + "\n", + " bd6 grid1 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] 213360.620262 NaN \n", + " [kW_th*h] 213360.620262 NaN \n", + "Electricity demand operation [kW_el*h/a] 19718.252364 NaN \n", + " [kW_el*h] 19718.252364 NaN \n", + "Electricity purchase NPVcontribution [€] 0 0 \n", + " TAC [€/a] 0 0 \n", + " commodCosts [€/a] NaN NaN \n", + " operation [kW_el*h/a] NaN NaN \n", + " [kW_el*h] NaN NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 0 \n", + " TAC [€/a] 0 0 \n", + " commodCosts [€/a] NaN NaN \n", + " operation [kW_CH4_LHV*h/a] NaN NaN \n", + " [kW_CH4_LHV*h] NaN NaN \n", + "PV NPVcontribution [€] 0.0 0 \n", + " TAC [€/a] 0.0 0 \n", + " capacity [kW_el] 0.0 NaN \n", + " capexCap [€/a] 0.0 NaN \n", + " capexIfBuilt [€/a] 0.0 NaN \n", + " commissioning [kW_el] 0.0 NaN \n", + " invest [€] 0.0 NaN \n", + " isBuilt [-] 0.0 NaN \n", + " operation [kW_el*h/a] 0.0 NaN \n", + " [kW_el*h] 0.0 NaN \n", + " opexIfBuilt [€/a] 0.0 NaN \n", + "\n", + " grid2 grid3 grid4 grid5 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] NaN NaN NaN NaN \n", + " [kW_th*h] NaN NaN NaN NaN \n", + "Electricity demand operation [kW_el*h/a] NaN NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN NaN \n", + "Electricity purchase NPVcontribution [€] 0 0 0 0 \n", + " TAC [€/a] 0 0 0 0 \n", + " commodCosts [€/a] NaN NaN NaN NaN \n", + " operation [kW_el*h/a] NaN NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 0 0 0 \n", + " TAC [€/a] 0 0 0 0 \n", + " commodCosts [€/a] NaN NaN NaN NaN \n", + " operation [kW_CH4_LHV*h/a] NaN NaN NaN NaN \n", + " [kW_CH4_LHV*h] NaN NaN NaN NaN \n", + "PV NPVcontribution [€] 0 0 0 0 \n", + " TAC [€/a] 0 0 0 0 \n", + " capacity [kW_el] NaN NaN NaN NaN \n", + " capexCap [€/a] NaN NaN NaN NaN \n", + " capexIfBuilt [€/a] NaN NaN NaN NaN \n", + " commissioning [kW_el] NaN NaN NaN NaN \n", + " invest [€] NaN NaN NaN NaN \n", + " isBuilt [-] NaN NaN NaN NaN \n", + " operation [kW_el*h/a] NaN NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN NaN \n", + " opexIfBuilt [€/a] NaN NaN NaN NaN \n", + "\n", + " grid6 grid7 \\\n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] NaN NaN \n", + " [kW_th*h] NaN NaN \n", + "Electricity demand operation [kW_el*h/a] NaN NaN \n", + " [kW_el*h] NaN NaN \n", + "Electricity purchase NPVcontribution [€] 0 0 \n", + " TAC [€/a] 0 0 \n", + " commodCosts [€/a] NaN NaN \n", + " operation [kW_el*h/a] NaN NaN \n", + " [kW_el*h] NaN NaN \n", + "NaturalGas purchase NPVcontribution [€] 0 0 \n", + " TAC [€/a] 0 0 \n", + " commodCosts [€/a] NaN NaN \n", + " operation [kW_CH4_LHV*h/a] NaN NaN \n", + " [kW_CH4_LHV*h] NaN NaN \n", + "PV NPVcontribution [€] 0 0 \n", + " TAC [€/a] 0 0 \n", + " capacity [kW_el] NaN NaN \n", + " capexCap [€/a] NaN NaN \n", + " capexIfBuilt [€/a] NaN NaN \n", + " commissioning [kW_el] NaN NaN \n", + " invest [€] NaN NaN \n", + " isBuilt [-] NaN NaN \n", + " operation [kW_el*h/a] NaN NaN \n", + " [kW_el*h] NaN NaN \n", + " opexIfBuilt [€/a] NaN NaN \n", + "\n", + " transformer \n", + "Component Property Unit \n", + "BuildingsHeat operation [kW_th*h/a] NaN \n", + " [kW_th*h] NaN \n", + "Electricity demand operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + "Electricity purchase NPVcontribution [€] 30842.495427 \n", + " TAC [€/a] 30842.495427 \n", + " commodCosts [€/a] 30842.495427 \n", + " operation [kW_el*h/a] 103498.306802 \n", + " [kW_el*h] 103498.306802 \n", + "NaturalGas purchase NPVcontribution [€] 91270.980537 \n", + " TAC [€/a] 91270.980537 \n", + " commodCosts [€/a] 91270.980537 \n", + " operation [kW_CH4_LHV*h/a] 1404168.931345 \n", + " [kW_CH4_LHV*h] 1404168.931345 \n", + "PV NPVcontribution [€] 0 \n", + " TAC [€/a] 0 \n", + " capacity [kW_el] NaN \n", + " capexCap [€/a] NaN \n", + " capexIfBuilt [€/a] NaN \n", + " commissioning [kW_el] NaN \n", + " invest [€] NaN \n", + " isBuilt [-] NaN \n", + " operation [kW_el*h/a] NaN \n", + " [kW_el*h] NaN \n", + " opexIfBuilt [€/a] NaN " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"bd1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = fn.plotOperationColorMap(esM, \"Electricity purchase\", \"transformer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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bd1bd2bd3bd4bd5bd6grid1grid2grid3grid4grid5grid6grid7transformer
ComponentPropertyUnit
BoilerNPVcontribution[€]883.607351980.718203937.6439931004.157289956.361348955.4526580.00.00.00.00.00.00.00.0
TAC[€/a]859.607351956.718203913.643993980.157289932.361348931.4526580.00.00.00.00.00.00.00.0
capacity[kW_th]88.823444102.02112696.1672105.20657498.7109598.5874560.00.00.00.00.00.00.00.0
capexCap[€/a]653.57845750.689302707.615092774.128388726.332447725.4237570.00.00.00.00.00.00.00.0
capexIfBuilt[€/a]206.028901206.028901206.028901206.028901206.028901206.0289010.00.00.00.00.00.00.00.0
commissioning[kW_th]88.823444102.02112696.1672105.20657498.7109598.5874560.00.00.00.00.00.00.00.0
invest[€]11682.34443413002.112612416.72002713320.6574312671.09499212658.7456020.00.00.00.00.00.00.00.0
isBuilt[-]1.01.01.01.01.01.00.00.00.00.00.00.00.00.0
operation[kW_th*h/a]213048.223269211990.635343212185.711381212891.770831213017.011012213383.8584780.00.00.00.00.00.00.00.0
[kW_th*h]213048.223269211990.635343212185.711381212891.770831213017.011012213383.8584780.00.00.00.00.00.00.00.0
opexIfBuilt[€/a]24.024.024.024.024.024.00.00.00.00.00.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " bd1 bd2 \\\n", + "Component Property Unit \n", + "Boiler NPVcontribution [€] 883.607351 980.718203 \n", + " TAC [€/a] 859.607351 956.718203 \n", + " capacity [kW_th] 88.823444 102.021126 \n", + " capexCap [€/a] 653.57845 750.689302 \n", + " capexIfBuilt [€/a] 206.028901 206.028901 \n", + " commissioning [kW_th] 88.823444 102.021126 \n", + " invest [€] 11682.344434 13002.1126 \n", + " isBuilt [-] 1.0 1.0 \n", + " operation [kW_th*h/a] 213048.223269 211990.635343 \n", + " [kW_th*h] 213048.223269 211990.635343 \n", + " opexIfBuilt [€/a] 24.0 24.0 \n", + "\n", + " bd3 bd4 \\\n", + "Component Property Unit \n", + "Boiler NPVcontribution [€] 937.643993 1004.157289 \n", + " TAC [€/a] 913.643993 980.157289 \n", + " capacity [kW_th] 96.1672 105.206574 \n", + " capexCap [€/a] 707.615092 774.128388 \n", + " capexIfBuilt [€/a] 206.028901 206.028901 \n", + " commissioning [kW_th] 96.1672 105.206574 \n", + " invest [€] 12416.720027 13320.65743 \n", + " isBuilt [-] 1.0 1.0 \n", + " operation [kW_th*h/a] 212185.711381 212891.770831 \n", + " [kW_th*h] 212185.711381 212891.770831 \n", + " opexIfBuilt [€/a] 24.0 24.0 \n", + "\n", + " bd5 bd6 grid1 \\\n", + "Component Property Unit \n", + "Boiler NPVcontribution [€] 956.361348 955.452658 0.0 \n", + " TAC [€/a] 932.361348 931.452658 0.0 \n", + " capacity [kW_th] 98.71095 98.587456 0.0 \n", + " capexCap [€/a] 726.332447 725.423757 0.0 \n", + " capexIfBuilt [€/a] 206.028901 206.028901 0.0 \n", + " commissioning [kW_th] 98.71095 98.587456 0.0 \n", + " invest [€] 12671.094992 12658.745602 0.0 \n", + " isBuilt [-] 1.0 1.0 0.0 \n", + " operation [kW_th*h/a] 213017.011012 213383.858478 0.0 \n", + " [kW_th*h] 213017.011012 213383.858478 0.0 \n", + " opexIfBuilt [€/a] 24.0 24.0 0.0 \n", + "\n", + " grid2 grid3 grid4 grid5 grid6 grid7 \\\n", + "Component Property Unit \n", + "Boiler NPVcontribution [€] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " TAC [€/a] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " capacity [kW_th] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " capexCap [€/a] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " capexIfBuilt [€/a] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " commissioning [kW_th] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " invest [€] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " isBuilt [-] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " operation [kW_th*h/a] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " [kW_th*h] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + " opexIfBuilt [€/a] 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " transformer \n", + "Component Property Unit \n", + "Boiler NPVcontribution [€] 0.0 \n", + " TAC [€/a] 0.0 \n", + " capacity [kW_th] 0.0 \n", + " capexCap [€/a] 0.0 \n", + " capexIfBuilt [€/a] 0.0 \n", + " commissioning [kW_th] 0.0 \n", + " invest [€] 0.0 \n", + " isBuilt [-] 0.0 \n", + " operation [kW_th*h/a] 0.0 \n", + " [kW_th*h] 0.0 \n", + " opexIfBuilt [€/a] 0.0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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bd1bd2bd3bd4bd5bd6grid1grid2grid3grid4grid5grid6grid7transformer
ComponentPropertyUnit
Battery StorageoperationCharge[kW_el*h/a]-0.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
[kW_el*h]-0.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
operationDischarge[kW_el*h/a]-0.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
[kW_el*h]-0.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaN
Thermal StorageNPVcontribution[€]244.728977223.545122211.68068214.875936210.820358210.31317800000000
TAC[€/a]244.728977223.545122211.68068214.875936210.820358210.31317800000000
capacity[kW_th*h]158.340652144.551657136.828854138.908711136.268854135.93872NaNNaNNaNNaNNaNNaNNaNNaN
capexCap[€/a]243.256702222.072847210.208405213.403661209.348083208.840902NaNNaNNaNNaNNaNNaNNaNNaN
capexIfBuilt[€/a]1.4722751.4722751.4722751.4722751.4722751.472275NaNNaNNaNNaNNaNNaNNaNNaN
commissioning[kW_th*h]158.340652144.551657136.828854138.908711136.268854135.93872NaNNaNNaNNaNNaNNaNNaNNaN
invest[€]3823.1756393492.2397653306.8925073356.8090533293.4524933285.529273NaNNaNNaNNaNNaNNaNNaNNaN
isBuilt[-]1.01.01.01.01.01.0NaNNaNNaNNaNNaNNaNNaNNaN
operationCharge[kW_th*h/a]23892.88795415657.54575419326.48173714554.5857119826.53229926109.918425NaNNaNNaNNaNNaNNaNNaNNaN
[kW_th*h]23892.88795415657.54575419326.48173714554.5857119826.53229926109.918425NaNNaNNaNNaNNaNNaNNaNNaN
operationDischarge[kW_th*h/a]23853.00398515628.28360219300.97196214528.28398419797.46721826086.680208NaNNaNNaNNaNNaNNaNNaNNaN
[kW_th*h]23853.00398515628.28360219300.97196214528.28398419797.46721826086.680208NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " bd1 bd2 \\\n", + "Component Property Unit \n", + "Battery Storage operationCharge [kW_el*h/a] -0.0 0.0 \n", + " [kW_el*h] -0.0 0.0 \n", + " operationDischarge [kW_el*h/a] -0.0 0.0 \n", + " [kW_el*h] -0.0 0.0 \n", + "Thermal Storage NPVcontribution [€] 244.728977 223.545122 \n", + " TAC [€/a] 244.728977 223.545122 \n", + " capacity [kW_th*h] 158.340652 144.551657 \n", + " capexCap [€/a] 243.256702 222.072847 \n", + " capexIfBuilt [€/a] 1.472275 1.472275 \n", + " commissioning [kW_th*h] 158.340652 144.551657 \n", + " invest [€] 3823.175639 3492.239765 \n", + " isBuilt [-] 1.0 1.0 \n", + " operationCharge [kW_th*h/a] 23892.887954 15657.545754 \n", + " [kW_th*h] 23892.887954 15657.545754 \n", + " operationDischarge [kW_th*h/a] 23853.003985 15628.283602 \n", + " [kW_th*h] 23853.003985 15628.283602 \n", + "\n", + " bd3 bd4 \\\n", + "Component Property Unit \n", + "Battery Storage operationCharge [kW_el*h/a] 0.0 0.0 \n", + " [kW_el*h] 0.0 0.0 \n", + " operationDischarge [kW_el*h/a] 0.0 0.0 \n", + " [kW_el*h] 0.0 0.0 \n", + "Thermal Storage NPVcontribution [€] 211.68068 214.875936 \n", + " TAC [€/a] 211.68068 214.875936 \n", + " capacity [kW_th*h] 136.828854 138.908711 \n", + " capexCap [€/a] 210.208405 213.403661 \n", + " capexIfBuilt [€/a] 1.472275 1.472275 \n", + " commissioning [kW_th*h] 136.828854 138.908711 \n", + " invest [€] 3306.892507 3356.809053 \n", + " isBuilt [-] 1.0 1.0 \n", + " operationCharge [kW_th*h/a] 19326.481737 14554.58571 \n", + " [kW_th*h] 19326.481737 14554.58571 \n", + " operationDischarge [kW_th*h/a] 19300.971962 14528.283984 \n", + " [kW_th*h] 19300.971962 14528.283984 \n", + "\n", + " bd5 bd6 \\\n", + "Component Property Unit \n", + "Battery Storage operationCharge [kW_el*h/a] 0.0 0.0 \n", + " [kW_el*h] 0.0 0.0 \n", + " operationDischarge [kW_el*h/a] 0.0 0.0 \n", + " [kW_el*h] 0.0 0.0 \n", + "Thermal Storage NPVcontribution [€] 210.820358 210.313178 \n", + " TAC [€/a] 210.820358 210.313178 \n", + " capacity [kW_th*h] 136.268854 135.93872 \n", + " capexCap [€/a] 209.348083 208.840902 \n", + " capexIfBuilt [€/a] 1.472275 1.472275 \n", + " commissioning [kW_th*h] 136.268854 135.93872 \n", + " invest [€] 3293.452493 3285.529273 \n", + " isBuilt [-] 1.0 1.0 \n", + " operationCharge [kW_th*h/a] 19826.532299 26109.918425 \n", + " [kW_th*h] 19826.532299 26109.918425 \n", + " operationDischarge [kW_th*h/a] 19797.467218 26086.680208 \n", + " [kW_th*h] 19797.467218 26086.680208 \n", + "\n", + " grid1 grid2 grid3 grid4 grid5 \\\n", + "Component Property Unit \n", + "Battery Storage operationCharge [kW_el*h/a] NaN NaN NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN NaN NaN \n", + " operationDischarge [kW_el*h/a] NaN NaN NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN NaN NaN \n", + "Thermal Storage NPVcontribution [€] 0 0 0 0 0 \n", + " TAC [€/a] 0 0 0 0 0 \n", + " capacity [kW_th*h] NaN NaN NaN NaN NaN \n", + " capexCap [€/a] NaN NaN NaN NaN NaN \n", + " capexIfBuilt [€/a] NaN NaN NaN NaN NaN \n", + " commissioning [kW_th*h] NaN NaN NaN NaN NaN \n", + " invest [€] NaN NaN NaN NaN NaN \n", + " isBuilt [-] NaN NaN NaN NaN NaN \n", + " operationCharge [kW_th*h/a] NaN NaN NaN NaN NaN \n", + " [kW_th*h] NaN NaN NaN NaN NaN \n", + " operationDischarge [kW_th*h/a] NaN NaN NaN NaN NaN \n", + " [kW_th*h] NaN NaN NaN NaN NaN \n", + "\n", + " grid6 grid7 transformer \n", + "Component Property Unit \n", + "Battery Storage operationCharge [kW_el*h/a] NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN \n", + " operationDischarge [kW_el*h/a] NaN NaN NaN \n", + " [kW_el*h] NaN NaN NaN \n", + "Thermal Storage NPVcontribution [€] 0 0 0 \n", + " TAC [€/a] 0 0 0 \n", + " capacity [kW_th*h] NaN NaN NaN \n", + " capexCap [€/a] NaN NaN NaN \n", + " capexIfBuilt [€/a] NaN NaN NaN \n", + " commissioning [kW_th*h] NaN NaN NaN \n", + " invest [€] NaN NaN NaN \n", + " isBuilt [-] NaN NaN NaN \n", + " operationCharge [kW_th*h/a] NaN NaN NaN \n", + " [kW_th*h] NaN NaN NaN \n", + " operationDischarge [kW_th*h/a] NaN NaN NaN \n", + " [kW_th*h] NaN NaN NaN " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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"NG_Distribution_Grid operation [kW_CH4_LHV*h] grid4 NaN \n", + " grid5 NaN \n", + " grid6 NaN \n", + " grid7 NaN \n", + " transformer NaN \n", + "\n", + "[102 rows x 14 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/District_Optimization/InputData/E_Demand.xlsx b/examples/05_District_Optimization/InputData/E_Demand.xlsx similarity index 100% rename from examples/District_Optimization/InputData/E_Demand.xlsx rename to examples/05_District_Optimization/InputData/E_Demand.xlsx diff --git a/examples/District_Optimization/InputData/Heat_Demand.xlsx b/examples/05_District_Optimization/InputData/Heat_Demand.xlsx similarity index 100% rename from examples/District_Optimization/InputData/Heat_Demand.xlsx rename to examples/05_District_Optimization/InputData/Heat_Demand.xlsx diff --git a/examples/District_Optimization/InputData/Locations.xlsx b/examples/05_District_Optimization/InputData/Locations.xlsx similarity index 100% rename from examples/District_Optimization/InputData/Locations.xlsx rename to examples/05_District_Optimization/InputData/Locations.xlsx diff --git a/examples/District_Optimization/InputData/PV_Capacity.xlsx b/examples/05_District_Optimization/InputData/PV_Capacity.xlsx similarity index 100% rename from examples/District_Optimization/InputData/PV_Capacity.xlsx rename to examples/05_District_Optimization/InputData/PV_Capacity.xlsx diff --git a/examples/District_Optimization/InputData/PV_Generation.xlsx b/examples/05_District_Optimization/InputData/PV_Generation.xlsx similarity index 100% rename from examples/District_Optimization/InputData/PV_Generation.xlsx rename to examples/05_District_Optimization/InputData/PV_Generation.xlsx diff --git a/examples/District_Optimization/InputData/Storage_capacities.xlsx b/examples/05_District_Optimization/InputData/Storage_capacities.xlsx similarity index 100% rename from examples/District_Optimization/InputData/Storage_capacities.xlsx rename to examples/05_District_Optimization/InputData/Storage_capacities.xlsx diff --git a/examples/District_Optimization/InputData/grid_capacity_matrix.xlsx b/examples/05_District_Optimization/InputData/grid_capacity_matrix.xlsx similarity index 100% rename from examples/District_Optimization/InputData/grid_capacity_matrix.xlsx rename to examples/05_District_Optimization/InputData/grid_capacity_matrix.xlsx diff --git a/examples/District_Optimization/InputData/grid_length_matrix.xlsx b/examples/05_District_Optimization/InputData/grid_length_matrix.xlsx similarity index 100% rename from examples/District_Optimization/InputData/grid_length_matrix.xlsx rename to examples/05_District_Optimization/InputData/grid_length_matrix.xlsx diff --git a/examples/District_Optimization/InputData/purchaseElectricity.xlsx b/examples/05_District_Optimization/InputData/purchaseElectricity.xlsx similarity index 100% rename from examples/District_Optimization/InputData/purchaseElectricity.xlsx rename to examples/05_District_Optimization/InputData/purchaseElectricity.xlsx diff --git a/examples/District_Optimization/InputData/purchaseNaturalGas.xlsx b/examples/05_District_Optimization/InputData/purchaseNaturalGas.xlsx similarity index 100% rename from examples/District_Optimization/InputData/purchaseNaturalGas.xlsx rename to examples/05_District_Optimization/InputData/purchaseNaturalGas.xlsx diff --git a/examples/District_Optimization/getData.py b/examples/05_District_Optimization/getData.py similarity index 100% rename from examples/District_Optimization/getData.py rename to examples/05_District_Optimization/getData.py diff --git a/examples/06_Water_Supply_System/06_Water Supply System.ipynb b/examples/06_Water_Supply_System/06_Water Supply System.ipynb new file mode 100644 index 00000000..4c5794ce --- /dev/null +++ b/examples/06_Water_Supply_System/06_Water Supply System.ipynb @@ -0,0 +1,1369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import fine as fn\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Water supply of a small mountain village\n", + "\n", + "Two new houses (house 5 and 6) were built in a small mountain village in Utopia which requires an update of the existing clean water supply system of the village which now consists of 6 houses:\n", + "\n", + "\n", + "\n", + "\n", + "### Water demand\n", + "The demand for clean water occurs in spring between 5 am and 11 pm, in summer between 4 am and 12 pm, in autumn between 5 am and 11 pm and in winter between 6 am and 11 pm. The demand for one house assumes random values between 0 to 1 Uh (Unit*hour) during the demand hours. These values are uniformly distributed and are 0 outside the demand hours.\n", + "\n", + "### Water supply \n", + "The water supply comes from a small tributary of a glacier river, which provides more water in summer and less in winter: the profile is given for each hour of the year as\n", + "\n", + "f(t) = 8 \\* sin(π*t/8760) + g(t) \n", + " \n", + "where g(t) is a uniformly distributed random value between 0 and 4.\n", + "\n", + "### Water storage\n", + "Clean water can be stored in a water tank (newly purchased). The invest per capacity is 100€/Uh, the economic lifetime is 20 years.\n", + "\n", + "### Water treatment\n", + "The river water is converted to clean water in a water treatment plant (newly purchased). The invest per capacity is 7000€/U, the economic lifetime is 20 years. Further, it needs some electricity wherefore it has operational cost of 0.05 €/U.\n", + "\n", + "### Water transmission\n", + "The clean water can be transported via water pipes, where some already exist between the houses 1-4, the water treatment plant and the\n", + "water tank, however new ones might need to\n", + "be built to connect the newly built houses or reinforce the transmission along the old pipes. The invest for new pipes per capacity is 100 €/(m\\*U), the invest if a new pipe route is built is 500 €/(m\\*U), the economic lifetime is 20 years.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "locations = [\n", + " \"House 1\",\n", + " \"House 2\",\n", + " \"House 3\",\n", + " \"House 4\",\n", + " \"House 5\",\n", + " \"House 6\",\n", + " \"Node 1\",\n", + " \"Node 2\",\n", + " \"Node 3\",\n", + " \"Node 4\",\n", + " \"Water treatment\",\n", + " \"Water tank\",\n", + "]\n", + "commodityUnitDict = {\"clean water\": \"U\", \"river water\": \"U\"}\n", + "commodities = {\"clean water\", \"river water\"}\n", + "numberOfTimeSteps = 8760\n", + "hoursPerTimeStep = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "esM = fn.EnergySystemModel(\n", + " locations=set(locations),\n", + " commodities=commodities,\n", + " numberOfTimeSteps=8760,\n", + " commodityUnitsDict=commodityUnitDict,\n", + " hoursPerTimeStep=1,\n", + " costUnit=\"1e3 Euro\",\n", + " lengthUnit=\"m\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Source" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "riverFlow = pd.DataFrame(np.zeros((8760, 12)), columns=locations)\n", + "np.random.seed(42)\n", + "riverFlow.loc[:, \"Water treatment\"] = np.random.uniform(0, 4, (8760)) + 8 * np.sin(\n", + " np.pi * np.arange(8760) / 8760\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Source(\n", + " esM=esM,\n", + " name=\"River\",\n", + " commodity=\"river water\",\n", + " hasCapacityVariable=False,\n", + " operationRateMax=riverFlow,\n", + " opexPerOperation=0.05,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conversion" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "eligibility = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], index=locations)\n", + "esM.add(\n", + " fn.Conversion(\n", + " esM=esM,\n", + " name=\"Water treatment plant\",\n", + " physicalUnit=\"U\",\n", + " commodityConversionFactors={\"river water\": -1, \"clean water\": 1},\n", + " hasCapacityVariable=True,\n", + " locationalEligibility=eligibility,\n", + " investPerCapacity=7,\n", + " opexPerCapacity=0.02 * 7,\n", + " interestRate=0.08,\n", + " economicLifetime=20,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Storage" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "eligibility = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], index=locations)\n", + "esM.add(\n", + " fn.Storage(\n", + " esM=esM,\n", + " name=\"Water tank\",\n", + " commodity=\"clean water\",\n", + " hasCapacityVariable=True,\n", + " chargeRate=1 / 24,\n", + " dischargeRate=1 / 24,\n", + " locationalEligibility=eligibility,\n", + " investPerCapacity=0.10,\n", + " opexPerCapacity=0.02 * 0.1,\n", + " interestRate=0.08,\n", + " economicLifetime=20,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transmission" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Distances between eligible regions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true, + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " House 1 House 2 House 3 House 4 House 5 House 6 Node 1 \\\n", + "House 1 0 0 0 0 0 0 0 \n", + "House 2 0 0 0 0 0 0 0 \n", + "House 3 0 0 0 0 0 0 0 \n", + "House 4 0 0 0 0 0 0 0 \n", + "House 5 0 0 0 0 0 0 38 \n", + "House 6 0 0 0 0 0 0 40 \n", + "Node 1 0 0 0 0 38 40 0 \n", + "Node 2 0 0 38 40 0 0 105 \n", + "Node 3 38 40 0 0 0 0 0 \n", + "Node 4 0 0 0 0 0 0 0 \n", + "Water treatment 0 0 0 0 0 0 0 \n", + "Water tank 0 0 0 0 0 0 0 \n", + "\n", + " Node 2 Node 3 Node 4 Water treatment Water tank \n", + "House 1 0 38 0 0 0 \n", + "House 2 0 40 0 0 0 \n", + "House 3 38 0 0 0 0 \n", + "House 4 40 0 0 0 0 \n", + "House 5 0 0 0 0 0 \n", + "House 6 0 0 0 0 0 \n", + "Node 1 105 0 0 0 0 \n", + "Node 2 0 100 0 0 0 \n", + "Node 3 100 0 30 0 0 \n", + "Node 4 0 30 0 20 50 \n", + "Water treatment 0 0 20 0 0 \n", + "Water tank 0 0 50 0 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "distances = np.array(\n", + " [\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 38, 40, 0, 105, 0, 0, 0, 0],\n", + " [0, 0, 38, 40, 0, 0, 105, 0, 100, 0, 0, 0],\n", + " [38, 40, 0, 0, 0, 0, 0, 100, 0, 30, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 20, 50],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 0],\n", + " ]\n", + ")\n", + "\n", + "distances = pd.DataFrame(distances, index=locations, columns=locations)\n", + "distances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Old water pipes" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true, + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0, 0, 0, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0],\n", + " [1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", + " ]\n", + ")\n", + "\n", + "eligibility = pd.DataFrame(incidence, index=locations, columns=locations)\n", + "eligibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The new are pipes are better but still lose 0.05%/m of the water they transport." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Transmission(\n", + " esM=esM,\n", + " name=\"New water pipes\",\n", + " commodity=\"clean water\",\n", + " losses=0.05e-2,\n", + " distances=distances,\n", + " hasCapacityVariable=True,\n", + " hasIsBuiltBinaryVariable=True,\n", + " bigM=100,\n", + " locationalEligibility=eligibility,\n", + " investPerCapacity=0.1,\n", + " investIfBuilt=0.5,\n", + " interestRate=0.08,\n", + " economicLifetime=50,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sink" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "winterHours = np.append(range(8520, 8760), range(1920))\n", + "springHours, summerHours, autumnHours = (\n", + " np.arange(1920, 4128),\n", + " np.arange(4128, 6384),\n", + " np.arange(6384, 8520),\n", + ")\n", + "\n", + "demand = pd.DataFrame(np.zeros((8760, 12)), columns=list(locations))\n", + "np.random.seed(42)\n", + "demand[locations[0:6]] = np.random.uniform(0, 1, (8760, 6))\n", + "\n", + "demand.loc[winterHours[(winterHours % 24 < 5) | (winterHours % 24 >= 23)]] = 0\n", + "demand.loc[springHours[(springHours % 24 < 4)]] = 0\n", + "demand.loc[summerHours[(summerHours % 24 < 5) | (summerHours % 24 >= 23)]] = 0\n", + "demand.loc[autumnHours[(autumnHours % 24 < 6) | (autumnHours % 24 >= 23)]] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19955.415289775883" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demand.sum().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "esM.add(\n", + " fn.Sink(\n", + " esM=esM,\n", + " name=\"Water demand\",\n", + " commodity=\"clean water\",\n", + " hasCapacityVariable=False,\n", + " operationRateFix=demand,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimize the system" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "esM.aggregateTemporally(numberOfTypicalPeriods=7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='LogToConsole=1 OptimalityTol=1e-6 crossover=1')\n", + "esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Selected results output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sources and Sinks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [], + "source": [ + "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conversion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [], + "source": [ + "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transmission" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [], + "source": [ + "esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [ + "nbval-check-output" + ] + }, + "outputs": [], + "source": [ + "esM.componentModelingDict[\"TransmissionModel\"].operationVariablesOptimum.sum(axis=1)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:percent", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Water_Supply_System/MountainVillage.png b/examples/06_Water_Supply_System/figures/MountainVillage.png similarity index 100% rename from examples/Water_Supply_System/MountainVillage.png rename to examples/06_Water_Supply_System/figures/MountainVillage.png diff --git a/examples/NetCDF_to_save_and_set_up_model_instance/NetCDF_to_save_and_set_up_model_instance.ipynb b/examples/07_NetCDF_to_save_and_set_up_model_instance/07_NetCDF_to_save_and_set_up_model_instance.ipynb similarity index 58% rename from examples/NetCDF_to_save_and_set_up_model_instance/NetCDF_to_save_and_set_up_model_instance.ipynb rename to examples/07_NetCDF_to_save_and_set_up_model_instance/07_NetCDF_to_save_and_set_up_model_instance.ipynb index 552a74a9..4afe0478 100644 --- a/examples/NetCDF_to_save_and_set_up_model_instance/NetCDF_to_save_and_set_up_model_instance.ipynb +++ b/examples/07_NetCDF_to_save_and_set_up_model_instance/07_NetCDF_to_save_and_set_up_model_instance.ipynb @@ -63,7 +63,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### STEP 1. Set up your ESM instance " + "### STEP 1. Set up your ESM instance " ] }, { @@ -75,11 +75,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using license file C:\\Users\\ra.maier\\gurobi.lic\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", "Academic license - for non-commercial use only\n", - "Read LP format model from file C:\\Users\\RAEF3F~1.MAI\\AppData\\Local\\Temp\\tmpwxqr6him.pyomo.lp\n", - "Reading time = 0.03 seconds\n", - "x115: 99 rows, 75 columns, 243 nonzeros\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpsvmlos8_.pyomo.lp\n", + "Reading time = 0.00 seconds\n", + "x1: 96 rows, 74 columns, 240 nonzeros\n", "Changed value of parameter QCPDual to 1\n", " Prev: 0 Min: 0 Max: 1 Default: 0\n", "Changed value of parameter Threads to 3\n", @@ -87,25 +87,25 @@ "Parameter logfile unchanged\n", " Value: Default: \n", "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", - "Optimize a model with 99 rows, 75 columns and 243 nonzeros\n", - "Model fingerprint: 0x7500b870\n", + "Optimize a model with 96 rows, 74 columns and 240 nonzeros\n", + "Model fingerprint: 0x9066f971\n", "Coefficient statistics:\n", " Matrix range [3e-01, 2e+03]\n", " Objective range [7e-03, 9e+01]\n", - " Bounds range [0e+00, 0e+00]\n", - " RHS range [1e+00, 2e+09]\n", - "Warning: Model contains large rhs\n", + " Bounds range [1e+07, 2e+09]\n", + " RHS range [0e+00, 0e+00]\n", + "Warning: Model contains large bounds\n", " Consider reformulating model or setting NumericFocus parameter\n", " to avoid numerical issues.\n", - "Presolve removed 49 rows and 41 columns\n", + "Presolve removed 46 rows and 40 columns\n", "Presolve time: 0.00s\n", "Presolved: 50 rows, 34 columns, 168 nonzeros\n", "\n", "Iteration Objective Primal Inf. Dual Inf. Time\n", - " 0 0.0000000e+00 7.555500e+07 0.000000e+00 0s\n", - " 24 3.8832953e+06 0.000000e+00 0.000000e+00 0s\n", + " 0 0.0000000e+00 3.675094e+07 0.000000e+00 0s\n", + " 17 3.8832953e+06 0.000000e+00 0.000000e+00 0s\n", "\n", - "Solved in 24 iterations and 0.02 seconds\n", + "Solved in 17 iterations and 0.00 seconds\n", "Optimal objective 3.883295266e+06\n" ] } @@ -121,7 +121,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### STEP 2. Conversion to xarray datasets and saving as NetCDF file\n", + "### STEP 2. Conversion to xarray datasets and saving as NetCDF file\n", "You can convert the esM to xarray datasets with `esm_to_datasets` and access Input, Parameters or Result.\n" ] }, @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -264,6 +264,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -276,7 +277,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -493,6 +494,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -514,14 +520,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -531,13 +539,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -575,7 +586,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -584,80 +596,80 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                    (space: 2, time: 4)\n",
+       "
<xarray.Dataset> Size: 528B\n",
+       "Dimensions:                          (space: 2, time: 4)\n",
        "Coordinates:\n",
-       "  * space                      (space) <U20 'ElectrolyzerLocation' 'IndustryL...\n",
-       "  * time                       (time) int64 0 1 2 3\n",
-       "Data variables: (12/16)\n",
-       "    NPVcontribution            (space) float64 1.52e+06 0.0\n",
-       "    TAC                        (space) float64 1.52e+06 0.0\n",
-       "    capacity                   (space) float64 nan nan\n",
-       "    capexCap                   (space) float64 nan nan\n",
-       "    capexIfBuilt               (space) float64 nan nan\n",
-       "    commissioning              (space) float64 nan nan\n",
-       "    ...                         ...\n",
-       "    isBuilt                    (space) float64 nan nan\n",
-       "    operation                  (space) float64 7.509e+07 nan\n",
-       "    opexCap                    (space) float64 nan nan\n",
-       "    opexIfBuilt                (space) float64 nan nan\n",
-       "    opexOp                     (space) float64 0.0 nan\n",
-       "    operationVariablesOptimum  (time, space) float64 1.877e+07 nan ... nan\n",
-       "Attributes: (12/15)\n",
-       "    NPVcontribution:  [1 Euro]\n",
-       "    TAC:              [1 Euro/a]\n",
-       "    capacity:         [kW$_{el}$]\n",
-       "    capexCap:         [1 Euro/a]\n",
-       "    capexIfBuilt:     [1 Euro/a]\n",
-       "    commissioning:    [kW$_{el}$]\n",
-       "    ...               ...\n",
-       "    invest:           [1 Euro]\n",
-       "    isBuilt:          [-]\n",
-       "    operation:        [kW$_{el}$*h]\n",
-       "    opexCap:          [1 Euro/a]\n",
-       "    opexIfBuilt:      [1 Euro/a]\n",
-       "    opexOp:           [1 Euro/a]
    • time
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      PandasIndex(RangeIndex(start=0, stop=4, step=1, name='time'))
    • space
      PandasIndex
      PandasIndex(Index(['ElectrolyzerLocation', 'IndustryLocation'], dtype='object', name='space'))
  • NPVcontribution :
    [1 Euro]
    TAC :
    [1 Euro/a]
    capacity :
    [kW$_{el}$]
    capexCap :
    [1 Euro/a]
    capexIfBuilt :
    [1 Euro/a]
    commissioning :
    [kW$_{el}$]
    commodCosts :
    [1 Euro/a]
    commodRevenues :
    [1 Euro/a]
    decommissioning :
    [kW$_{el}$]
    invest :
    [1 Euro]
    investLifetimeExtension :
    [1 Euro]
    isBuilt :
    [-]
    operation :
    [kW$_{el}$*h]
    opexCap :
    [1 Euro/a]
    opexIfBuilt :
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    opexOp :
    [1 Euro/a]
    revenueLifetimeShorteningResale :
    [1 Euro]
    • " ], "text/plain": [ - "\n", - "Dimensions: (space: 2, time: 4)\n", + " Size: 528B\n", + "Dimensions: (space: 2, time: 4)\n", "Coordinates:\n", - " * space (space) 
    <xarray.Dataset>\n",
+       "
    <xarray.Dataset> Size: 0B\n",
        "Dimensions:  ()\n",
        "Data variables:\n",
        "    *empty*\n",
-       "Attributes: (12/14)\n",
+       "Attributes: (12/15)\n",
        "    locations:                  {'ElectrolyzerLocation', 'IndustryLocation'}\n",
        "    commodities:                {'hydrogen', 'electricity'}\n",
        "    commodityUnitsDict:         {'electricity': 'kW$_{el}$', 'hydrogen': 'kW$...\n",
@@ -1037,19 +1061,19 @@
        "    hoursPerTimeStep:           2190\n",
        "    startYear:                  0\n",
        "    ...                         ...\n",
-       "    stochasticModel:            False\n",
        "    costUnit:                   1 Euro\n",
        "    lengthUnit:                 km\n",
        "    verboseLogLevel:            1\n",
        "    balanceLimit:               None\n",
-       "    lowerBound:                 False
    "
+       "    pathwayBalanceLimit:        None\n",
+       "    annuityPerpetuity:          False
    " ], "text/plain": [ - "\n", + " Size: 0B\n", "Dimensions: ()\n", "Data variables:\n", " *empty*\n", - "Attributes: (12/14)\n", + "Attributes: (12/15)\n", " locations: {'ElectrolyzerLocation', 'IndustryLocation'}\n", " commodities: {'hydrogen', 'electricity'}\n", " commodityUnitsDict: {'electricity': 'kW$_{el}$', 'hydrogen': 'kW$...\n", @@ -1057,15 +1081,15 @@ " hoursPerTimeStep: 2190\n", " startYear: 0\n", " ... ...\n", - " stochasticModel: False\n", " costUnit: 1 Euro\n", " lengthUnit: km\n", " verboseLogLevel: 1\n", " balanceLimit: None\n", - " lowerBound: False" + " pathwayBalanceLimit: None\n", + " annuityPerpetuity: False" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1083,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1096,14 +1120,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### STEP 3. Load esM from NetCDF file or xarray datasets\n", + "### STEP 3. Load esM from NetCDF file or xarray datasets\n", "\n", "You can load an esM from file with `netcdf_to_esm`." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -1112,7 +1136,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1179,7 +1203,7 @@ "3 0.0 13140000.0" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1197,7 +1221,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1273,7 +1297,7 @@ "3 0.0 13140000.0" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1284,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1321,6 +1345,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -1333,7 +1358,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -1550,6 +1575,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -1571,14 +1601,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -1588,13 +1620,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -1632,7 +1667,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -1641,30 +1677,30 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray 'operationVariablesOptimum' (time: 4, space: 2)>\n",
+       "
    <xarray.DataArray 'operationVariablesOptimum' (time: 4, space: 2)> Size: 64B\n",
        "array([[18771428.57142857,               nan],\n",
        "       [37542857.14285714,               nan],\n",
        "       [       0.        ,               nan],\n",
        "       [18771428.57142857,               nan]])\n",
        "Coordinates:\n",
-       "  * space    (space) <U20 'ElectrolyzerLocation' 'IndustryLocation'\n",
-       "  * time     (time) int64 0 1 2 3
    • time
      (time)
      int64
      0 1 2 3
      array([0, 1, 2, 3], dtype=int64)
    • space
      (space)
      <U20
      'ElectrolyzerLocation' 'Industry...
      array(['ElectrolyzerLocation', 'IndustryLocation'], dtype='<U20')
    • time
      PandasIndex
      PandasIndex(RangeIndex(start=0, stop=4, step=1, name='time'))
    • space
      PandasIndex
      PandasIndex(Index(['ElectrolyzerLocation', 'IndustryLocation'], dtype='object', name='space'))
    • " ], "text/plain": [ - "\n", + " Size: 64B\n", "array([[18771428.57142857, nan],\n", " [37542857.14285714, nan],\n", " [ 0. , nan],\n", " [18771428.57142857, nan]])\n", "Coordinates:\n", - " * space (space) " ] @@ -254,7 +227,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -489,7 +462,7 @@ "\n", "Clustering time series data with 7 typical periods and 24 time steps per period \n", "further clustered to 12 segments per period...\n", - "\t\t(3.0275 sec)\n", + "\t\t(3.8514 sec)\n", "\n" ] } @@ -523,111 +496,219 @@ "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.2200 sec)\n", + "\t\t(0.0937 sec)\n", "\n", "Declaring sets, variables and constraints for ConversionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0370 sec)\n", + "\t\t(0.1056 sec)\n", "\n", "Declaring sets, variables and constraints for StorageModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.3120 sec)\n", + "\t\t(0.2504 sec)\n", "\n", "Declaring sets, variables and constraints for LOPFModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0520 sec)\n", + "\t\t(0.0385 sec)\n", "\n", "Declaring sets, variables and constraints for TransmissionModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0540 sec)\n", + "\t\t(0.1211 sec)\n", "\n", "Declaring shared potential constraint...\n", - "\t\t(0.0000 sec)\n", + "\t\t(0.0010 sec)\n", "\n", "Declaring linked component quantity constraint...\n", "\t\t(0.0000 sec)\n", "\n", "Declaring commodity balances...\n", - "\t\t(0.0530 sec)\n", + "\t\t(0.0420 sec)\n", "\n", "\t\t(0.0000 sec)\n", "\n", "Declaring objective function...\n", - "\t\t(0.3905 sec)\n", + "\t\t(0.3129 sec)\n", + "\n", + "Either solver not selected or specified solver not available.gurobi is set as solver.\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", + "Academic license - for non-commercial use only\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpzuqnq1n1.pyomo.lp\n", + "Reading time = 0.04 seconds\n", + "x1: 15910 rows, 8034 columns, 46799 nonzeros\n", + "Changed value of parameter QCPDual to 1\n", + " Prev: 0 Min: 0 Max: 1 Default: 0\n", + "Changed value of parameter Threads to 3\n", + " Prev: 0 Min: 0 Max: 1024 Default: 0\n", + "Parameter logfile unchanged\n", + " Value: Default: \n", + "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", + "Optimize a model with 15910 rows, 8034 columns and 46799 nonzeros\n", + "Model fingerprint: 0x2452aedb\n", + "Coefficient statistics:\n", + " Matrix range [2e-07, 5e+02]\n", + " Objective range [6e-04, 1e-01]\n", + " Bounds range [2e-01, 3e+04]\n", + " RHS range [1e+00, 2e+01]\n", + "\n", + "Concurrent LP optimizer: dual simplex and barrier\n", + "Showing barrier log only...\n", + "\n", + "Presolve removed 6353 rows and 3591 columns\n", + "Presolve time: 0.02s\n", + "Presolved: 9557 rows, 4443 columns, 39743 nonzeros\n", + "\n", + "Ordering time: 0.01s\n", + "\n", + "Barrier statistics:\n", + " Dense cols : 102\n", + " Free vars : 112\n", + " AA' NZ : 1.258e+05\n", + " Factor NZ : 5.140e+05 (roughly 10 MBytes of memory)\n", + " Factor Ops : 7.465e+07 (less than 1 second per iteration)\n", + " Threads : 2\n", + "\n", + " Objective Residual\n", + "Iter Primal Dual Primal Dual Compl Time\n", + " 0 7.58096445e+05 -1.29625417e+03 1.89e+07 3.10e-02 4.92e+04 0s\n", + " 1 7.80599325e+05 -3.63506474e+05 1.80e+07 7.91e+01 4.68e+04 0s\n", + " 2 7.80656442e+05 -4.92134488e+04 1.80e+07 7.91e+01 4.67e+04 0s\n", + " 3 8.73217500e+05 -1.65645018e+05 1.61e+07 8.10e+01 4.43e+04 0s\n", + " 4 9.09128498e+05 -1.33278249e+06 1.45e+07 1.29e+02 3.83e+04 0s\n", + " 5 8.38987250e+05 -1.06427635e+07 1.11e+07 7.52e+02 3.25e+04 0s\n", + " 6 6.97797668e+05 -1.19105517e+07 7.03e+06 8.09e+02 2.22e+04 0s\n", + " 7 6.93868729e+05 -1.15343883e+07 6.90e+06 7.78e+02 2.16e+04 0s\n", + " 8 6.80862332e+05 -1.13594320e+07 6.50e+06 7.59e+02 2.05e+04 0s\n", + " 9 5.24778290e+05 -4.67675094e+06 3.16e+06 2.71e+02 9.82e+03 0s\n", + " 10 4.95945862e+05 -4.89270018e+06 2.42e+06 3.05e+02 7.35e+03 0s\n", + " 11 4.65984484e+05 -3.87566383e+06 1.83e+06 2.04e+02 5.74e+03 0s\n", + " 12 4.65961814e+05 -3.87521520e+06 1.83e+06 2.04e+02 5.66e+03 0s\n", + " 13 4.84652312e+05 -2.56343202e+06 1.61e+06 1.89e+02 5.08e+03 0s\n", + " 14 4.16314510e+05 -2.35643982e+06 7.99e+05 3.24e+02 2.61e+03 0s\n", + " 15 4.16295105e+05 -2.36014031e+06 7.99e+05 3.24e+02 2.59e+03 0s\n", + " 16 4.61349963e+05 -2.19469362e+06 4.78e+05 2.70e+02 2.23e+03 0s\n", + " 17 4.35712381e+05 -3.71093489e+06 3.39e+05 2.48e+02 1.84e+03 0s\n", + " 18 4.35540260e+05 -3.76595403e+06 3.38e+05 2.64e+02 1.84e+03 0s\n", + " 19 5.66816557e+05 -1.38583423e+06 1.63e+04 7.09e+01 5.20e+02 0s\n", + " 20 5.65261008e+05 -1.06734647e+06 1.65e+04 7.69e+01 5.10e+02 0s\n", + " 21 4.98936867e+05 -1.28594002e+06 1.17e+04 1.46e+02 4.32e+02 0s\n", + " 22 3.26476203e+05 -1.42473927e+06 5.83e+03 1.51e+02 2.98e+02 0s\n", + " 23 2.80108763e+05 -4.42691243e+06 4.18e+03 2.37e+02 2.26e+02 0s\n", + " 24 2.35190970e+05 -5.02662821e+06 3.06e+03 3.06e+02 1.77e+02 0s\n", + " 25 2.13967140e+05 -4.36548646e+06 2.55e+03 2.63e+02 1.48e+02 0s\n", + " 26 1.14663511e+05 -1.50128995e+06 3.63e+02 1.48e+02 3.53e+01 0s\n", + " 27 7.05482217e+04 -5.40391737e+05 9.09e+01 5.37e+01 1.19e+01 0s\n", + " 28 3.36033803e+04 -2.00445555e+05 2.31e+01 1.78e+01 4.16e+00 0s\n", + " 29 5.48672130e+03 -3.40718357e+04 2.30e+00 2.93e+00 6.93e-01 0s\n", + " 30 1.10258489e+03 -5.02750356e+03 3.38e-01 4.08e-01 1.52e-01 0s\n", + " 31 2.39699694e+02 -8.91320539e+02 4.41e-02 7.52e-02 2.95e-02 0s\n", + " 32 9.54466756e+01 -2.47885700e+02 6.28e-03 2.39e-02 8.29e-03 0s\n", + " 33 8.22619211e+01 -1.56061266e+02 4.57e-03 1.65e-02 5.82e-03 0s\n", + " 34 7.00313581e+01 -1.32621608e+02 2.79e-03 1.46e-02 4.53e-03 0s\n", + " 35 6.01309369e+01 -7.68670744e+01 1.93e-03 9.67e-03 3.15e-03 0s\n", + " 36 5.70489954e+01 -7.34204516e+01 1.70e-03 9.23e-03 2.98e-03 0s\n", + " 37 4.63966523e+01 -2.48714916e+01 8.97e-04 5.08e-03 1.58e-03 0s\n", + " 38 4.21836900e+01 1.09166586e-01 5.10e-04 2.96e-03 9.46e-04 0s\n", + " 39 4.03990525e+01 8.35830167e+00 3.73e-04 2.28e-03 7.20e-04 0s\n", + " 40 3.98485723e+01 1.06049546e+01 3.29e-04 2.09e-03 6.49e-04 0s\n", + " 41 3.80841880e+01 2.13146207e+01 1.98e-04 1.18e-03 3.91e-04 0s\n", + " 42 3.69359922e+01 2.82139684e+01 8.20e-05 6.17e-04 2.04e-04 0s\n", + " 43 3.65932834e+01 3.34537431e+01 3.58e-05 2.07e-04 8.68e-05 0s\n", + " 44 3.64729673e+01 3.48436449e+01 2.21e-05 1.05e-04 4.67e-05 0s\n", + " 45 3.63752509e+01 3.57646367e+01 8.89e-06 3.71e-05 1.88e-05 0s\n", + " 46 3.63328669e+01 3.59823056e+01 2.88e-06 2.21e-05 1.02e-05 1s\n", + " 47 3.63155239e+01 3.61466652e+01 7.12e-07 1.08e-05 4.73e-06 1s\n", + " 48 3.63131990e+01 3.62328361e+01 4.90e-07 5.17e-06 2.42e-06 1s\n", + " 49 3.63106888e+01 3.62519409e+01 2.89e-07 3.80e-06 1.74e-06 1s\n", + " 50 3.63101346e+01 3.62796117e+01 2.44e-07 1.86e-06 9.85e-07 1s\n", + " 51 3.63091735e+01 3.62976780e+01 1.60e-07 5.19e-07 4.26e-07 1s\n", + " 52 3.63075276e+01 3.63049019e+01 2.11e-08 1.18e-07 9.78e-08 1s\n", + " 53 3.63073413e+01 3.63058898e+01 8.51e-09 6.41e-08 5.10e-08 1s\n", + " 54 3.63072686e+01 3.63067786e+01 4.06e-09 1.92e-08 1.91e-08 1s\n", + " 55 3.63072386e+01 3.63070507e+01 2.42e-09 6.86e-09 8.29e-09 1s\n", + " 56 3.63072268e+01 3.63071113e+01 1.75e-09 4.00e-09 5.36e-09 1s\n", + " 57 3.63071995e+01 3.63071598e+01 3.69e-10 1.47e-09 1.63e-09 1s\n", + " 58 3.63071971e+01 3.63071927e+01 7.32e-10 5.02e-10 2.24e-10 1s\n", + " 59 3.63071971e+01 3.63071927e+01 4.77e-07 5.02e-10 2.23e-10 1s\n", + " 60 3.63071968e+01 3.63075269e+01 4.08e-07 3.49e-07 1.74e-10 1s\n", + " 61 3.63071968e+01 3.63075463e+01 1.11e-06 3.48e-07 1.76e-10 1s\n", + " 62 3.63071968e+01 3.63075463e+01 1.11e-06 3.48e-07 1.76e-10 1s\n", + " 63 3.63071967e+01 3.63075463e+01 1.11e-06 3.48e-07 1.76e-10 1s\n", + " 64 3.63071967e+01 3.63075463e+01 1.11e-06 3.48e-07 1.75e-10 1s\n", + " 65 3.63071967e+01 3.63075463e+01 1.11e-06 3.48e-07 1.75e-10 1s\n", + " 66 3.63071967e+01 3.63075463e+01 1.11e-06 3.48e-07 1.75e-10 1s\n", + " 67 3.63071965e+01 3.63075310e+01 8.30e-07 4.86e-07 1.53e-10 1s\n", + " 68 3.63071963e+01 3.63075005e+01 6.06e-07 4.07e-07 1.22e-10 1s\n", + " 69 3.63071963e+01 3.63074955e+01 6.05e-07 4.05e-07 1.21e-10 1s\n", + " 70 3.63071963e+01 3.63074954e+01 6.05e-07 4.05e-07 1.21e-10 1s\n", + " 71 3.63071963e+01 3.63074920e+01 6.07e-07 4.00e-07 1.20e-10 1s\n", + " 72 3.63071963e+01 3.63074914e+01 6.06e-07 4.01e-07 1.20e-10 1s\n", + " 73 3.63071963e+01 3.63072785e+01 5.93e-07 5.92e-07 1.19e-10 1s\n", + " 74 3.63071963e+01 3.63072785e+01 5.94e-07 5.92e-07 1.19e-10 1s\n", + " 75 3.63071963e+01 3.63072785e+01 5.78e-07 5.92e-07 1.19e-10 1s\n", + " 76 3.63071963e+01 3.63072785e+01 6.69e-07 5.92e-07 1.19e-10 1s\n", + " 77 3.63071959e+01 3.63073846e+01 1.01e-06 6.86e-07 8.60e-11 1s\n", + "\n", + "Barrier solved model in 77 iterations and 0.84 seconds\n", + "Optimal objective 3.63071971e+01\n", + "\n", + "Crossover log...\n", + "\n", + " 0 DPushes remaining with DInf 0.0000000e+00 1s\n", + "\n", + " 2088 PPushes remaining with PInf 1.0917287e-04 1s\n", + " 0 PPushes remaining with PInf 0.0000000e+00 1s\n", + "\n", + " Push phase complete: Pinf 0.0000000e+00, Dinf 2.6076840e-02 1s\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 2947 3.6307198e+01 0.000000e+00 2.607684e-02 1s\n", + " 3011 3.6307197e+01 0.000000e+00 0.000000e+00 1s\n", "\n", - "Either solver not selected or specified solver not available.glpk is set as solver.\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\Julian\\AppData\\Local\\Temp\\tmplibouqxt.glpk.raw --wglp C:\\Users\\Julian\\AppData\\Local\\Temp\\tmp8nqrncti.glpk.glp\n", - " --cpxlp C:\\Users\\Julian\\AppData\\Local\\Temp\\tmp4qeydao0.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\Julian\\AppData\\Local\\Temp\\tmp4qeydao0.pyomo.lp'...\n", - "15911 rows, 8035 columns, 46800 non-zeros\n", - "102605 lines were read\n", - "Writing problem data to 'C:\\Users\\Julian\\AppData\\Local\\Temp\\tmp8nqrncti.glpk.glp'...\n", - "81894 lines were written\n", - "GLPK Simplex Optimizer 5.0\n", - "15911 rows, 8035 columns, 46800 non-zeros\n", - "Preprocessing...\n", - "15481 rows, 6997 columns, 43821 non-zeros\n", - "Scaling...\n", - " A: min|aij| = 2.038e-07 max|aij| = 4.545e+02 ratio = 2.230e+09\n", - "GM: min|aij| = 1.637e-01 max|aij| = 6.108e+00 ratio = 3.731e+01\n", - "EQ: min|aij| = 2.789e-02 max|aij| = 1.000e+00 ratio = 3.585e+01\n", - "Constructing initial basis...\n", - "Size of triangular part is 12777\n", - " 0: obj = 0.000000000e+00 inf = 6.021e+03 (826)\n", - "Warning: basis matrix is ill-conditioned (cond = 6.58e+12)\n", - "Perturbing LP to avoid instability [888]...\n", - " 2547: obj = 4.753528782e+01 inf = 7.764e-06 (0) 25\n", - "Removing LP perturbation [5449]...\n", - "* 5449: obj = 3.630719724e+01 inf = 1.599e-10 (0) 28\n", - "OPTIMAL LP SOLUTION FOUND\n", - "Time used: 2.6 secs\n", - "Memory used: 19.1 Mb (20041059 bytes)\n", - "Writing basic solution to 'C:\\Users\\Julian\\AppData\\Local\\Temp\\tmplibouqxt.glpk.raw'...\n", - "23955 lines were written\n", + "Solved with barrier\n", + "Solved in 3011 iterations and 0.97 seconds\n", + "Optimal objective 3.630719686e+01\n", "\n", "Status: ok\n", + "Return code: 0\n", + "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", "Termination condition: optimal\n", - "Statistics: \n", - " Branch and bound: \n", - " Number of bounded subproblems: 0\n", - " Number of created subproblems: 0\n", + "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Wall time: 0.9740200042724609\n", "Error rc: 0\n", - "Time: 2.873870372772217\n", + "Time: 1.4427695274353027\n", "\n", "\n", - "Name: unknown\n", - "Lower bound: 36.3071972379649\n", - "Upper bound: 36.3071972379649\n", + "Name: x1_copy\n", + "Lower bound: 36.30719686193935\n", + "Upper bound: 36.30719686193935\n", "Number of objectives: 1\n", - "Number of constraints: 15911\n", - "Number of variables: 8035\n", - "Number of nonzeros: 46800\n", + "Number of constraints: 15910\n", + "Number of variables: 8034\n", + "Number of binary variables: 0\n", + "Number of integer variables: 0\n", + "Number of continuous variables: 8034\n", + "Number of nonzeros: 46799\n", "Sense: minimize\n", "\n", - "Solve time: 3.721921920776367 sec.\n", + "Solve time: 1.9183151721954346 sec.\n", "\n", "Processing optimization output...\n", - "for SourceSinkModel ... (0.4480sec)\n", - "for ConversionModel ... (0.2020sec)\n" + "for SourceSinkModel ... (0.4365sec)\n", + "for ConversionModel ... (0.3531sec)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Programming\\fine\\FINE\\storage.py:1972: UserWarning: Charge and discharge at the same time for component Li-ion batteries\n", - " warnings.warn(\n", - "C:\\Programming\\fine\\FINE\\storage.py:1972: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", " warnings.warn(\n" ] }, @@ -635,10 +716,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "for StorageModel ... (0.5970sec)\n", - "for LOPFModel ... (0.5320sec)\n", - "for TransmissionModel ... (0.3590sec)\n", - "\t\t(2.1380 sec)\n", + "for StorageModel ... (0.7259sec)\n", + "for LOPFModel ... (0.4717sec)\n", + "for TransmissionModel ... (0.5247sec)\n", + "\t\t(2.5288 sec)\n", "\n" ] } @@ -653,7 +734,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -667,7 +748,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/Spatial_and_technology_aggregation/Technology_aggregation.ipynb b/examples/08_Spatial_and_technology_aggregation/08b_Technology_aggregation.ipynb similarity index 94% rename from examples/Spatial_and_technology_aggregation/Technology_aggregation.ipynb rename to examples/08_Spatial_and_technology_aggregation/08b_Technology_aggregation.ipynb index 4adbe0c2..8af87f99 100644 --- a/examples/Spatial_and_technology_aggregation/Technology_aggregation.ipynb +++ b/examples/08_Spatial_and_technology_aggregation/08b_Technology_aggregation.ipynb @@ -4,20 +4,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Programming\\fine\\FINE\\subclasses\\conversionPartLoad.py:12: UserWarning: \n", - " In order to user the `conversionPartLoadClass` you need to install GPyOpt. \n", - " GPyOpt reached end of maintenance and does not work with current versions of e.g. pandas.\n", - " Make sure to downgrade necessary packages to make GPyOpt work for your Python installation.\n", - " \n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import sys\n", @@ -49,7 +36,7 @@ "\n", "\n", "\n", - "The figure above dipicts the basic idea behind technology aggregation. Here, the number of VRES within each region is reduced to a desired number. To give you an example, if the results of your PV simulation are spatially detailed or spatially highly resolved, then you could reduce these to a few types within each region. The time series profiles are matched during grouping of these technologies. \n" + "The figure above depicts the basic idea behind technology aggregation. Here, the number of VRES within each region is reduced to a desired number. To give you an example, if the results of your PV simulation are spatially detailed or spatially highly resolved, then you could reduce these to a few types within each region. The time series profiles are matched during grouping of these technologies. \n" ] }, { @@ -455,19 +442,19 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset>\n",
+       "
    <xarray.Dataset> Size: 361MB\n",
        "Dimensions:      (y: 117, x: 88, time: 8760)\n",
        "Coordinates:\n",
-       "  * y            (y) float64 2.832e+06 2.838e+06 ... 3.408e+06 3.412e+06\n",
-       "  * x            (x) float64 4.092e+06 4.098e+06 ... 4.522e+06 4.528e+06\n",
-       "    spatial_ref  int32 ...\n",
-       "  * time         (time) datetime64[ns] 2015-01-01 ... 2015-12-31T23:00:00\n",
+       "  * y            (y) float64 936B 2.832e+06 2.838e+06 ... 3.408e+06 3.412e+06\n",
+       "  * x            (x) float64 704B 4.092e+06 4.098e+06 ... 4.522e+06 4.528e+06\n",
+       "    spatial_ref  int32 4B ...\n",
+       "  * time         (time) datetime64[ns] 70kB 2015-01-01 ... 2015-12-31T23:00:00\n",
        "Data variables:\n",
-       "    capacity     (y, x) float64 ...\n",
-       "    capfac       (time, y, x) float32 ...\n",
+       "    capacity     (y, x) float64 82kB ...\n",
+       "    capfac       (time, y, x) float32 361MB ...\n",
        "Attributes:\n",
        "    xy_reference_system:  EPSG:3035\n",
-       "    grid_mapping:         spatial_ref
  • xy_reference_system :
    EPSG:3035
    grid_mapping :
    spatial_ref
    • " ], "text/plain": [ - "\n", + " Size: 361MB\n", "Dimensions: (y: 117, x: 88, time: 8760)\n", "Coordinates:\n", - " * y (y) float64 2.832e+06 2.838e+06 ... 3.408e+06 3.412e+06\n", - " * x (x) float64 4.092e+06 4.098e+06 ... 4.522e+06 4.528e+06\n", - " spatial_ref int32 ...\n", - " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T23:00:00\n", + " * y (y) float64 936B 2.832e+06 2.838e+06 ... 3.408e+06 3.412e+06\n", + " * x (x) float64 704B 4.092e+06 4.098e+06 ... 4.522e+06 4.528e+06\n", + " spatial_ref int32 4B ...\n", + " * time (time) datetime64[ns] 70kB 2015-01-01 ... 2015-12-31T23:00:00\n", "Data variables:\n", - " capacity (y, x) float64 ...\n", - " capfac (time, y, x) float32 ...\n", + " capacity (y, x) float64 82kB ...\n", + " capfac (time, y, x) float32 361MB ...\n", "Attributes:\n", " xy_reference_system: EPSG:3035\n", " grid_mapping: spatial_ref" @@ -571,8 +558,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "elapsed time for aggregate_RE_technology: 1.08 minutes\n", - "elapsed time for aggregate_RE_technology: 1.06 minutes\n" + "elapsed time for aggregate_RE_technology: 1.17 minutes\n", + "elapsed time for aggregate_RE_technology: 1.15 minutes\n" ] } ], @@ -974,15 +961,15 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset>\n",
+       "
    <xarray.Dataset> Size: 1MB\n",
        "Dimensions:     (region_ids: 4, TS_ids: 5, time: 8760)\n",
        "Coordinates:\n",
-       "  * region_ids  (region_ids) object 'cluster_0_cluster_2' ... 'cluster_7'\n",
-       "  * TS_ids      (TS_ids) <U4 'TS_0' 'TS_1' 'TS_2' 'TS_3' 'TS_4'\n",
-       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T23:00:00\n",
+       "  * region_ids  (region_ids) object 32B 'cluster_0_cluster_2' ... 'cluster_7'\n",
+       "  * TS_ids      (TS_ids) <U4 80B 'TS_0' 'TS_1' 'TS_2' 'TS_3' 'TS_4'\n",
+       "  * time        (time) datetime64[ns] 70kB 2015-01-01 ... 2015-12-31T23:00:00\n",
        "Data variables:\n",
-       "    capacity    (region_ids, TS_ids) float64 6.236e+04 2.296e+04 ... 2.296e+04\n",
-       "    capfac      (time, region_ids, TS_ids) float64 0.384 0.7 0.5018 ... 0.2 0.7\n",
+       "    capacity    (region_ids, TS_ids) float64 160B 6.236e+04 ... 2.296e+04\n",
+       "    capfac      (time, region_ids, TS_ids) float64 1MB 0.384 0.7 ... 0.2 0.7\n",
        "Attributes: (12/20)\n",
        "    cluster_0_cluster_2.TS_0:                      [(4132500.0, 3092500.0), (...\n",
        "    cluster_0_cluster_2.TS_1:                      [(4147500.0, 2937500.0), (...\n",
@@ -996,18 +983,18 @@
        "    cluster_7.TS_1:                                [(4102500.0, 2927500.0), (...\n",
        "    cluster_7.TS_2:                                [(4127500.0, 2927500.0)]\n",
        "    cluster_7.TS_3:                                [(4107500.0, 3002500.0)]\n",
-       "    cluster_7.TS_4:                                [(4107500.0, 2907500.0), (...
  • cluster_0_cluster_2.TS_0 :
    [(4132500.0, 3092500.0), (4172500.0, 3137500.0), (4232500.0, 2972500.0)]
    cluster_0_cluster_2.TS_1 :
    [(4147500.0, 2937500.0), (4287500.0, 2852500.0)]
    cluster_0_cluster_2.TS_2 :
    [(4092500.0, 3037500.0), (4092500.0, 3042500.0), (4092500.0, 3047500.0), (4092500.0, 3052500.0), (4092500.0, 3057500.0), (4092500.0, 3062500.0), (4092500.0, 3067500.0), (4092500.0, 3072500.0), (4092500.0, 3077500.0), (4092500.0, 3082500.0), (4092500.0, 3087500.0), (4092500.0, 3092500.0), (4092500.0, 3097500.0), (4092500.0, 3102500.0), (4092500.0, 3107500.0), (4092500.0, 3112500.0), (4092500.0, 3117500.0), (4092500.0, 3122500.0), (4092500.0, 3127500.0), (4092500.0, 3132500.0), (4092500.0, 3137500.0), (4092500.0, 3142500.0), (4092500.0, 3147500.0), (4092500.0, 3152500.0), (4092500.0, 3157500.0), (4092500.0, 3162500.0), (4092500.0, 3167500.0), (4092500.0, 3172500.0), (4092500.0, 3177500.0), (4092500.0, 3182500.0), (4092500.0, 3187500.0), (4092500.0, 3192500.0), (4092500.0, 3197500.0), (4092500.0, 3217500.0), (4097500.0, 3027500.0), (4097500.0, 3032500.0), (4097500.0, 3037500.0), (4097500.0, 3042500.0), (4097500.0, 3047500.0), (4097500.0, 3052500.0), (4097500.0, 3057500.0), (4097500.0, 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    cluster_0_cluster_2.TS_3 :
    [(4217500.0, 3077500.0)]
    cluster_0_cluster_2.TS_4 :
    [(4097500.0, 3087500.0), (4172500.0, 3032500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_0 :
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    cluster_1_cluster_3_cluster_5_cluster_6.TS_1 :
    [(4312500.0, 3317500.0), (4352500.0, 3342500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_2 :
    [(4182500.0, 3222500.0), (4387500.0, 3012500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_3 :
    [(4347500.0, 3092500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_4 :
    [(4277500.0, 3177500.0), (4417500.0, 3402500.0)]
    cluster_4.TS_0 :
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(4492500.0, 2942500.0), (4492500.0, 2947500.0), (4492500.0, 2952500.0), (4492500.0, 2957500.0), (4492500.0, 2962500.0), (4492500.0, 2967500.0), (4492500.0, 2972500.0), (4492500.0, 2977500.0), (4492500.0, 2982500.0), (4492500.0, 2987500.0), (4497500.0, 2832500.0), (4497500.0, 2837500.0), (4497500.0, 2842500.0), (4497500.0, 2847500.0), (4497500.0, 2852500.0), (4497500.0, 2857500.0), (4497500.0, 2862500.0), (4497500.0, 2867500.0), (4497500.0, 2872500.0), (4497500.0, 2877500.0), (4497500.0, 2882500.0), (4497500.0, 2887500.0), (4497500.0, 2892500.0), (4497500.0, 2897500.0), (4497500.0, 2902500.0), (4497500.0, 2907500.0), (4497500.0, 2912500.0), (4497500.0, 2917500.0), (4497500.0, 2922500.0), (4497500.0, 2927500.0), (4497500.0, 2932500.0), (4497500.0, 2937500.0), (4497500.0, 2942500.0), (4497500.0, 2947500.0), (4497500.0, 2952500.0), (4497500.0, 2972500.0), (4497500.0, 2977500.0), (4502500.0, 2832500.0), (4502500.0, 2837500.0), (4502500.0, 2842500.0), (4502500.0, 2847500.0), (4502500.0, 2852500.0), (4502500.0, 2857500.0), (4502500.0, 2862500.0), (4502500.0, 2867500.0), (4502500.0, 2872500.0), (4502500.0, 2877500.0), (4502500.0, 2882500.0), (4502500.0, 2887500.0), (4502500.0, 2892500.0), (4502500.0, 2897500.0), (4502500.0, 2902500.0), (4502500.0, 2907500.0), (4502500.0, 2912500.0), (4502500.0, 2917500.0), (4502500.0, 2922500.0), (4502500.0, 2927500.0), (4502500.0, 2932500.0), (4502500.0, 2937500.0), (4502500.0, 2942500.0), (4507500.0, 2832500.0), (4507500.0, 2837500.0), (4507500.0, 2842500.0), (4507500.0, 2847500.0), (4507500.0, 2852500.0), (4507500.0, 2857500.0), (4507500.0, 2862500.0), (4507500.0, 2867500.0), (4507500.0, 2872500.0), (4507500.0, 2877500.0), (4507500.0, 2882500.0), (4507500.0, 2887500.0), (4507500.0, 2892500.0), (4507500.0, 2897500.0), (4507500.0, 2902500.0), (4507500.0, 2907500.0), (4507500.0, 2912500.0), (4507500.0, 2917500.0), (4507500.0, 2922500.0), (4507500.0, 2927500.0), (4507500.0, 2932500.0), (4512500.0, 2832500.0), (4512500.0, 2837500.0), (4512500.0, 2842500.0), (4512500.0, 2847500.0), (4512500.0, 2852500.0), (4512500.0, 2857500.0), (4512500.0, 2862500.0), (4512500.0, 2867500.0), (4512500.0, 2872500.0), (4512500.0, 2877500.0), (4512500.0, 2882500.0), (4512500.0, 2887500.0), (4512500.0, 2892500.0), (4512500.0, 2897500.0), (4512500.0, 2902500.0), (4512500.0, 2907500.0), (4512500.0, 2912500.0), (4512500.0, 2917500.0), (4512500.0, 2922500.0), (4517500.0, 2832500.0), (4517500.0, 2837500.0), (4517500.0, 2842500.0), (4517500.0, 2847500.0), (4517500.0, 2852500.0), (4517500.0, 2857500.0), (4517500.0, 2862500.0), (4517500.0, 2867500.0), (4517500.0, 2872500.0), (4517500.0, 2877500.0), (4517500.0, 2882500.0), (4517500.0, 2887500.0), (4517500.0, 2892500.0), (4517500.0, 2897500.0), (4517500.0, 2902500.0), (4517500.0, 2907500.0), (4517500.0, 2912500.0), (4517500.0, 2917500.0), (4522500.0, 2832500.0), (4522500.0, 2837500.0), (4522500.0, 2842500.0), (4522500.0, 2847500.0), (4522500.0, 2852500.0), (4522500.0, 2857500.0), (4522500.0, 2862500.0), (4522500.0, 2867500.0), (4522500.0, 2872500.0), (4522500.0, 2877500.0), (4522500.0, 2882500.0), (4522500.0, 2887500.0), (4522500.0, 2892500.0), (4522500.0, 2897500.0), (4522500.0, 2902500.0), (4522500.0, 2907500.0), (4522500.0, 2912500.0), (4527500.0, 2832500.0), (4527500.0, 2837500.0), (4527500.0, 2842500.0), (4527500.0, 2847500.0), (4527500.0, 2852500.0), (4527500.0, 2862500.0), (4527500.0, 2867500.0), (4527500.0, 2872500.0), (4527500.0, 2877500.0), (4527500.0, 2882500.0), (4527500.0, 2887500.0), (4527500.0, 2892500.0), (4527500.0, 2897500.0), (4527500.0, 2902500.0), (4527500.0, 2907500.0), (4527500.0, 2912500.0)]
    cluster_4.TS_1 :
    [(4442500.0, 2912500.0), (4527500.0, 2857500.0)]
    cluster_4.TS_2 :
    [(4332500.0, 2912500.0), (4467500.0, 2912500.0)]
    cluster_4.TS_3 :
    [(4357500.0, 2877500.0)]
    cluster_4.TS_4 :
    [(4357500.0, 2942500.0)]
    cluster_7.TS_0 :
    [(4092500.0, 2902500.0), (4092500.0, 2907500.0), (4092500.0, 2912500.0), (4092500.0, 2917500.0), (4092500.0, 2922500.0), (4092500.0, 2927500.0), (4092500.0, 2932500.0), (4092500.0, 2937500.0), (4092500.0, 2942500.0), (4092500.0, 2947500.0), (4092500.0, 2952500.0), (4092500.0, 2957500.0), (4092500.0, 2962500.0), (4092500.0, 2967500.0), (4092500.0, 2972500.0), (4092500.0, 2977500.0), (4092500.0, 2982500.0), (4092500.0, 2987500.0), (4092500.0, 2992500.0), (4092500.0, 2997500.0), (4092500.0, 3002500.0), (4092500.0, 3007500.0), (4092500.0, 3012500.0), (4092500.0, 3017500.0), (4092500.0, 3022500.0), (4092500.0, 3027500.0), (4092500.0, 3032500.0), (4097500.0, 2902500.0), (4097500.0, 2907500.0), (4097500.0, 2912500.0), (4097500.0, 2917500.0), (4097500.0, 2922500.0), (4097500.0, 2927500.0), (4097500.0, 2932500.0), (4097500.0, 2937500.0), (4097500.0, 2942500.0), (4097500.0, 2947500.0), (4097500.0, 2952500.0), (4097500.0, 2957500.0), (4097500.0, 2962500.0), (4097500.0, 2967500.0), (4097500.0, 2972500.0), (4097500.0, 2977500.0), (4097500.0, 2982500.0), (4097500.0, 2987500.0), (4097500.0, 2992500.0), (4097500.0, 2997500.0), (4097500.0, 3002500.0), (4097500.0, 3007500.0), (4097500.0, 3012500.0), (4097500.0, 3017500.0), (4097500.0, 3022500.0), (4102500.0, 2897500.0), (4102500.0, 2902500.0), (4102500.0, 2907500.0), (4102500.0, 2912500.0), (4102500.0, 2917500.0), (4102500.0, 2922500.0), (4102500.0, 2932500.0), (4102500.0, 2937500.0), (4102500.0, 2942500.0), (4102500.0, 2947500.0), (4102500.0, 2952500.0), (4102500.0, 2957500.0), (4102500.0, 2962500.0), (4102500.0, 2967500.0), (4102500.0, 2977500.0), (4102500.0, 2982500.0), (4102500.0, 2987500.0), (4102500.0, 2992500.0), (4102500.0, 2997500.0), (4102500.0, 3002500.0), (4102500.0, 3007500.0), (4102500.0, 3012500.0), (4102500.0, 3017500.0), (4107500.0, 2897500.0), (4107500.0, 2902500.0), (4107500.0, 2917500.0), (4107500.0, 2922500.0), (4107500.0, 2927500.0), (4107500.0, 2932500.0), (4107500.0, 2937500.0), (4107500.0, 2942500.0), (4107500.0, 2947500.0), (4107500.0, 2952500.0), (4107500.0, 2957500.0), (4107500.0, 2962500.0), (4107500.0, 2967500.0), (4107500.0, 2972500.0), (4107500.0, 2977500.0), (4107500.0, 2982500.0), (4107500.0, 2987500.0), (4107500.0, 2992500.0), (4107500.0, 2997500.0), (4107500.0, 3007500.0), (4107500.0, 3012500.0), (4112500.0, 2892500.0), (4112500.0, 2897500.0), (4112500.0, 2902500.0), (4112500.0, 2907500.0), (4112500.0, 2912500.0), (4112500.0, 2917500.0), (4112500.0, 2922500.0), (4112500.0, 2927500.0), (4112500.0, 2932500.0), (4112500.0, 2937500.0), (4112500.0, 2942500.0), (4112500.0, 2947500.0), (4112500.0, 2952500.0), (4112500.0, 2957500.0), (4112500.0, 2962500.0), (4112500.0, 2967500.0), (4112500.0, 2972500.0), (4112500.0, 2977500.0), (4112500.0, 2982500.0), (4112500.0, 2987500.0), (4112500.0, 2992500.0), (4112500.0, 2997500.0), (4112500.0, 3002500.0), (4112500.0, 3007500.0), (4112500.0, 3012500.0), (4112500.0, 3017500.0), (4117500.0, 2892500.0), (4117500.0, 2897500.0), (4117500.0, 2902500.0), (4117500.0, 2907500.0), (4117500.0, 2912500.0), (4117500.0, 2917500.0), (4117500.0, 2922500.0), (4117500.0, 2927500.0), (4117500.0, 2932500.0), (4117500.0, 2937500.0), (4117500.0, 2942500.0), (4117500.0, 2947500.0), (4117500.0, 2952500.0), (4117500.0, 2957500.0), (4117500.0, 2962500.0), (4117500.0, 2967500.0), (4117500.0, 2972500.0), (4117500.0, 2977500.0), (4117500.0, 2982500.0), (4117500.0, 2987500.0), (4117500.0, 2992500.0), (4117500.0, 2997500.0), (4117500.0, 3002500.0), (4117500.0, 3007500.0), (4122500.0, 2897500.0), (4122500.0, 2902500.0), (4122500.0, 2907500.0), (4122500.0, 2912500.0), (4122500.0, 2917500.0), (4122500.0, 2922500.0), (4122500.0, 2927500.0), (4122500.0, 2932500.0), (4122500.0, 2937500.0), (4122500.0, 2942500.0), (4122500.0, 2947500.0), (4122500.0, 2952500.0), (4122500.0, 2957500.0), (4122500.0, 2962500.0), (4122500.0, 2967500.0), (4122500.0, 2972500.0), (4122500.0, 2977500.0), (4122500.0, 2982500.0), (4122500.0, 2987500.0), (4122500.0, 2992500.0), (4122500.0, 2997500.0), (4127500.0, 2897500.0), (4127500.0, 2902500.0), (4127500.0, 2907500.0), (4127500.0, 2912500.0), (4127500.0, 2917500.0), (4127500.0, 2922500.0), (4127500.0, 2932500.0), (4127500.0, 2937500.0), (4127500.0, 2942500.0), (4127500.0, 2947500.0), (4127500.0, 2952500.0), (4127500.0, 2957500.0), (4127500.0, 2962500.0), (4127500.0, 2972500.0), (4127500.0, 2977500.0), (4127500.0, 2982500.0), (4127500.0, 2987500.0), (4132500.0, 2897500.0), (4132500.0, 2902500.0), (4132500.0, 2907500.0), (4132500.0, 2912500.0), (4132500.0, 2917500.0), (4132500.0, 2922500.0), (4132500.0, 2927500.0), (4132500.0, 2932500.0), (4132500.0, 2937500.0), (4132500.0, 2942500.0), (4132500.0, 2947500.0), (4132500.0, 2952500.0), (4137500.0, 2892500.0), (4137500.0, 2897500.0), (4137500.0, 2902500.0), (4137500.0, 2907500.0)]
    cluster_7.TS_1 :
    [(4102500.0, 2927500.0), (4102500.0, 2972500.0), (4107500.0, 3017500.0)]
    cluster_7.TS_2 :
    [(4127500.0, 2927500.0)]
    cluster_7.TS_3 :
    [(4107500.0, 3002500.0)]
    cluster_7.TS_4 :
    [(4107500.0, 2907500.0), (4107500.0, 2912500.0)]
    • " ], "text/plain": [ - "\n", + " Size: 1MB\n", "Dimensions: (region_ids: 4, TS_ids: 5, time: 8760)\n", "Coordinates:\n", - " * region_ids (region_ids) object 'cluster_0_cluster_2' ... 'cluster_7'\n", - " * TS_ids (TS_ids) 
    <xarray.Dataset>\n",
+       "
    <xarray.Dataset> Size: 1MB\n",
        "Dimensions:     (region_ids: 4, TS_ids: 5, time: 8760)\n",
        "Coordinates:\n",
-       "  * region_ids  (region_ids) object 'cluster_0_cluster_2' ... 'cluster_7'\n",
-       "  * TS_ids      (TS_ids) <U4 'TS_0' 'TS_1' 'TS_2' 'TS_3' 'TS_4'\n",
-       "  * time        (time) datetime64[ns] 2015-01-01 ... 2015-12-31T23:00:00\n",
+       "  * region_ids  (region_ids) object 32B 'cluster_0_cluster_2' ... 'cluster_7'\n",
+       "  * TS_ids      (TS_ids) <U4 80B 'TS_0' 'TS_1' 'TS_2' 'TS_3' 'TS_4'\n",
+       "  * time        (time) datetime64[ns] 70kB 2015-01-01 ... 2015-12-31T23:00:00\n",
        "Data variables:\n",
-       "    capacity    (region_ids, TS_ids) float64 4.03e+07 3.592e+04 ... 1.746e+04\n",
-       "    capfac      (time, region_ids, TS_ids) float64 0.1992 0.1361 0.1 ... 0.2 0.3\n",
+       "    capacity    (region_ids, TS_ids) float64 160B 4.03e+07 ... 1.746e+04\n",
+       "    capfac      (time, region_ids, TS_ids) float64 1MB 0.1992 0.1361 ... 0.2 0.3\n",
        "Attributes: (12/20)\n",
        "    cluster_0_cluster_2.TS_0:                      [(4092500.0, 3037500.0), (...\n",
        "    cluster_0_cluster_2.TS_1:                      [(4167500.0, 2962500.0), (...\n",
@@ -1484,18 +1471,18 @@
        "    cluster_7.TS_1:                                [(4092500.0, 2902500.0), (...\n",
        "    cluster_7.TS_2:                                [(4092500.0, 2927500.0)]\n",
        "    cluster_7.TS_3:                                [(4097500.0, 2967500.0)]\n",
-       "    cluster_7.TS_4:                                [(4107500.0, 2937500.0), (...
  • cluster_0_cluster_2.TS_0 :
    [(4092500.0, 3037500.0), (4092500.0, 3042500.0), (4092500.0, 3047500.0), (4092500.0, 3052500.0), (4092500.0, 3057500.0), (4092500.0, 3062500.0), (4092500.0, 3067500.0), (4092500.0, 3072500.0), (4092500.0, 3077500.0), (4092500.0, 3082500.0), (4092500.0, 3087500.0), (4092500.0, 3092500.0), (4092500.0, 3097500.0), (4092500.0, 3102500.0), (4092500.0, 3107500.0), (4092500.0, 3112500.0), (4092500.0, 3117500.0), (4092500.0, 3122500.0), (4092500.0, 3127500.0), (4092500.0, 3132500.0), (4092500.0, 3137500.0), (4092500.0, 3142500.0), (4092500.0, 3147500.0), (4092500.0, 3152500.0), (4092500.0, 3157500.0), (4092500.0, 3162500.0), (4092500.0, 3167500.0), (4092500.0, 3172500.0), (4092500.0, 3177500.0), (4092500.0, 3182500.0), (4092500.0, 3187500.0), (4092500.0, 3192500.0), (4092500.0, 3197500.0), (4092500.0, 3217500.0), (4097500.0, 3027500.0), (4097500.0, 3032500.0), (4097500.0, 3037500.0), (4097500.0, 3042500.0), (4097500.0, 3047500.0), (4097500.0, 3052500.0), (4097500.0, 3057500.0), (4097500.0, 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    cluster_0_cluster_2.TS_1 :
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    cluster_0_cluster_2.TS_2 :
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    cluster_0_cluster_2.TS_3 :
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    cluster_0_cluster_2.TS_4 :
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    cluster_1_cluster_3_cluster_5_cluster_6.TS_0 :
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    cluster_1_cluster_3_cluster_5_cluster_6.TS_1 :
    [(4227500.0, 3382500.0), (4262500.0, 3117500.0), (4397500.0, 3302500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_2 :
    [(4272500.0, 3402500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_3 :
    [(4362500.0, 3397500.0)]
    cluster_1_cluster_3_cluster_5_cluster_6.TS_4 :
    [(4467500.0, 3147500.0), (4497500.0, 3307500.0)]
    cluster_4.TS_0 :
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(4512500.0, 2862500.0), (4512500.0, 2867500.0), (4512500.0, 2872500.0), (4512500.0, 2877500.0), (4512500.0, 2882500.0), (4512500.0, 2887500.0), (4512500.0, 2892500.0), (4512500.0, 2897500.0), (4512500.0, 2902500.0), (4512500.0, 2907500.0), (4512500.0, 2912500.0), (4512500.0, 2917500.0), (4512500.0, 2922500.0), (4517500.0, 2832500.0), (4517500.0, 2837500.0), (4517500.0, 2842500.0), (4517500.0, 2847500.0), (4517500.0, 2852500.0), (4517500.0, 2857500.0), (4517500.0, 2862500.0), (4517500.0, 2867500.0), (4517500.0, 2872500.0), (4517500.0, 2877500.0), (4517500.0, 2882500.0), (4517500.0, 2887500.0), (4517500.0, 2892500.0), (4517500.0, 2897500.0), (4517500.0, 2902500.0), (4517500.0, 2907500.0), (4517500.0, 2912500.0), (4517500.0, 2917500.0), (4522500.0, 2832500.0), (4522500.0, 2837500.0), (4522500.0, 2842500.0), (4522500.0, 2847500.0), (4522500.0, 2852500.0), (4522500.0, 2857500.0), (4522500.0, 2862500.0), (4522500.0, 2867500.0), (4522500.0, 2872500.0), (4522500.0, 2882500.0), (4522500.0, 2887500.0), (4522500.0, 2892500.0), (4522500.0, 2897500.0), (4522500.0, 2902500.0), (4522500.0, 2907500.0), (4522500.0, 2912500.0), (4527500.0, 2832500.0), (4527500.0, 2837500.0), (4527500.0, 2842500.0), (4527500.0, 2847500.0), (4527500.0, 2852500.0), (4527500.0, 2857500.0), (4527500.0, 2862500.0), (4527500.0, 2867500.0), (4527500.0, 2872500.0), (4527500.0, 2877500.0), (4527500.0, 2882500.0), (4527500.0, 2887500.0), (4527500.0, 2892500.0), (4527500.0, 2897500.0), (4527500.0, 2902500.0), (4527500.0, 2907500.0), (4527500.0, 2912500.0)]
    cluster_4.TS_1 :
    [(4417500.0, 2917500.0), (4442500.0, 2947500.0), (4467500.0, 2832500.0), (4477500.0, 2917500.0)]
    cluster_4.TS_2 :
    [(4522500.0, 2877500.0)]
    cluster_4.TS_3 :
    [(4317500.0, 2842500.0)]
    cluster_4.TS_4 :
    [(4362500.0, 2932500.0), (4447500.0, 2852500.0), (4447500.0, 2962500.0), (4457500.0, 2847500.0)]
    cluster_7.TS_0 :
    [(4097500.0, 3012500.0), (4102500.0, 3017500.0)]
    cluster_7.TS_1 :
    [(4092500.0, 2902500.0), (4092500.0, 2907500.0), (4092500.0, 2912500.0), (4092500.0, 2917500.0), (4092500.0, 2922500.0), (4092500.0, 2932500.0), (4092500.0, 2937500.0), (4092500.0, 2942500.0), (4092500.0, 2947500.0), (4092500.0, 2952500.0), (4092500.0, 2957500.0), (4092500.0, 2962500.0), (4092500.0, 2967500.0), (4092500.0, 2972500.0), (4092500.0, 2977500.0), (4092500.0, 2982500.0), (4092500.0, 2987500.0), (4092500.0, 2992500.0), (4092500.0, 2997500.0), (4092500.0, 3002500.0), (4092500.0, 3007500.0), (4092500.0, 3012500.0), (4092500.0, 3017500.0), (4092500.0, 3022500.0), (4092500.0, 3027500.0), (4092500.0, 3032500.0), (4097500.0, 2902500.0), (4097500.0, 2907500.0), (4097500.0, 2912500.0), (4097500.0, 2917500.0), (4097500.0, 2922500.0), (4097500.0, 2927500.0), (4097500.0, 2932500.0), (4097500.0, 2937500.0), (4097500.0, 2942500.0), (4097500.0, 2947500.0), (4097500.0, 2952500.0), (4097500.0, 2957500.0), (4097500.0, 2962500.0), (4097500.0, 2972500.0), (4097500.0, 2977500.0), (4097500.0, 2982500.0), (4097500.0, 2987500.0), (4097500.0, 2992500.0), (4097500.0, 2997500.0), (4097500.0, 3002500.0), (4097500.0, 3007500.0), (4097500.0, 3017500.0), (4097500.0, 3022500.0), (4102500.0, 2897500.0), (4102500.0, 2902500.0), (4102500.0, 2907500.0), (4102500.0, 2912500.0), (4102500.0, 2917500.0), (4102500.0, 2922500.0), (4102500.0, 2927500.0), (4102500.0, 2932500.0), (4102500.0, 2937500.0), (4102500.0, 2942500.0), (4102500.0, 2947500.0), (4102500.0, 2952500.0), (4102500.0, 2957500.0), (4102500.0, 2962500.0), (4102500.0, 2967500.0), (4102500.0, 2972500.0), (4102500.0, 2977500.0), (4102500.0, 2982500.0), (4102500.0, 2987500.0), (4102500.0, 2992500.0), (4102500.0, 2997500.0), (4102500.0, 3002500.0), (4102500.0, 3007500.0), (4102500.0, 3012500.0), (4107500.0, 2897500.0), (4107500.0, 2902500.0), (4107500.0, 2907500.0), (4107500.0, 2912500.0), (4107500.0, 2917500.0), (4107500.0, 2922500.0), (4107500.0, 2927500.0), (4107500.0, 2932500.0), (4107500.0, 2942500.0), (4107500.0, 2947500.0), (4107500.0, 2952500.0), (4107500.0, 2957500.0), (4107500.0, 2962500.0), (4107500.0, 2967500.0), (4107500.0, 2972500.0), (4107500.0, 2977500.0), (4107500.0, 2982500.0), (4107500.0, 2987500.0), (4107500.0, 2992500.0), (4107500.0, 2997500.0), (4107500.0, 3002500.0), (4107500.0, 3007500.0), (4107500.0, 3012500.0), (4107500.0, 3017500.0), (4112500.0, 2892500.0), (4112500.0, 2897500.0), (4112500.0, 2902500.0), (4112500.0, 2907500.0), (4112500.0, 2912500.0), (4112500.0, 2917500.0), (4112500.0, 2922500.0), (4112500.0, 2927500.0), (4112500.0, 2932500.0), (4112500.0, 2937500.0), (4112500.0, 2942500.0), (4112500.0, 2947500.0), (4112500.0, 2952500.0), (4112500.0, 2957500.0), (4112500.0, 2962500.0), (4112500.0, 2967500.0), (4112500.0, 2972500.0), (4112500.0, 2977500.0), (4112500.0, 2982500.0), (4112500.0, 2987500.0), (4112500.0, 2992500.0), (4112500.0, 2997500.0), (4112500.0, 3002500.0), (4112500.0, 3007500.0), (4112500.0, 3012500.0), (4112500.0, 3017500.0), (4117500.0, 2892500.0), (4117500.0, 2897500.0), (4117500.0, 2902500.0), (4117500.0, 2907500.0), (4117500.0, 2912500.0), (4117500.0, 2917500.0), (4117500.0, 2922500.0), (4117500.0, 2927500.0), (4117500.0, 2932500.0), (4117500.0, 2937500.0), (4117500.0, 2942500.0), (4117500.0, 2947500.0), (4117500.0, 2952500.0), (4117500.0, 2957500.0), (4117500.0, 2962500.0), (4117500.0, 2967500.0), (4117500.0, 2972500.0), (4117500.0, 2977500.0), (4117500.0, 2982500.0), (4117500.0, 2987500.0), (4117500.0, 2992500.0), (4117500.0, 2997500.0), (4117500.0, 3002500.0), (4117500.0, 3007500.0), (4122500.0, 2897500.0), (4122500.0, 2902500.0), (4122500.0, 2907500.0), (4122500.0, 2912500.0), (4122500.0, 2917500.0), (4122500.0, 2922500.0), (4122500.0, 2927500.0), (4122500.0, 2932500.0), (4122500.0, 2937500.0), (4122500.0, 2942500.0), (4122500.0, 2947500.0), (4122500.0, 2952500.0), (4122500.0, 2957500.0), (4122500.0, 2962500.0), (4122500.0, 2967500.0), (4122500.0, 2972500.0), (4122500.0, 2977500.0), (4122500.0, 2982500.0), (4122500.0, 2987500.0), (4122500.0, 2992500.0), (4122500.0, 2997500.0), (4127500.0, 2897500.0), (4127500.0, 2902500.0), (4127500.0, 2907500.0), (4127500.0, 2912500.0), (4127500.0, 2917500.0), (4127500.0, 2922500.0), (4127500.0, 2927500.0), (4127500.0, 2932500.0), (4127500.0, 2937500.0), (4127500.0, 2942500.0), (4127500.0, 2947500.0), (4127500.0, 2952500.0), (4127500.0, 2957500.0), (4127500.0, 2962500.0), (4127500.0, 2972500.0), (4127500.0, 2977500.0), (4127500.0, 2982500.0), (4127500.0, 2987500.0), (4132500.0, 2897500.0), (4132500.0, 2902500.0), (4132500.0, 2907500.0), (4132500.0, 2912500.0), (4132500.0, 2917500.0), (4132500.0, 2922500.0), (4132500.0, 2927500.0), (4132500.0, 2932500.0), (4132500.0, 2937500.0), (4132500.0, 2942500.0), (4132500.0, 2952500.0), (4137500.0, 2892500.0), (4137500.0, 2897500.0), (4137500.0, 2902500.0), (4137500.0, 2907500.0)]
    cluster_7.TS_2 :
    [(4092500.0, 2927500.0)]
    cluster_7.TS_3 :
    [(4097500.0, 2967500.0)]
    cluster_7.TS_4 :
    [(4107500.0, 2937500.0), (4132500.0, 2947500.0)]
    • " ], "text/plain": [ - "\n", + " Size: 1MB\n", "Dimensions: (region_ids: 4, TS_ids: 5, time: 8760)\n", "Coordinates:\n", - " * region_ids (region_ids) object 'cluster_0_cluster_2' ... 'cluster_7'\n", - " * TS_ids (TS_ids) 1\u001b[0m \u001b[39m# First, set up the ESM instance\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m esm \u001b[39m=\u001b[39m xrIO\u001b[39m.\u001b[39;49mreadNetCDFtoEnergySystemModel(\n\u001b[0;32m 3\u001b[0m os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(cwd, \u001b[39m\"\u001b[39;49m\u001b[39moutput_data\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39maggregated_xr_ds.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m 4\u001b[0m )\n", - "File \u001b[1;32mC:\\Programming\\fine\\FINE\\IOManagement\\xarrayIO.py:1049\u001b[0m, in \u001b[0;36mreadNetCDFtoEnergySystemModel\u001b[1;34m(filePath, groupPrefix)\u001b[0m\n\u001b[0;32m 1046\u001b[0m xr_dss \u001b[39m=\u001b[39m readNetCDFToDatasets(filePath, groupPrefix)\n\u001b[0;32m 1048\u001b[0m \u001b[39m# xarray dataset to esm\u001b[39;00m\n\u001b[1;32m-> 1049\u001b[0m esM \u001b[39m=\u001b[39m convertDatasetsToEnergySystemModel(xr_dss)\n\u001b[0;32m 1051\u001b[0m \u001b[39mreturn\u001b[39;00m esM\n", - "File \u001b[1;32mC:\\Programming\\fine\\FINE\\IOManagement\\xarrayIO.py:527\u001b[0m, in \u001b[0;36mconvertDatasetsToEnergySystemModel\u001b[1;34m(datasets)\u001b[0m\n\u001b[0;32m 522\u001b[0m component_dict \u001b[39m=\u001b[39m utilsIO\u001b[39m.\u001b[39madd0dVariableToDict(\n\u001b[0;32m 523\u001b[0m component_dict, comp_var_xr, component, variable\n\u001b[0;32m 524\u001b[0m )\n\u001b[0;32m 526\u001b[0m \u001b[39m# Create esm from esm_dict and component_dict\u001b[39;00m\n\u001b[1;32m--> 527\u001b[0m esM \u001b[39m=\u001b[39m dictIO\u001b[39m.\u001b[39;49mimportFromDict(esm_dict, component_dict)\n\u001b[0;32m 529\u001b[0m \u001b[39m# Read output\u001b[39;00m\n\u001b[0;32m 530\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mResults\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m datasets:\n\u001b[0;32m 531\u001b[0m \u001b[39m# get startyear to find model classes\u001b[39;00m\n", - "File \u001b[1;32mC:\\Programming\\fine\\FINE\\IOManagement\\dictIO.py:107\u001b[0m, in \u001b[0;36mimportFromDict\u001b[1;34m(esmDict, compDict)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mimportFromDict\u001b[39m(esmDict, compDict):\n\u001b[0;32m 96\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 97\u001b[0m \u001b[39m Converts the dictionaries created by the exportToDict function to an EnergySystemModel.\u001b[39;00m\n\u001b[0;32m 98\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[39m :return: esM - EnergySystemModel instance in which the optimized model is held\u001b[39;00m\n\u001b[0;32m 106\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m esM \u001b[39m=\u001b[39m fn\u001b[39m.\u001b[39mEnergySystemModel(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mesmDict)\n\u001b[0;32m 109\u001b[0m \u001b[39m# add components\u001b[39;00m\n\u001b[0;32m 110\u001b[0m \u001b[39mfor\u001b[39;00m classname \u001b[39min\u001b[39;00m compDict:\n\u001b[0;32m 111\u001b[0m \u001b[39m# get class\u001b[39;00m\n", - "\u001b[1;31mTypeError\u001b[0m: EnergySystemModel.__init__() got an unexpected keyword argument 'lowerBound'" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\utils.py:1238: UserWarning: A declaration of bigM is not necessary if hasIsBuiltBinaryVariable is set to false. The value of bigM will be ignored in the optimization.\n", + " warnings.warn(\n" ] } ], @@ -1620,9 +1601,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Wind (onshore)': ,\n", + " 'PV': ,\n", + " 'Electricity demand': ,\n", + " 'Hydrogen demand': }" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# If wind turbine and PV are already present in the ESM instance, we need to replace them like shown in the cells below.\n", "esm.componentModelingDict[\"SourceSinkModel\"].componentsDict" @@ -1630,7 +1625,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1651,20 +1646,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "tags": [ "nbval-ignore-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Electricity demand': ,\n", + " 'Hydrogen demand': }" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "esm.componentModelingDict[\"SourceSinkModel\"].componentsDict" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1718,7 +1725,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1757,13 +1764,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "tags": [ "nbval-ignore-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Electricity demand': ,\n", + " 'Hydrogen demand': ,\n", + " 'Wind (onshore) 0': ,\n", + " 'PV 0': ,\n", + " 'Wind (onshore) 1': ,\n", + " 'PV 1': ,\n", + " 'Wind (onshore) 2': ,\n", + " 'PV 2': ,\n", + " 'Wind (onshore) 3': ,\n", + " 'PV 3': ,\n", + " 'Wind (onshore) 4': ,\n", + " 'PV 4': }" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "esm.componentModelingDict[\"SourceSinkModel\"].componentsDict" ] @@ -1777,13 +1806,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "tags": [ "nbval-ignore-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Clustering time series data with 7 typical periods and 24 time steps per period \n", + "further clustered to 12 segments per period...\n", + "\t\t(8.8650 sec)\n", + "\n" + ] + } + ], "source": [ "esm.aggregateTemporally(numberOfTypicalPeriods=7)" ] @@ -1797,13 +1838,230 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "tags": [ "nbval-ignore-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series aggregation specifications:\n", + "Number of typical periods:7, number of time steps per period:24, number of segments per period:12\n", + "\n", + "Declaring sets, variables and constraints for SourceSinkModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.2534 sec)\n", + "\n", + "Declaring sets, variables and constraints for ConversionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.0271 sec)\n", + "\n", + "Declaring sets, variables and constraints for StorageModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.3176 sec)\n", + "\n", + "Declaring sets, variables and constraints for LOPFModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.0382 sec)\n", + "\n", + "Declaring sets, variables and constraints for TransmissionModel\n", + "\tdeclaring sets... \n", + "\tdeclaring variables... \n", + "\tdeclaring constraints... \n", + "\t\t(0.0381 sec)\n", + "\n", + "Declaring shared potential constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring linked component quantity constraint...\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring commodity balances...\n", + "\t\t(0.0490 sec)\n", + "\n", + "\t\t(0.0000 sec)\n", + "\n", + "Declaring objective function...\n", + "\t\t(0.6909 sec)\n", + "\n", + "Either solver not selected or specified solver not available.gurobi is set as solver.\n", + "Using license file C:\\Users\\t.gross\\gurobi.lic\n", + "Academic license - for non-commercial use only\n", + "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpnp0yd561.pyomo.lp\n", + "Reading time = 0.05 seconds\n", + "x1: 18694 rows, 10850 columns, 55156 nonzeros\n", + "Changed value of parameter QCPDual to 1\n", + " Prev: 0 Min: 0 Max: 1 Default: 0\n", + "Changed value of parameter Threads to 3\n", + " Prev: 0 Min: 0 Max: 1024 Default: 0\n", + "Parameter logfile unchanged\n", + " Value: Default: \n", + "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", + "Optimize a model with 18694 rows, 10850 columns and 55156 nonzeros\n", + "Model fingerprint: 0xe25986fa\n", + "Coefficient statistics:\n", + " Matrix range [3e-05, 5e+02]\n", + " Objective range [6e-04, 1e-01]\n", + " Bounds range [2e-01, 8e+07]\n", + " RHS range [1e+00, 2e+01]\n", + "\n", + "Concurrent LP optimizer: dual simplex and barrier\n", + "Showing barrier log only...\n", + "\n", + "Presolve removed 6348 rows and 3586 columns\n", + "Presolve time: 0.03s\n", + "Presolved: 12346 rows, 7264 columns, 50624 nonzeros\n", + "\n", + "Ordering time: 0.01s\n", + "\n", + "Barrier statistics:\n", + " Dense cols : 136\n", + " Free vars : 112\n", + " AA' NZ : 1.414e+05\n", + " Factor NZ : 6.090e+05 (roughly 13 MBytes of memory)\n", + " Factor Ops : 9.265e+07 (less than 1 second per iteration)\n", + " Threads : 2\n", + "\n", + " Objective Residual\n", + "Iter Primal Dual Primal Dual Compl Time\n", + " 0 5.30633349e+06 -2.58480686e+07 3.81e+07 5.04e-02 8.81e+04 0s\n", + " 1 5.30816342e+06 -2.58109339e+07 3.80e+07 7.41e+01 8.78e+04 0s\n", + " 2 5.34183287e+06 -2.58538659e+07 3.76e+07 8.46e+01 8.71e+04 0s\n", + " 3 5.60275677e+06 -2.61981326e+07 3.46e+07 8.88e+01 8.25e+04 0s\n", + " 4 5.65035263e+06 -2.61033450e+07 3.40e+07 8.95e+01 8.11e+04 0s\n", + " 5 5.65100168e+06 -2.63486941e+07 3.40e+07 8.97e+01 8.10e+04 0s\n", + " 6 6.05754755e+06 -2.15107004e+07 3.10e+07 1.09e+02 7.22e+04 0s\n", + " 7 6.15987213e+06 -2.07799945e+07 2.94e+07 1.59e+02 6.82e+04 0s\n", + " 8 6.22973003e+06 -2.01356309e+07 2.81e+07 1.84e+02 6.52e+04 0s\n", + " 9 6.23668354e+06 -2.00626479e+07 2.79e+07 1.84e+02 6.49e+04 0s\n", + " 10 6.23745567e+06 -2.02067062e+07 2.79e+07 1.87e+02 6.50e+04 0s\n", + " 11 6.85815484e+06 -1.43116521e+07 1.98e+07 1.44e+02 4.74e+04 0s\n", + " 12 6.36414736e+06 -6.79019938e+06 3.50e+06 1.91e+02 1.37e+04 0s\n", + " 13 6.04763609e+06 -5.55259845e+06 2.86e+06 1.79e+02 1.11e+04 0s\n", + " 14 5.99546383e+06 -6.21977812e+06 2.77e+06 2.71e+02 1.03e+04 0s\n", + " 15 5.59393852e+06 -5.79295987e+06 2.11e+06 4.62e+02 8.36e+03 0s\n", + " 16 3.60550858e+06 -2.12306715e+06 1.45e+05 2.79e+02 1.49e+03 0s\n", + " 17 3.02847468e+06 -2.86195843e+06 1.15e+05 2.52e+02 1.19e+03 0s\n", + " 18 2.64070305e+06 -5.91819574e+06 9.86e+04 4.76e+02 9.49e+02 0s\n", + " 19 1.82718222e+06 -5.19960587e+06 6.37e+04 4.01e+02 6.25e+02 0s\n", + " 20 1.77491469e+06 -5.41045977e+06 6.16e+04 4.43e+02 6.01e+02 0s\n", + " 21 1.26489904e+06 -4.42713341e+06 4.04e+04 3.62e+02 4.07e+02 0s\n", + " 22 8.36719151e+05 -3.82774054e+06 2.35e+04 2.87e+02 2.51e+02 0s\n", + " 23 5.01926962e+05 -7.00847548e+05 1.14e+04 2.68e+01 9.29e+01 0s\n", + " 24 3.08970972e+05 -4.97009783e+05 5.59e+03 1.28e+01 4.73e+01 0s\n", + " 25 2.44300585e+05 -4.38594625e+05 3.95e+03 3.04e+01 3.07e+01 0s\n", + " 26 2.17394603e+05 -4.45720763e+05 3.45e+03 3.42e+01 2.60e+01 0s\n", + " 27 1.38305777e+05 -3.87403760e+05 1.78e+03 2.97e+01 1.69e+01 0s\n", + " 28 7.30320623e+04 -2.80773995e+05 7.37e+02 2.25e+01 9.37e+00 0s\n", + " 29 3.02819655e+04 -1.72849430e+05 2.62e+02 1.39e+01 4.48e+00 0s\n", + " 30 1.35850975e+04 -7.59342228e+04 9.43e+01 5.79e+00 1.98e+00 0s\n", + " 31 7.09603224e+03 -1.51135749e+04 4.18e+01 1.12e+00 6.40e-01 0s\n", + " 32 3.42517356e+03 -4.79263319e+03 1.70e+01 3.42e-01 2.52e-01 0s\n", + " 33 9.55232353e+02 -8.10314961e+02 4.56e+00 5.53e-02 6.55e-02 0s\n", + " 34 5.20918602e+02 -3.28547822e+02 2.43e+00 2.24e-02 3.47e-02 1s\n", + " 35 2.41252292e+02 -1.71355333e+02 1.04e+00 1.21e-02 1.64e-02 1s\n", + " 36 9.01891663e+01 -1.50343086e+02 2.89e-01 1.07e-02 7.69e-03 1s\n", + " 37 5.65726013e+01 -9.68689234e+01 1.63e-01 7.13e-03 4.77e-03 1s\n", + " 38 3.81573689e+01 -3.27491565e+01 8.48e-02 2.92e-03 2.36e-03 1s\n", + " 39 3.42534980e+01 -3.22214200e+01 7.19e-02 2.59e-03 2.24e-03 1s\n", + " 40 2.33612228e+01 -9.28093168e+00 2.86e-02 1.28e-03 1.07e-03 1s\n", + " 41 1.73597844e+01 -1.64847761e+00 1.37e-02 7.99e-04 5.96e-04 1s\n", + " 42 1.41779489e+01 5.91306500e+00 6.34e-03 4.16e-04 2.98e-04 1s\n", + " 43 1.34049784e+01 9.16923498e+00 4.58e-03 2.17e-04 1.91e-04 1s\n", + " 44 1.30211062e+01 9.32239589e+00 3.72e-03 2.02e-04 1.65e-04 1s\n", + " 45 1.24919958e+01 1.02941988e+01 2.53e-03 9.53e-05 1.03e-04 1s\n", + " 46 1.22444597e+01 1.06958060e+01 1.97e-03 5.49e-05 7.32e-05 1s\n", + " 47 1.20908701e+01 1.08954170e+01 1.56e-03 3.89e-05 5.66e-05 1s\n", + " 48 1.19361700e+01 1.10086978e+01 1.17e-03 3.24e-05 4.39e-05 1s\n", + " 49 1.18812239e+01 1.10673821e+01 1.04e-03 3.00e-05 3.99e-05 1s\n", + " 50 1.18299397e+01 1.10878821e+01 9.15e-04 2.80e-05 3.63e-05 1s\n", + " 51 1.17232692e+01 1.12163463e+01 6.39e-04 2.28e-05 2.78e-05 1s\n", + " 52 1.16859019e+01 1.17676831e+01 5.58e-04 3.03e-05 2.38e-05 1s\n", + " 53 1.15877903e+01 1.13127266e+01 3.46e-04 1.77e-05 1.53e-05 1s\n", + " 54 1.15421265e+01 1.12217188e+01 2.52e-04 3.19e-05 1.09e-05 1s\n", + " 55 1.15110103e+01 1.13746648e+01 1.90e-04 9.69e-06 7.81e-06 1s\n", + " 56 1.14866327e+01 1.14014951e+01 1.39e-04 9.73e-06 5.56e-06 1s\n", + " 57 1.14762039e+01 1.14458117e+01 1.16e-04 6.84e-06 4.44e-06 1s\n", + " 58 1.14667174e+01 1.13730360e+01 9.52e-05 5.81e-06 3.55e-06 1s\n", + " 59 1.14622200e+01 1.15275207e+01 8.59e-05 6.85e-06 2.90e-06 1s\n", + " 60 1.14535540e+01 1.14270299e+01 6.46e-05 4.36e-06 2.02e-06 1s\n", + " 61 1.14370704e+01 1.14104506e+01 2.58e-05 3.66e-06 1.02e-06 1s\n", + " 62 1.14351485e+01 1.13622698e+01 2.20e-05 2.68e-06 8.08e-07 1s\n", + " 63 1.14308821e+01 1.13524902e+01 1.25e-05 2.93e-06 3.88e-07 1s\n", + " 64 1.14282609e+01 1.14130612e+01 6.47e-06 6.61e-07 1.78e-07 1s\n", + " 65 1.14262966e+01 1.14236843e+01 2.04e-06 9.00e-08 5.47e-08 1s\n", + " 66 1.14255403e+01 1.14257676e+01 3.68e-07 5.31e-08 1.03e-08 1s\n", + " 67 1.14254359e+01 1.14252057e+01 1.44e-07 5.26e-08 3.97e-09 1s\n", + " 68 1.14253688e+01 1.14250449e+01 6.64e-09 9.07e-08 2.52e-10 1s\n", + " 69 1.14253653e+01 1.14253489e+01 1.83e-10 3.27e-09 1.22e-11 1s\n", + " 70 1.14253651e+01 1.14253590e+01 2.05e-10 3.96e-09 1.53e-12 1s\n", + "\n", + "Barrier solved model in 70 iterations and 0.85 seconds\n", + "Optimal objective 1.14253651e+01\n", + "\n", + "Crossover log...\n", + "\n", + " 0 DPushes remaining with DInf 0.0000000e+00 1s\n", + "\n", + " 1574 PPushes remaining with PInf 1.6016160e-06 1s\n", + " 0 PPushes remaining with PInf 0.0000000e+00 1s\n", + "\n", + " Push phase complete: Pinf 0.0000000e+00, Dinf 7.7146236e-16 1s\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 3724 1.1425365e+01 0.000000e+00 0.000000e+00 1s\n", + "\n", + "Solved with barrier\n", + "Solved in 3724 iterations and 1.01 seconds\n", + "Optimal objective 1.142536510e+01\n", + "\n", + "Status: ok\n", + "Return code: 0\n", + "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Termination condition: optimal\n", + "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", + "Wall time: 1.0118732452392578\n", + "Error rc: 0\n", + "Time: 1.5696656703948975\n", + "\n", + "\n", + "Name: x1_copy\n", + "Lower bound: 11.425365101598155\n", + "Upper bound: 11.425365101598155\n", + "Number of objectives: 1\n", + "Number of constraints: 18694\n", + "Number of variables: 10850\n", + "Number of binary variables: 0\n", + "Number of integer variables: 0\n", + "Number of continuous variables: 10850\n", + "Number of nonzeros: 55156\n", + "Sense: minimize\n", + "\n", + "Solve time: 2.154900550842285 sec.\n", + "\n", + "Processing optimization output...\n", + "for SourceSinkModel ... (0.8675sec)\n", + "for ConversionModel ... (0.1979sec)\n", + "for StorageModel ... (0.7131sec)\n", + "for LOPFModel ... (0.4298sec)\n", + "for TransmissionModel ... (0.3495sec)\n", + "\t\t(2.5931 sec)\n", + "\n" + ] + } + ], "source": [ "esm.optimize(timeSeriesAggregation=True)\n", "# The following `optimizationSpecs` are recommended if you use the Gurobi solver.\n", @@ -1825,7 +2083,7 @@ "formats": "ipynb,py:percent" }, "kernelspec": { - "display_name": "Python 3.6.13 64-bit ('FINE_dev': conda)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1839,7 +2097,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPriceTimeSeries_MrdEuro_GWh.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPriceTimeSeries_MrdEuro_GWh.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPriceTimeSeries_MrdEuro_GWh.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Biogas/biogasPriceTimeSeries_MrdEuro_GWh.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx rename to examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx b/examples/08_Spatial_and_technology_aggregation/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx similarity index 100% rename from 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Optimization.ipynb similarity index 97% rename from examples/StochasticOptimization/Stochastic Optimization.ipynb rename to examples/09_Stochastic_Optimization/09_Stochastic Optimization.ipynb index 2d772331..48d3a461 100644 --- a/examples/StochasticOptimization/Stochastic Optimization.ipynb +++ b/examples/09_Stochastic_Optimization/09_Stochastic Optimization.ipynb @@ -6,7 +6,7 @@ "source": [ "# Workflow for a stoachstic opimization\n", "\n", - "In this example of the FINE framework, a stochastic optimization is performed. It allows a energy system optimization to consider several years of operation input (e.g. different wind power due to different weather conditions) in the optimization of the energy system design. By optimizing a system considering several years of input data for operation, a more robust energy system is achieved." + "In this example of the ETHOS.FINE framework, a stochastic optimization is performed. It allows a energy system optimization to consider several years of operation input (e.g. different wind power due to different weather conditions) in the optimization of the energy system design. By optimizing a system considering several years of input data for operation, a more robust energy system is achieved." ] }, { diff --git a/examples/10_PerfectForesight/10_perfectForesight_example.ipynb b/examples/10_PerfectForesight/10_perfectForesight_example.ipynb new file mode 100644 index 00000000..3f5e0215 --- /dev/null +++ b/examples/10_PerfectForesight/10_perfectForesight_example.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":[" # Workflow for a transformation pathway of a single node energy system with perfect foresight\n","\n"," In this application of the ETHOS.FINE framework, a transformation pathway of a energy system is modeled and optimized.\n","\n"," All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n","\n"," The workflow is structures as follows:\n"," 1. Required packages are imported and the input data path is set\n"," 2. An energy system model instance is created\n"," 3. Commodity sources are added to the energy system model\n"," 4. Commodity conversion components are added to the energy system model\n"," 5. Commodity storages are added to the energy system model\n"," 7. Commodity sinks are added to the energy system model\n"," 8. The energy system model is optimized\n"," 9. Selected optimization results are presented\n"]},{"cell_type":"markdown","metadata":{},"source":[" # 1. Import required packages and set input data path\n","\n"," The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import fine as fn\n","from getData import getData\n","\n","import os\n","\n","cwd = os.getcwd()\n","data = getData()"]},{"cell_type":"markdown","metadata":{},"source":[" # 2. Create an energy system model instance\n","\n"," The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n","\n"," The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["locations = {\"GermanyRegion\"}\n","commodityUnitDict = {\"electricity\": r\"GW$_{el}$\", \"hydrogen\": r\"GW$_{H_{2},LHV}$\"}\n","commodities = {\"electricity\", \"hydrogen\"}\n","numberOfTimeSteps = 8760\n","hoursPerTimeStep = 1"]},{"cell_type":"markdown","metadata":{},"source":[" # 2.1 define Transformation Pathway parameters\n","\n"," Transformation Pathway Analyses can be run by setting a number of investment periods\n"," larger than 1, which is the default value and results in a single year optimization."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["numberOfInvestmentPeriods=3\n","startYear=2020\n","interval=5"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM = fn.EnergySystemModel(\n"," locations=locations,\n"," commodities=commodities,\n"," numberOfInvestmentPeriods=numberOfInvestmentPeriods,\n"," startYear=startYear, \n"," investmentPeriodInterval=interval,\n"," numberOfTimeSteps=8760,\n"," commodityUnitsDict=commodityUnitDict,\n"," hoursPerTimeStep=1,\n"," costUnit=\"1e9 Euro\",\n"," lengthUnit=\"km\",\n"," verboseLogLevel=0,\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 3. Add commodity sources to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 3.1. Electricity sources"]},{"cell_type":"markdown","metadata":{},"source":[" ### Wind onshore"]},{"cell_type":"markdown","metadata":{},"source":[" change weather conditions for the different investment periods"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["operationRateMax={}\n","operationRateMax[2020]=1.2*data[\"Wind (onshore), operationRateMax\"]\n","operationRateMax[2025]=0.7*data[\"Wind (onshore), operationRateMax\"]\n","operationRateMax[2030]=1*data[\"Wind (onshore), operationRateMax\"]"]},{"cell_type":"markdown","metadata":{},"source":[" define existing stock for wind onshore turbines"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["stockWindCommissioning={\n"," 2010:5,\n"," 2015:10,\n","}"]},{"cell_type":"markdown","metadata":{},"source":[" define invest and opex per capacity for wind onshore turbines"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["investPerCapacityWind={\n"," 2010:1.5, \n"," 2015:1.25, \n"," 2020:1.1, \n"," 2025:1, \n"," 2030:0.95\n"," }\n","\n","opexPerCapacityWind={\n"," 2010:1.5*0.02, \n"," 2015:1.25*0.02, \n"," 2020:1.1*0.02, \n"," 2025:1*0.02, \n"," 2030:0.95*0.02\n"," }"]},{"cell_type":"markdown","metadata":{},"source":[" add wind onshore source to esM"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Source(\n"," esM=esM,\n"," name=\"Wind (onshore)\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=True,\n"," operationRateMax=data[\"Wind (onshore), operationRateMax\"],\n"," capacityMax=data[\"Wind (onshore), capacityMax\"],\n"," investPerCapacity=investPerCapacityWind,\n"," opexPerCapacity=opexPerCapacityWind,\n"," interestRate=0.08,\n"," economicLifetime=20,\n"," stockCommissioning=stockWindCommissioning,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Full load hours:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data[\"Wind (onshore), operationRateMax\"].sum()"]},{"cell_type":"markdown","metadata":{},"source":[" # 4. Add conversion components to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ### New combined cycly gas turbines for hydrogen"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Conversion(\n"," esM=esM,\n"," name=\"New CCGT plants (hydrogen)\",\n"," physicalUnit=r\"GW$_{el}$\",\n"," commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.6},\n"," hasCapacityVariable=True,\n"," investPerCapacity=0.7,\n"," opexPerCapacity={2020:0.021, 2025:0.018, 2030:0.025},\n"," interestRate=0.08,\n"," economicLifetime=30,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ### Electrolyzers"]},{"cell_type":"markdown","metadata":{},"source":[" add component with constant invest and opex per capacity"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Conversion(\n"," esM=esM,\n"," name=\"Electroylzers\",\n"," physicalUnit=r\"GW$_{el}$\",\n"," commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.7},\n"," hasCapacityVariable=True,\n"," investPerCapacity=0.5,\n"," opexPerCapacity=0.5 * 0.025,\n"," interestRate=0.08,\n"," economicLifetime=10,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 5. Add commodity storages to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 5.1. Electricity storage"]},{"cell_type":"markdown","metadata":{},"source":[" ### Lithium ion batteries\n","\n"," The self discharge of a lithium ion battery is here described as 3% per month. The self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Storage(\n"," esM=esM,\n"," name=\"Li-ion batteries\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=True,\n"," chargeEfficiency=0.95,\n"," cyclicLifetime=10000,\n"," dischargeEfficiency=0.95,\n"," selfDischarge=1 - (1 - 0.03) ** (1 / (30 * 24)),\n"," chargeRate=1,\n"," dischargeRate=1,\n"," doPreciseTsaModeling=False,\n"," investPerCapacity=0.151,\n"," opexPerCapacity=0.002,\n"," interestRate=0.08,\n"," economicLifetime=20,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ## 5.2. Hydrogen storage"]},{"cell_type":"markdown","metadata":{},"source":[" ### Hydrogen filled salt caverns\n"," The maximum capacity is here obtained by: dividing the given capacity (which is given for methane) by the lower heating value of methane and then multiplying it with the lower heating value of hydrogen."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Storage(\n"," esM=esM,\n"," name=\"Salt caverns (hydrogen)\",\n"," commodity=\"hydrogen\",\n"," hasCapacityVariable=True,\n"," capacityVariableDomain=\"continuous\",\n"," capacityPerPlantUnit=133,\n"," chargeRate=1 / 470.37,\n"," dischargeRate=1 / 470.37,\n"," sharedPotentialID=\"Existing salt caverns\",\n"," stateOfChargeMin=0.33,\n"," stateOfChargeMax=1,\n"," capacityMax=data[\"Salt caverns (hydrogen), capacityMax\"],\n"," investPerCapacity={2020:0.00011,2025: 0.00009,2030:0.00009},\n"," opexPerCapacity=0.00057,\n"," interestRate=0.08,\n"," economicLifetime=30,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 7. Add commodity sinks to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 7.1. Electricity sinks"]},{"cell_type":"markdown","metadata":{},"source":[" ### Electricity demand"]},{"cell_type":"markdown","metadata":{},"source":[" vary the demand with the years - increasing demand by 30% per year"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["electricityDemand={}\n","electricityDemand[2020]=(1+0*0.3)*data[\"Electricity demand, operationRateFix\"]\n","electricityDemand[2025]=(1+1*0.3)*data[\"Electricity demand, operationRateFix\"]\n","electricityDemand[2030]=(1+2*0.3)*data[\"Electricity demand, operationRateFix\"]\n","\n","esM.add(\n"," fn.Sink(\n"," esM=esM,\n"," name=\"Electricity demand\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=False,\n"," operationRateFix=electricityDemand,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ## 7.2. Hydrogen sinks"]},{"cell_type":"markdown","metadata":{},"source":[" ### Fuel cell electric vehicle (FCEV) demand"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["FCEV_penetration = 0.5\n","\n","# vary the demand with the years - increasing demand by 25% per year\n","hydrogendDemand={}\n","hydrogendDemand[2020]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","hydrogendDemand[2025]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","hydrogendDemand[2030]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","\n","\n","esM.add(\n"," fn.Sink(\n"," esM=esM,\n"," name=\"Hydrogen demand\",\n"," commodity=\"hydrogen\",\n"," hasCapacityVariable=False,\n"," operationRateFix=hydrogendDemand,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 8. Optimize energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.aggregateTemporally(numberOfTypicalPeriods=20)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")"]},{"cell_type":"markdown","metadata":{},"source":[" # 9. Selected results output"]},{"cell_type":"markdown","metadata":{},"source":[" ### Sources and Sink\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of SourceSinkModel for year {year}\")\n"," print(esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2, ip=year))"]},{"cell_type":"markdown","metadata":{},"source":[" Plot operation time series (either one or two dimensional) for different years"]},{"cell_type":"markdown","metadata":{},"source":[" Electricity demand operation for Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperation(esM, \"Electricity demand\", \"GermanyRegion\", ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Electricity demand operation for Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperation(esM, \"Electricity demand\", \"GermanyRegion\", ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Electricity demand in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"GermanyRegion\",ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Electricity demand in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"GermanyRegion\",ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" ### Conversion\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of ConversionMpdel for year {year}\")\n"," esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2, ip=year)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for New CCGT plants (hydrogen) in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (hydrogen)\", \"GermanyRegion\", ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for New CCGT plants (hydrogen) in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (hydrogen)\", \"GermanyRegion\", ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" ### Storage\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of StorageModel for year {year}\")\n"," print(esM.getOptimizationSummary(\"StorageModel\", outputLevel=2, ip=year))"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2020\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2025"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2025\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2030\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2020\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2025"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2025\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2030\n",")"]}],"metadata":{"kernelspec":{"display_name":"FINE_dev","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":2} diff --git a/examples/PerfectForesight/InputData/Locations.png b/examples/10_PerfectForesight/InputData/Locations.png similarity index 100% rename from examples/PerfectForesight/InputData/Locations.png rename to examples/10_PerfectForesight/InputData/Locations.png diff --git a/examples/PerfectForesight/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Biogas/biogasPotential_GWh_biogas.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Demands/electricityDemand_GWh_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Demands/hydrogenDemand_GWh_hydrogen.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableExistingCapacity_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableIncidence.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/ACcableReactance.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableExistingCapacity_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLength_km.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/ElectricGrid/DCcableLosses.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/GeologicalStorage/existingSaltCavernsCapacity_GWh_methane.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityPHS_storage_GWh_energyPHS.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixCapacityROR_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/HydroPower/fixInflowPHS_GWh_energyPHS.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixInflowPHS_GWh_energyPHS.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/HydroPower/fixInflowPHS_GWh_energyPHS.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixInflowPHS_GWh_energyPHS.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/HydroPower/fixOperationRateROR_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixOperationRateROR_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/HydroPower/fixOperationRateROR_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/HydroPower/fixOperationRateROR_GW_el.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/NaturalGasPlants/existingCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/NaturalGasPlants/existingCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/NaturalGasPlants/existingCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/NaturalGasPlants/existingCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/NaturalGasPlants/newCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/NaturalGasPlants/newCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/NaturalGasPlants/newCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/NaturalGasPlants/newCombinedCycleGasTurbinePlantsCapacity_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/PV/maxCapacityPV_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/PV/maxCapacityPV_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/PV/maxCapacityPV_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/PV/maxCapacityPV_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/PV/maxOperationRatePV_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/PV/maxOperationRatePV_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/PV/maxOperationRatePV_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/PV/maxOperationRatePV_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Pipelines/pipelineIncidence.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Pipelines/pipelineIncidence.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Pipelines/pipelineIncidence.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Pipelines/pipelineIncidence.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Pipelines/pipelineLength.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Pipelines/pipelineLength.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Pipelines/pipelineLength.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Pipelines/pipelineLength.xlsx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/AClines.cpg b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.cpg similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/AClines.cpg rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.cpg diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.dbf b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.dbf similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.dbf rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.dbf diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/AClines.prj b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.prj similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/AClines.prj rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.prj diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shp b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shp similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shp rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shp diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shx b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shx rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/AClines.shx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/DClines.cpg b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.cpg similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/DClines.cpg rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.cpg diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.dbf b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.dbf similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.dbf rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.dbf diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/DClines.prj b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.prj similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/DClines.prj rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.prj diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shp b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shp similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shp rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shp diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shx b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shx rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/DClines.shx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/buses.cpg b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.cpg similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/buses.cpg rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.cpg diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.dbf b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.dbf similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.dbf rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.dbf diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/buses.prj b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.prj similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/buses.prj rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.prj diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shp b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shp similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shp rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shp diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shx b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shx rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/buses.shx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/clusteredRegions.cpg b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.cpg similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/clusteredRegions.cpg rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.cpg diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.dbf b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.dbf similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.dbf rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.dbf diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/clusteredRegions.prj b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.prj similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/clusteredRegions.prj rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.prj diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shp b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shp similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shp rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shp diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shx b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shx rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/clusteredRegions.shx diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/transmissionPipeline.cpg b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.cpg similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/transmissionPipeline.cpg rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.cpg diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.dbf b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.dbf similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.dbf rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.dbf diff --git a/examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/transmissionPipeline.prj b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.prj similarity index 100% rename from examples/Spatial_and_technology_aggregation/InputData/SpatialData/ShapeFiles/transmissionPipeline.prj rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.prj diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shp b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shp similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shp rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shp diff --git a/examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shx b/examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shx rename to examples/10_PerfectForesight/InputData/SpatialData/ShapeFiles/transmissionPipeline.shx diff --git a/examples/PerfectForesight/InputData/SpatialData/Wind/maxCapacityOffshore_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Wind/maxCapacityOffshore_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Wind/maxCapacityOffshore_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Wind/maxCapacityOffshore_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Wind/maxCapacityOnshore_GW_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Wind/maxCapacityOnshore_GW_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Wind/maxCapacityOnshore_GW_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Wind/maxCapacityOnshore_GW_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOffshore_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOffshore_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOffshore_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOffshore_el.xlsx diff --git a/examples/PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx b/examples/10_PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx similarity index 100% rename from examples/PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx rename to examples/10_PerfectForesight/InputData/SpatialData/Wind/maxOperationRateOnshore_el.xlsx diff --git a/examples/PerfectForesight/getData.py b/examples/10_PerfectForesight/getData.py similarity index 100% rename from examples/PerfectForesight/getData.py rename to examples/10_PerfectForesight/getData.py diff --git a/examples/Partload/Partload_Example.ipynb b/examples/11_Partload/11_Partload_Example.ipynb similarity index 52% rename from examples/Partload/Partload_Example.ipynb rename to examples/11_Partload/11_Partload_Example.ipynb index 30a25a9e..45ceeb34 100644 --- a/examples/Partload/Partload_Example.ipynb +++ b/examples/11_Partload/11_Partload_Example.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Workflow for a 1node energy system\n", + "# Example for modeling part load behavior\n", "\n", - "In this application of the FINE framework, a hydrogen system is modeled and optimized considering partload behaviour of the electrolyzer. \n", + "In this application of the ETHOS.FINE framework, a hydrogen system is modeled and optimized considering partload behavior of the electrolyzer. \n", "\n", "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", "\n", @@ -27,7 +27,7 @@ "source": [ "# 1. Import required packages and set input data path\n", "\n", - "The FINE framework is imported which provides the required classes and functions for modeling the energy system." + "The ETHOS.FINE framework is imported which provides the required classes and functions for modeling the energy system." ] }, { @@ -679,45 +679,45 @@ "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0257 sec)\n", + "\t\t(0.0363 sec)\n", "\n", "Declaring sets, variables and constraints for ConversionPartLoadModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0298 sec)\n", + "\t\t(0.0263 sec)\n", "\n", "Declaring sets, variables and constraints for StorageModel\n", "\tdeclaring sets... \n", "\tdeclaring variables... \n", "\tdeclaring constraints... \n", - "\t\t(0.0217 sec)\n", + "\t\t(0.0249 sec)\n", "\n", "Declaring shared potential constraint...\n", - "\t\t(0.0002 sec)\n", + "\t\t(0.0000 sec)\n", "\n", "Declaring linked component quantity constraint...\n", "\t\t(0.0000 sec)\n", "\n", "Declaring commodity balances...\n", - "\t\t(0.0084 sec)\n", + "\t\t(0.0092 sec)\n", "\n", "\t\t(0.0000 sec)\n", "\n", "Declaring objective function...\n", - "\t\t(0.0931 sec)\n", + "\t\t(0.1587 sec)\n", "\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp7uymbmyq.glpk.raw --wglp /tmp/tmpxfu9nc06.glpk.glp --cpxlp\n", - " /tmp/tmpt1z47td8.pyomo.lp\n", - "Reading problem data from '/tmp/tmpt1z47td8.pyomo.lp'...\n", - "/tmp/tmpt1z47td8.pyomo.lp:24990: warning: lower bound of variable 'x352' redefined\n", - "/tmp/tmpt1z47td8.pyomo.lp:24990: warning: upper bound of variable 'x352' redefined\n", + " --write C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpji4o8a7e.glpk.raw --wglp\n", + " C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpxrkzd3l2.glpk.glp --cpxlp C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpb210kk3l.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpb210kk3l.pyomo.lp'...\n", + "C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpb210kk3l.pyomo.lp:24990: warning: lower bound of variable 'x352' redefined\n", + "C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpb210kk3l.pyomo.lp:24990: warning: upper bound of variable 'x352' redefined\n", "3883 rows, 2707 columns, 10620 non-zeros\n", "337 integer variables, all of which are binary\n", "25327 lines were read\n", - "Writing problem data to '/tmp/tmpxfu9nc06.glpk.glp'...\n", + "Writing problem data to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpxrkzd3l2.glpk.glp'...\n", "22278 lines were written\n", "GLPK Integer Optimizer 5.0\n", "3883 rows, 2707 columns, 10620 non-zeros\n", @@ -737,47 +737,47 @@ "3703 rows, 2528 columns, 10261 non-zeros\n", " 0: obj = 0.000000000e+00 inf = 6.749e+03 (428)\n", "Perturbing LP to avoid stalling [199]...\n", - " 720: obj = 2.007285148e+04 inf = 1.301e-08 (0) 5\n", - "Removing LP perturbation [2322]...\n", - "* 2322: obj = 1.444372703e+03 inf = 6.750e-14 (0) 13\n", + " 720: obj = 2.007285159e+04 inf = 1.467e-08 (0) 5\n", + "Removing LP perturbation [2320]...\n", + "* 2320: obj = 1.444372704e+03 inf = 8.246e-14 (0) 13\n", "OPTIMAL LP SOLUTION FOUND\n", "Integer optimization begins...\n", "Long-step dual simplex will be used\n", - "+ 2322: mip = not found yet >= -inf (1; 0)\n", - "+ 2344: >>>>> 1.444372741e+03 >= 1.444372703e+03 < 0.1% (11; 0)\n", - "+ 2344: mip = 1.444372741e+03 >= tree is empty 0.0% (0; 21)\n", + "+ 2320: mip = not found yet >= -inf (1; 0)\n", + "+ 2344: >>>>> 1.444372742e+03 >= 1.444372704e+03 < 0.1% (13; 0)\n", + "+ 2344: mip = 1.444372742e+03 >= tree is empty 0.0% (0; 25)\n", "INTEGER OPTIMAL SOLUTION FOUND\n", - "Time used: 0.3 secs\n", - "Memory used: 6.2 Mb (6512172 bytes)\n", - "Writing MIP solution to '/tmp/tmp7uymbmyq.glpk.raw'...\n", + "Time used: 0.2 secs\n", + "Memory used: 6.2 Mb (6543460 bytes)\n", + "Writing MIP solution to 'C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpji4o8a7e.glpk.raw'...\n", "6599 lines were written\n", "\n", "Status: ok\n", "Termination condition: optimal\n", "Statistics: \n", " Branch and bound: \n", - " Number of bounded subproblems: 21\n", - " Number of created subproblems: 21\n", + " Number of bounded subproblems: 25\n", + " Number of created subproblems: 25\n", "Error rc: 0\n", - "Time: 0.3461582660675049\n", + "Time: 0.32294297218322754\n", "\n", "\n", "Name: unknown\n", - "Lower bound: 1444.3727410255\n", - "Upper bound: 1444.3727410255\n", + "Lower bound: 1444.37274233088\n", + "Upper bound: 1444.37274233088\n", "Number of objectives: 1\n", "Number of constraints: 3883\n", "Number of variables: 2707\n", "Number of nonzeros: 10620\n", "Sense: minimize\n", "\n", - "Solve time: 0.44771242141723633 sec.\n", + "Solve time: 0.5451617240905762 sec.\n", "\n", "Processing optimization output...\n", - "for SourceSinkModel ...(0.1447sec)\n", - "for ConversionPartLoadModel ...(0.1244sec)\n", - "for StorageModel ... (0.1426sec)\n", - "\t\t(0.4157 sec)\n", + "for SourceSinkModel ...(0.1649sec)\n", + "for ConversionPartLoadModel ...(0.1207sec)\n", + "for StorageModel ... (0.1415sec)\n", + "\t\t(0.4311 sec)\n", "\n" ] }, @@ -785,7 +785,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/fast/home/d-marmullaku/Diss/fine/fine/storage.py:1978: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", + "C:\\Users\\t.gross\\Documents\\Programming\\Jugit\\fine\\fine\\storage.py:1984: UserWarning: Charge and discharge at the same time for component Salt caverns (hydrogen)\n", " warnings.warn(\n" ] } @@ -866,41 +866,41 @@ " Wind (onshore)\n", " NPVcontribution\n", " [1e9 Euro]\n", - " 2.210271\n", + " 2.210274\n", " \n", " \n", " TAC\n", " [1e9 Euro/a]\n", - " 2.210271\n", + " 2.210274\n", " \n", " \n", " capacity\n", " [GW$_{el}$]\n", - " 16.489955\n", + " 16.489973\n", " \n", " \n", " capexCap\n", " [1e9 Euro/a]\n", - " 1.847492\n", + " 1.847494\n", " \n", " \n", " commissioning\n", " [GW$_{el}$]\n", - " 16.489955\n", + " 16.489973\n", " \n", " \n", " invest\n", " [1e9 Euro]\n", - " 18.138951\n", + " 18.138971\n", " \n", " \n", " operation\n", " [GW$_{el}$*h/a]\n", - " 56346.194513\n", + " 56346.222875\n", " \n", " \n", " [GW$_{el}$*h]\n", - " 1080.61195\n", + " 1080.612494\n", " \n", " \n", " opexCap\n", @@ -916,14 +916,14 @@ "Component Property Unit \n", "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 28588.126857\n", " [GW$_{H_{2},LHV}$*h] 548.265447\n", - "Wind (onshore) NPVcontribution [1e9 Euro] 2.210271\n", - " TAC [1e9 Euro/a] 2.210271\n", - " capacity [GW$_{el}$] 16.489955\n", - " capexCap [1e9 Euro/a] 1.847492\n", - " commissioning [GW$_{el}$] 16.489955\n", - " invest [1e9 Euro] 18.138951\n", - " operation [GW$_{el}$*h/a] 56346.194513\n", - " [GW$_{el}$*h] 1080.61195\n", + "Wind (onshore) NPVcontribution [1e9 Euro] 2.210274\n", + " TAC [1e9 Euro/a] 2.210274\n", + " capacity [GW$_{el}$] 16.489973\n", + " capexCap [1e9 Euro/a] 1.847494\n", + " commissioning [GW$_{el}$] 16.489973\n", + " invest [1e9 Euro] 18.138971\n", + " operation [GW$_{el}$*h/a] 56346.222875\n", + " [GW$_{el}$*h] 1080.612494\n", " opexCap [1e9 Euro/a] 0.362779" ] }, @@ -999,12 +999,12 @@ " Electroylzers\n", " NPVcontribution\n", " [1e9 Euro]\n", - " 1441.053418\n", + " 1441.053417\n", " \n", " \n", " TAC\n", " [1e9 Euro/a]\n", - " 1441.053418\n", + " 1441.053417\n", " \n", " \n", " capacity\n", @@ -1024,7 +1024,7 @@ " \n", " invest\n", " [1e9 Euro]\n", - " 9061.548456\n", + " 9061.548451\n", " \n", " \n", " isBuilt\n", @@ -1034,11 +1034,11 @@ " \n", " operation\n", " [GW$_{el}$*h/a]\n", - " 56031.729791\n", + " 56031.729261\n", " \n", " \n", " [GW$_{el}$*h]\n", - " 1074.581119\n", + " 1074.581109\n", " \n", " \n", " opexCap\n", @@ -1052,15 +1052,15 @@ "text/plain": [ " GermanyRegion\n", "Component Property Unit \n", - "Electroylzers NPVcontribution [1e9 Euro] 1441.053418\n", - " TAC [1e9 Euro/a] 1441.053418\n", + "Electroylzers NPVcontribution [1e9 Euro] 1441.053417\n", + " TAC [1e9 Euro/a] 1441.053417\n", " capacity [GW$_{el}$] 10.068387\n", " capexCap [1e9 Euro/a] 1350.437933\n", " commissioning [GW$_{el}$] 10.068387\n", - " invest [1e9 Euro] 9061.548456\n", + " invest [1e9 Euro] 9061.548451\n", " isBuilt [-] 1.0\n", - " operation [GW$_{el}$*h/a] 56031.729791\n", - " [GW$_{el}$*h] 1074.581119\n", + " operation [GW$_{el}$*h/a] 56031.729261\n", + " [GW$_{el}$*h] 1074.581109\n", " opexCap [1e9 Euro/a] 90.615485" ] }, @@ -1143,17 +1143,17 @@ " Li-ion batteries\n", " NPVcontribution\n", " [1e9 Euro]\n", - " 0.39291\n", + " 0.392909\n", " \n", " \n", " TAC\n", " [1e9 Euro/a]\n", - " 0.39291\n", + " 0.392909\n", " \n", " \n", " capacity\n", " [GW$_{el}$*h]\n", - " 23.383515\n", + " 23.383494\n", " \n", " \n", " capexCap\n", @@ -1163,30 +1163,30 @@ " \n", " commissioning\n", " [GW$_{el}$*h]\n", - " 23.383515\n", + " 23.383494\n", " \n", " \n", " invest\n", " [1e9 Euro]\n", - " 3.530911\n", + " 3.530908\n", " \n", " \n", " operationCharge\n", " [GW$_{el}$*h/a]\n", - " 3205.335238\n", + " 3205.356143\n", " \n", " \n", " [GW$_{el}$*h]\n", - " 61.472183\n", + " 61.472584\n", " \n", " \n", " operationDischarge\n", " [GW$_{el}$*h/a]\n", - " 55.441352\n", + " 55.441199\n", " \n", " \n", " [GW$_{el}$*h]\n", - " 55.441352\n", + " 55.441199\n", " \n", " \n", " opexCap\n", @@ -1227,20 +1227,20 @@ " \n", " operationCharge\n", " [GW$_{H_{2},LHV}$*h/a]\n", - " 10408.111893\n", + " 9801.382275\n", " \n", " \n", " [GW$_{H_{2},LHV}$*h]\n", - " 199.607625\n", + " 187.971715\n", " \n", " \n", " operationDischarge\n", " [GW$_{H_{2},LHV}$*h/a]\n", - " 199.607625\n", + " 187.971715\n", " \n", " \n", " [GW$_{H_{2},LHV}$*h]\n", - " 199.607625\n", + " 187.971715\n", " \n", " \n", " opexCap\n", @@ -1254,16 +1254,16 @@ "text/plain": [ " GermanyRegion\n", "Component Property Unit \n", - "Li-ion batteries NPVcontribution [1e9 Euro] 0.39291\n", - " TAC [1e9 Euro/a] 0.39291\n", - " capacity [GW$_{el}$*h] 23.383515\n", + "Li-ion batteries NPVcontribution [1e9 Euro] 0.392909\n", + " TAC [1e9 Euro/a] 0.392909\n", + " capacity [GW$_{el}$*h] 23.383494\n", " capexCap [1e9 Euro/a] 0.346142\n", - " commissioning [GW$_{el}$*h] 23.383515\n", - " invest [1e9 Euro] 3.530911\n", - " operationCharge [GW$_{el}$*h/a] 3205.335238\n", - " [GW$_{el}$*h] 61.472183\n", - " operationDischarge [GW$_{el}$*h/a] 55.441352\n", - " [GW$_{el}$*h] 55.441352\n", + " commissioning [GW$_{el}$*h] 23.383494\n", + " invest [1e9 Euro] 3.530908\n", + " operationCharge [GW$_{el}$*h/a] 3205.356143\n", + " [GW$_{el}$*h] 61.472584\n", + " operationDischarge [GW$_{el}$*h/a] 55.441199\n", + " [GW$_{el}$*h] 55.441199\n", " opexCap [1e9 Euro/a] 0.046767\n", "Salt caverns (hydrogen) NPVcontribution [1e9 Euro] 0.716143\n", " TAC [1e9 Euro/a] 0.716143\n", @@ -1271,10 +1271,10 @@ " capexCap [1e9 Euro/a] 0.012069\n", " commissioning [GW$_{H_{2},LHV}$*h] 1235.216115\n", " invest [1e9 Euro] 0.135874\n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 10408.111893\n", - " [GW$_{H_{2},LHV}$*h] 199.607625\n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 199.607625\n", - " [GW$_{H_{2},LHV}$*h] 199.607625\n", + " operationCharge [GW$_{H_{2},LHV}$*h/a] 9801.382275\n", + " [GW$_{H_{2},LHV}$*h] 187.971715\n", + " operationDischarge [GW$_{H_{2},LHV}$*h/a] 187.971715\n", + " [GW$_{H_{2},LHV}$*h] 187.971715\n", " opexCap [1e9 Euro/a] 0.704073" ] }, @@ -1302,7 +1302,7 @@ "outputs": [ { "data": { - "image/png": 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    " ] @@ -1338,7 +1338,7 @@ "outputs": [ { "data": { - "image/png": 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    " ] diff --git a/examples/Partload/getData.py b/examples/11_Partload/getData.py similarity index 94% rename from examples/Partload/getData.py rename to examples/11_Partload/getData.py index 1a56a1da..417edd11 100644 --- a/examples/Partload/getData.py +++ b/examples/11_Partload/getData.py @@ -4,7 +4,7 @@ def getData(engine="openpyxl"): - current_directory = Path(__file__).parents[1].joinpath("1node_Energy_System_Workflow").absolute() + current_directory = Path(__file__).parents[1].joinpath("01_1node_Energy_System_Workflow").absolute() inputDataPath = os.path.join(current_directory, "InputData") data = {} diff --git a/examples/District_Optimization/Urban_District_Optimization_Workflow.ipynb b/examples/District_Optimization/Urban_District_Optimization_Workflow.ipynb deleted file mode 100644 index 40b62502..00000000 --- a/examples/District_Optimization/Urban_District_Optimization_Workflow.ipynb +++ /dev/null @@ -1,619 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workflow for a district optimization\n", - "\n", - "In this application of the FINE framework, a small district is modeled and optimized.\n", - "\n", - "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", - "\n", - "The workflow is structures as follows:\n", - "1. Required packages are imported and the input data path is set\n", - "2. An energy system model instance is created\n", - "3. Commodity sources are added to the energy system model\n", - "4. Commodity conversion components are added to the energy system model\n", - "5. Commodity storages are added to the energy system model\n", - "6. Commodity transmission components are added to the energy system model\n", - "7. Commodity sinks are added to the energy system model\n", - "8. The energy system model is optimized\n", - "9. Selected optimization results are presented\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Import required packages and set input data path\n", - "\n", - "The FINE framework is imported which provides the required classes and functions for modeling the energy system." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import fine as fn\n", - "from getData import getData\n", - "import pandas as pd\n", - "\n", - "data = getData()\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Create an energy system model instance \n", - "\n", - "The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", - "\n", - "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "locations = data[\"locations\"]\n", - "commodityUnitDict = {\"electricity\": \"kW_el\", \"methane\": \"kW_CH4_LHV\", \"heat\": \"kW_th\"}\n", - "commodities = {\"electricity\", \"methane\", \"heat\"}\n", - "numberOfTimeSteps = 8760\n", - "hoursPerTimeStep = 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM = fn.EnergySystemModel(\n", - " locations=locations,\n", - " commodities=commodities,\n", - " numberOfTimeSteps=8760,\n", - " commodityUnitsDict=commodityUnitDict,\n", - " hoursPerTimeStep=1,\n", - " costUnit=\"€\",\n", - " lengthUnit=\"m\",\n", - " verboseLogLevel=2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Add commodity sources to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electricity Purchase" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Electricity purchase\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=False,\n", - " operationRateMax=data[\"El Purchase, operationRateMax\"],\n", - " commodityCost=0.298,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Natural Gas Purchase" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"NaturalGas purchase\",\n", - " commodity=\"methane\",\n", - " hasCapacityVariable=False,\n", - " operationRateMax=data[\"NG Purchase, operationRateMax\"],\n", - " commodityCost=0.065,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PV" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"PV\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " hasIsBuiltBinaryVariable=True,\n", - " operationRateMax=data[\"PV, operationRateMax\"],\n", - " capacityMax=data[\"PV, capacityMax\"],\n", - " interestRate=0.04,\n", - " economicLifetime=20,\n", - " investIfBuilt=1000,\n", - " investPerCapacity=1400,\n", - " opexIfBuilt=10,\n", - " bigM=40,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Add conversion components to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Boiler" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"Boiler\",\n", - " physicalUnit=\"kW_th\",\n", - " commodityConversionFactors={\"methane\": -1.1, \"heat\": 1},\n", - " hasIsBuiltBinaryVariable=True,\n", - " hasCapacityVariable=True,\n", - " interestRate=0.04,\n", - " economicLifetime=20,\n", - " investIfBuilt=2800,\n", - " investPerCapacity=100,\n", - " opexIfBuilt=24,\n", - " bigM=200,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Add commodity storages to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Thermal Storage " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Thermal Storage\",\n", - " commodity=\"heat\",\n", - " selfDischarge=0.001,\n", - " hasIsBuiltBinaryVariable=True,\n", - " capacityMax=data[\"TS, capacityMax\"],\n", - " interestRate=0.04,\n", - " economicLifetime=25,\n", - " investIfBuilt=23,\n", - " investPerCapacity=24,\n", - " bigM=250,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Battery Storage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Battery Storage\",\n", - " commodity=\"electricity\",\n", - " cyclicLifetime=10000,\n", - " chargeEfficiency=0.95,\n", - " dischargeEfficiency=0.95,\n", - " chargeRate=0.5,\n", - " dischargeRate=0.5,\n", - " hasIsBuiltBinaryVariable=True,\n", - " capacityMax=data[\"BS, capacityMax\"],\n", - " interestRate=0.04,\n", - " economicLifetime=12,\n", - " investIfBuilt=2000,\n", - " investPerCapacity=700,\n", - " bigM=110,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. Add commodity transmission components to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cable Electricty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"E_Distribution_Grid\",\n", - " commodity=\"electricity\",\n", - " losses=0.00001,\n", - " distances=data[\"cables, distances\"],\n", - " capacityFix=data[\"cables, capacityFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Natural Gas Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"NG_Distribution_Grid\",\n", - " commodity=\"methane\",\n", - " distances=data[\"NG, distances\"],\n", - " capacityFix=data[\"NG, capacityFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7. Add commodity sinks to the energy system model\n", - "\n", - "### Electricity Demand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"Electricity demand\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=False,\n", - " operationRateFix=data[\"Electricity demand, operationRateFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Heat Demand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"BuildingsHeat\",\n", - " commodity=\"heat\",\n", - " hasCapacityVariable=False,\n", - " operationRateFix=data[\"Heat demand, operationRateFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 8. Optimize energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.aggregateTemporally(numberOfTypicalPeriods=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='cuts=0 method=2')\n", - "esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 9. Selected results output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sources and Sink" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"PV\", \"bd1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"bd1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"Electricity purchase\", \"transformer\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"NaturalGas purchase\", \"transformer\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conversion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"Boiler\", \"bd1\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Storage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "fig, ax = fn.plotOperationColorMap(\n", - " esM, \"Thermal Storage\", \"bd1\", variableName=\"stateOfChargeOperationVariablesOptimum\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transmission" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2)" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Model_Run_from_Excel/scenarioResults.xlsx b/examples/Model_Run_from_Excel/scenarioResults.xlsx deleted file mode 100644 index 3822d054..00000000 Binary files a/examples/Model_Run_from_Excel/scenarioResults.xlsx and /dev/null differ diff --git a/examples/Multi-regional_Energy_System_Workflow/Multi-regional_Energy_System_Workflow.ipynb b/examples/Multi-regional_Energy_System_Workflow/Multi-regional_Energy_System_Workflow.ipynb deleted file mode 100644 index cb1f4e58..00000000 --- a/examples/Multi-regional_Energy_System_Workflow/Multi-regional_Energy_System_Workflow.ipynb +++ /dev/null @@ -1,5419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workflow for a multi-regional energy system\n", - "\n", - "In this application of the FINE framework, a multi-regional energy system is modeled and optimized.\n", - "\n", - "All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n", - "\n", - "The workflow is structures as follows:\n", - "1. Required packages are imported and the input data path is set\n", - "2. An energy system model instance is created\n", - "3. Commodity sources are added to the energy system model\n", - "4. Commodity conversion components are added to the energy system model\n", - "5. Commodity storages are added to the energy system model\n", - "6. Commodity transmission components are added to the energy system model\n", - "7. Commodity sinks are added to the energy system model\n", - "8. The energy system model is optimized\n", - "9. Selected optimization results are presented\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Import required packages and set input data path\n", - "\n", - "The FINE framework is imported which provides the required classes and functions for modeling the energy system." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import fine as fn\n", - "import matplotlib.pyplot as plt\n", - "from getData import getData\n", - "import pandas as pd\n", - "import os\n", - "\n", - "cwd = os.getcwd()\n", - "data = getData()\n", - "\n", - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2. Create an energy system model instance \n", - "\n", - "The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", - "\n", - "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "locations = {\n", - " \"cluster_0\",\n", - " \"cluster_1\",\n", - " \"cluster_2\",\n", - " \"cluster_3\",\n", - " \"cluster_4\",\n", - " \"cluster_5\",\n", - " \"cluster_6\",\n", - " \"cluster_7\",\n", - "}\n", - "commodityUnitDict = {\n", - " \"electricity\": r\"GW$_{el}$\",\n", - " \"methane\": r\"GW$_{CH_{4},LHV}$\",\n", - " \"biogas\": r\"GW$_{biogas,LHV}$\",\n", - " \"CO2\": r\"Mio. t$_{CO_2}$/h\",\n", - " \"hydrogen\": r\"GW$_{H_{2},LHV}$\",\n", - "}\n", - "commodities = {\"electricity\", \"hydrogen\", \"methane\", \"biogas\", \"CO2\"}\n", - "numberOfTimeSteps = 8760\n", - "hoursPerTimeStep = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "esM = fn.EnergySystemModel(\n", - " locations=locations,\n", - " commodities=commodities,\n", - " numberOfTimeSteps=8760,\n", - " commodityUnitsDict=commodityUnitDict,\n", - " hoursPerTimeStep=1,\n", - " costUnit=\"1e9 Euro\",\n", - " lengthUnit=\"km\",\n", - " verboseLogLevel=0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "CO2_reductionTarget = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3. Add commodity sources to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.1. Electricity sources" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wind onshore" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Wind (onshore)\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " operationRateMax=data[\"Wind (onshore), operationRateMax\"],\n", - " capacityMax=data[\"Wind (onshore), capacityMax\"],\n", - " investPerCapacity=1.1,\n", - " opexPerCapacity=1.1 * 0.02,\n", - " interestRate=0.08,\n", - " economicLifetime=20,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Full load hours:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cluster_0 1572.003960\n", - "cluster_1 2350.292663\n", - "cluster_2 2374.507270\n", - "cluster_3 2186.572278\n", - "cluster_4 1572.650655\n", - "cluster_5 1767.840650\n", - "cluster_6 2719.564564\n", - "cluster_7 1553.045964\n", - "dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[\"Wind (onshore), operationRateMax\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wind offshore" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Wind (offshore)\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " operationRateMax=data[\"Wind (offshore), operationRateMax\"],\n", - " capacityMax=data[\"Wind (offshore), capacityMax\"],\n", - " investPerCapacity=2.3,\n", - " opexPerCapacity=2.3 * 0.02,\n", - " interestRate=0.08,\n", - " economicLifetime=20,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Full load hours:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cluster_0 0.000000\n", - "cluster_1 4435.420314\n", - "cluster_2 4301.655834\n", - "cluster_3 3902.391858\n", - "cluster_4 0.000000\n", - "cluster_5 0.000000\n", - "cluster_6 4609.508396\n", - "cluster_7 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[\"Wind (offshore), operationRateMax\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PV" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"PV\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " operationRateMax=data[\"PV, operationRateMax\"],\n", - " capacityMax=data[\"PV, capacityMax\"],\n", - " investPerCapacity=0.65,\n", - " opexPerCapacity=0.65 * 0.02,\n", - " interestRate=0.08,\n", - " economicLifetime=25,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Full load hours:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cluster_0 1113.216464\n", - "cluster_1 1053.579422\n", - "cluster_2 1058.005181\n", - "cluster_3 1079.872237\n", - "cluster_4 1140.407380\n", - "cluster_5 1051.848141\n", - "cluster_6 1069.843344\n", - "cluster_7 1085.697466\n", - "dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[\"PV, operationRateMax\"].sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exisisting run-of-river hydroelectricity plants" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Existing run-of-river plants\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " operationRateFix=data[\"Existing run-of-river plants, operationRateFix\"],\n", - " tsaWeight=0.01,\n", - " capacityFix=data[\"Existing run-of-river plants, capacityFix\"],\n", - " investPerCapacity=0,\n", - " opexPerCapacity=0.208,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3.2. Methane (natural gas and biogas)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Natural gas" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Natural gas purchase\",\n", - " commodity=\"methane\",\n", - " hasCapacityVariable=False,\n", - " commodityCost=0.0331 * 1e-3,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Biogas" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"Biogas purchase\",\n", - " commodity=\"biogas\",\n", - " operationRateMax=data[\"Biogas, operationRateMax\"],\n", - " hasCapacityVariable=False,\n", - " commodityCost=0.05409 * 1e-3,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Add conversion components to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combined cycle gas turbine plants" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"CCGT plants (methane)\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " commodityConversionFactors={\n", - " \"electricity\": 1,\n", - " \"methane\": -1 / 0.6,\n", - " \"CO2\": 201 * 1e-6 / 0.6,\n", - " },\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=0.65,\n", - " opexPerCapacity=0.021,\n", - " interestRate=0.08,\n", - " economicLifetime=33,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### New combined cycle gas turbine plants for biogas" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"New CCGT plants (biogas)\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " commodityConversionFactors={\"electricity\": 1, \"biogas\": -1 / 0.63},\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=0.7,\n", - " opexPerCapacity=0.021,\n", - " interestRate=0.08,\n", - " economicLifetime=33,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### New combined cycly gas turbines for hydrogen" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"New CCGT plants (hydrogen)\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.63},\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=0.7,\n", - " opexPerCapacity=0.021,\n", - " interestRate=0.08,\n", - " economicLifetime=33,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electrolyzers" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"Electrolyzer\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.7},\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=0.5,\n", - " opexPerCapacity=0.5 * 0.025,\n", - " interestRate=0.08,\n", - " economicLifetime=10,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### rSOC" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "capexRSOC = 1.5\n", - "\n", - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"rSOEC\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " linkedConversionCapacityID=\"rSOC\",\n", - " commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.6},\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=capexRSOC / 2,\n", - " opexPerCapacity=capexRSOC * 0.02 / 2,\n", - " interestRate=0.08,\n", - " economicLifetime=10,\n", - " )\n", - ")\n", - "\n", - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"rSOFC\",\n", - " physicalUnit=r\"GW$_{el}$\",\n", - " linkedConversionCapacityID=\"rSOC\",\n", - " commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.6},\n", - " hasCapacityVariable=True,\n", - " investPerCapacity=capexRSOC / 2,\n", - " opexPerCapacity=capexRSOC * 0.02 / 2,\n", - " interestRate=0.08,\n", - " economicLifetime=10,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. Add commodity storages to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.1. Electricity storage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lithium ion batteries\n", - "\n", - "The self discharge of a lithium ion battery is here described as 3% per month. The self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Li-ion batteries\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " chargeEfficiency=0.95,\n", - " cyclicLifetime=10000,\n", - " dischargeEfficiency=0.95,\n", - " selfDischarge=1 - (1 - 0.03) ** (1 / (30 * 24)),\n", - " chargeRate=1,\n", - " dischargeRate=1,\n", - " doPreciseTsaModeling=False,\n", - " investPerCapacity=0.151,\n", - " opexPerCapacity=0.002,\n", - " interestRate=0.08,\n", - " economicLifetime=22,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.2. Hydrogen storage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hydrogen filled salt caverns\n", - "The maximum capacity is here obtained by: dividing the given capacity (which is given for methane) by the lower heating value of methane and then multiplying it with the lower heating value of hydrogen." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Salt caverns (hydrogen)\",\n", - " commodity=\"hydrogen\",\n", - " hasCapacityVariable=True,\n", - " capacityVariableDomain=\"continuous\",\n", - " capacityPerPlantUnit=133,\n", - " chargeRate=1 / 470.37,\n", - " dischargeRate=1 / 470.37,\n", - " sharedPotentialID=\"Existing salt caverns\",\n", - " stateOfChargeMin=0.33,\n", - " stateOfChargeMax=1,\n", - " capacityMax=data[\"Salt caverns (hydrogen), capacityMax\"],\n", - " investPerCapacity=0.00011,\n", - " opexPerCapacity=0.00057,\n", - " interestRate=0.08,\n", - " economicLifetime=30,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.3. Methane storage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Methane filled salt caverns" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Salt caverns (biogas)\",\n", - " commodity=\"biogas\",\n", - " hasCapacityVariable=True,\n", - " capacityVariableDomain=\"continuous\",\n", - " capacityPerPlantUnit=443,\n", - " chargeRate=1 / 470.37,\n", - " dischargeRate=1 / 470.37,\n", - " sharedPotentialID=\"Existing salt caverns\",\n", - " stateOfChargeMin=0.33,\n", - " stateOfChargeMax=1,\n", - " capacityMax=data[\"Salt caverns (methane), capacityMax\"],\n", - " investPerCapacity=0.00004,\n", - " opexPerCapacity=0.00001,\n", - " interestRate=0.08,\n", - " economicLifetime=30,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.4 Pumped hydro storage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pumped hydro storage" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Pumped hydro storage\",\n", - " commodity=\"electricity\",\n", - " chargeEfficiency=0.88,\n", - " dischargeEfficiency=0.88,\n", - " hasCapacityVariable=True,\n", - " selfDischarge=1 - (1 - 0.00375) ** (1 / (30 * 24)),\n", - " chargeRate=0.16,\n", - " dischargeRate=0.12,\n", - " capacityFix=data[\"Pumped hydro storage, capacityFix\"],\n", - " investPerCapacity=0,\n", - " opexPerCapacity=0.000153,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. Add commodity transmission components to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.1. Electricity transmission" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### AC cables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "esM.add(fn.LinearOptimalPowerFlow(esM=esM, name='AC cables', commodity='electricity',\n", - " hasCapacityVariable=True, capacityFix=data['AC cables, capacityFix'],\n", - " reactances=data['AC cables, reactances']))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The distances of a component are set to a normalized value of 1.\n" - ] - } - ], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"AC cables\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=True,\n", - " capacityFix=data[\"AC cables, capacityFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DC cables" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"DC cables\",\n", - " commodity=\"electricity\",\n", - " losses=data[\"DC cables, losses\"],\n", - " distances=data[\"DC cables, distances\"],\n", - " hasCapacityVariable=True,\n", - " capacityFix=data[\"DC cables, capacityFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.2 Methane transmission" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Methane pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"Pipelines (biogas)\",\n", - " commodity=\"biogas\",\n", - " distances=data[\"Pipelines, distances\"],\n", - " hasCapacityVariable=True,\n", - " hasIsBuiltBinaryVariable=True,\n", - " bigM=300,\n", - " locationalEligibility=data[\"Pipelines, eligibility\"],\n", - " capacityMax=data[\"Pipelines, eligibility\"] * 15,\n", - " sharedPotentialID=\"pipelines\",\n", - " investPerCapacity=0.000037,\n", - " investIfBuilt=0.000314,\n", - " interestRate=0.08,\n", - " economicLifetime=40,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.3 Hydrogen transmission" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hydrogen pipelines" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"Pipelines (hydrogen)\",\n", - " commodity=\"hydrogen\",\n", - " distances=data[\"Pipelines, distances\"],\n", - " hasCapacityVariable=True,\n", - " hasIsBuiltBinaryVariable=True,\n", - " bigM=300,\n", - " locationalEligibility=data[\"Pipelines, eligibility\"],\n", - " capacityMax=data[\"Pipelines, eligibility\"] * 15,\n", - " sharedPotentialID=\"pipelines\",\n", - " investPerCapacity=0.000177,\n", - " investIfBuilt=0.00033,\n", - " interestRate=0.08,\n", - " economicLifetime=40,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7. Add commodity sinks to the energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7.1. Electricity sinks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electricity demand" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"Electricity demand\",\n", - " commodity=\"electricity\",\n", - " hasCapacityVariable=False,\n", - " operationRateFix=data[\"Electricity demand, operationRateFix\"],\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7.2. Hydrogen sinks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fuel cell electric vehicle (FCEV) demand" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "FCEV_penetration = 0.5\n", - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"Hydrogen demand\",\n", - " commodity=\"hydrogen\",\n", - " hasCapacityVariable=False,\n", - " operationRateFix=data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7.3. CO2 sinks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### CO2 exiting the system's boundary" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"CO2 to enviroment\",\n", - " commodity=\"CO2\",\n", - " hasCapacityVariable=False,\n", - " commodityLimitID=\"CO2 limit\",\n", - " yearlyLimit=366 * (1 - CO2_reductionTarget),\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 8 Temporal Aggregation" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "tags": [ - "nbval-ignore-output" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Clustering time series data with 7 typical periods and 24 time steps per period...\n", - "\t\t(2.3767 sec)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " C:\\Users\\t.gross\\Anaconda3\\envs\\dev_FINE\\lib\\site-packages\\tsam\\timeseriesaggregation.py:1070: UserWarning:Something went wrong: At least one maximal value of the aggregated time series exceeds the maximal value the input time series\n" - ] - } - ], - "source": [ - "esM.aggregateTemporally(numberOfTypicalPeriods=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optimization" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "scrolled": true, - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time series aggregation specifications:\n", - "Number of typical periods:7, number of time steps per period:24\n", - "\n", - "Declaring sets, variables and constraints for SourceSinkModel\n", - "\tdeclaring sets... \n", - "\tdeclaring variables... \n", - "\tdeclaring constraints... \n", - "\t\t(0.7352 sec)\n", - "\n", - "Declaring sets, variables and constraints for ConversionModel\n", - "\tdeclaring sets... \n", - "\tdeclaring variables... \n", - "\tdeclaring constraints... \n", - "\t\t(0.3377 sec)\n", - "\n", - "Declaring sets, variables and constraints for StorageModel\n", - "\tdeclaring sets... \n", - "\tdeclaring variables... \n", - "\tdeclaring constraints... \n", - "\t\t(2.5192 sec)\n", - "\n", - "Declaring sets, variables and constraints for TransmissionModel\n", - "\tdeclaring sets... \n", - "\tdeclaring variables... \n", - "\tdeclaring constraints... \n", - "\t\t(0.6316 sec)\n", - "\n", - "Declaring shared potential constraint...\n", - "\t\t(0.0030 sec)\n", - "\n", - "Declaring linked component quantity constraint...\n", - "\t\t(0.0000 sec)\n", - "\n", - "Declaring commodity balances...\n", - "\t\t(1.0349 sec)\n", - "\n", - "\t\t(0.0000 sec)\n", - "\n", - "Declaring objective function...\n", - "\t\t(0.4120 sec)\n", - "\n", - "Either solver not selected or specified solver not available.gurobi is set as solver.\n", - "Using license file C:\\Users\\t.gross\\gurobi.lic\n", - "Academic license - for non-commercial use only\n", - "Read LP format model from file C:\\Users\\T58C8~1.GRO\\AppData\\Local\\Temp\\tmpvduyqo_c.pyomo.lp\n", - "Reading time = 0.39 seconds\n", - "x96946: 85942 rows, 55618 columns, 264787 nonzeros\n", - "Changed value of parameter QCPDual to 1\n", - " Prev: 0 Min: 0 Max: 1 Default: 0\n", - "Changed value of parameter Threads to 3\n", - " Prev: 0 Min: 0 Max: 1024 Default: 0\n", - "Parameter logfile unchanged\n", - " Value: Default: \n", - "Changed value of parameter OptimalityTol to 0.001\n", - " Prev: 1e-06 Min: 1e-09 Max: 0.01 Default: 1e-06\n", - "Changed value of parameter method to 2\n", - " Prev: -1 Min: -1 Max: 5 Default: -1\n", - "Changed value of parameter cuts to 0\n", - " Prev: -1 Min: -1 Max: 3 Default: -1\n", - "Changed value of parameter MIPGap to 0.005\n", - " Prev: 0.0001 Min: 0.0 Max: inf Default: 0.0001\n", - "Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (win64)\n", - "Optimize a model with 85942 rows, 55618 columns and 264787 nonzeros\n", - "Model fingerprint: 0xa45d037c\n", - "Variable types: 55566 continuous, 52 integer (52 binary)\n", - "Coefficient statistics:\n", - " Matrix range [2e-06, 5e+02]\n", - " Objective range [1e-05, 3e-01]\n", - " Bounds range [1e+00, 1e+05]\n", - " RHS range [4e-02, 3e+01]\n", - "Presolve removed 26139 rows and 15143 columns\n", - "Presolve time: 0.50s\n", - "Presolved: 59803 rows, 40475 columns, 207390 nonzeros\n", - "Variable types: 40449 continuous, 26 integer (26 binary)\n", - "Root barrier log...\n", - "\n", - "Ordering time: 1.18s\n", - "\n", - "Barrier statistics:\n", - " Dense cols : 188\n", - " Free vars : 1288\n", - " AA' NZ : 1.230e+06\n", - " Factor NZ : 6.586e+06 (roughly 100 MBytes of memory)\n", - " Factor Ops : 3.173e+09 (less than 1 second per iteration)\n", - " Threads : 3\n", - "\n", - " Objective Residual\n", - "Iter Primal Dual Primal Dual Compl Time\n", - " 0 2.78674159e+05 -2.24194221e+06 3.17e+05 3.87e-03 2.17e+03 2s\n", - " 1 2.59570369e+05 -2.15614669e+06 2.20e+05 2.00e-01 1.41e+03 2s\n", - " 2 1.86684134e+05 -1.96135838e+06 1.51e+05 1.07e-01 9.48e+02 3s\n", - " 3 8.46423238e+04 -1.50686486e+06 4.76e+04 3.79e-02 3.31e+02 3s\n", - " 4 2.80234249e+04 -6.85922647e+05 2.79e+03 1.83e-03 3.64e+01 3s\n", - " 5 1.91653046e+04 -2.13723619e+05 9.66e+01 6.54e-04 1.06e+01 3s\n", - " 6 6.99044164e+03 -1.63349490e+05 1.83e+01 3.27e-04 3.13e+00 4s\n", - " 7 4.11506465e+03 -6.79879556e+04 7.45e+00 1.22e-04 1.16e+00 4s\n", - " 8 1.51308517e+03 -1.59889366e+04 1.22e+00 5.55e-04 2.00e-01 4s\n", - " 9 5.57271886e+02 -2.99965692e+03 1.98e-01 2.16e-04 3.33e-02 4s\n", - " 10 2.69643491e+02 -6.17100408e+02 4.71e-02 1.88e-04 7.79e-03 4s\n", - " 11 1.57440697e+02 -2.89568051e+02 1.58e-02 9.04e-05 3.84e-03 4s\n", - " 12 9.08052671e+01 -1.03357712e+02 3.99e-03 4.11e-05 1.65e-03 5s\n", - " 13 7.81974606e+01 -3.59076717e+01 2.39e-03 2.34e-05 9.71e-04 5s\n", - " 14 6.99934488e+01 -3.06365807e+00 1.43e-03 1.49e-05 6.21e-04 5s\n", - " 15 6.54405007e+01 2.14959387e+01 1.01e-03 8.14e-06 3.74e-04 5s\n", - " 16 6.24298144e+01 3.58210054e+01 7.40e-04 4.40e-06 2.26e-04 5s\n", - " 17 5.66996402e+01 4.27718670e+01 2.13e-04 2.47e-06 1.18e-04 6s\n", - " 18 5.46325910e+01 4.67867510e+01 8.00e-05 1.49e-06 6.66e-05 6s\n", - " 19 5.39589168e+01 4.91055773e+01 4.89e-05 9.24e-07 4.12e-05 6s\n", - " 20 5.35018917e+01 5.02664664e+01 3.13e-05 6.15e-07 2.75e-05 6s\n", - " 21 5.32079971e+01 5.09186394e+01 2.06e-05 4.40e-07 1.94e-05 6s\n", - " 22 5.29013263e+01 5.14687105e+01 1.03e-05 2.94e-07 1.22e-05 7s\n", - " 23 5.27459168e+01 5.20878831e+01 4.88e-06 1.23e-07 5.59e-06 7s\n", - " 24 5.26427343e+01 5.24377278e+01 1.58e-06 6.97e-08 1.74e-06 7s\n", - " 25 5.26144584e+01 5.24933594e+01 8.20e-07 4.80e-08 1.03e-06 7s\n", - " 26 5.26021250e+01 5.25436472e+01 4.93e-07 2.24e-08 4.97e-07 7s\n", - " 27 5.25962500e+01 5.25524232e+01 3.48e-07 1.72e-08 3.72e-07 8s\n", - " 28 5.25931992e+01 5.25595348e+01 2.74e-07 1.29e-08 2.86e-07 8s\n", - " 29 5.25892013e+01 5.25660007e+01 1.82e-07 9.17e-09 1.97e-07 8s\n", - " 30 5.25868835e+01 5.25730227e+01 1.27e-07 5.46e-09 1.18e-07 8s\n", - " 31 5.25843152e+01 5.25773016e+01 6.69e-08 2.82e-09 5.96e-08 8s\n", - " 32 5.25822789e+01 5.25803694e+01 2.01e-08 6.72e-10 1.63e-08 8s\n", - " 33 5.25818461e+01 5.25809595e+01 1.07e-08 2.94e-10 7.56e-09 9s\n", - " 34 5.25816871e+01 5.25811994e+01 6.60e-09 1.41e-10 4.16e-09 9s\n", - " 35 5.25815419e+01 5.25813356e+01 4.30e-09 6.12e-11 1.76e-09 9s\n", - " 36 5.25815027e+01 5.25813877e+01 3.00e-09 1.94e-11 9.80e-10 9s\n", - " 37 5.25814528e+01 5.25814066e+01 1.12e-09 4.49e-12 3.93e-10 9s\n", - " 38 5.25814203e+01 5.25814125e+01 7.72e-10 1.17e-12 6.71e-11 10s\n", - " 39 5.25814185e+01 5.25814138e+01 2.20e-08 2.45e-10 3.99e-11 10s\n", - " 40 5.25814185e+01 5.25814137e+01 6.48e-08 4.41e-08 3.98e-11 10s\n", - " 41 5.25814185e+01 5.25814137e+01 1.30e-07 4.41e-08 3.98e-11 10s\n", - " 42 5.25814185e+01 5.25814137e+01 1.30e-07 4.41e-08 3.99e-11 11s\n", - "\n", - "Barrier solved model in 42 iterations and 10.51 seconds\n", - "Optimal objective 5.25814185e+01\n", - "\n", - "\n", - "Root crossover log...\n", - "\n", - " 16210 DPushes remaining with DInf 1.4109036e-02 11s\n", - " 0 DPushes remaining with DInf 2.1063811e+09 13s\n", - "\n", - "Restart crossover...\n", - "\n", - " 16227 DPushes remaining with DInf 0.0000000e+00 13s\n", - " 2517 DPushes remaining with DInf 2.3036386e-03 15s\n", - " 0 DPushes remaining with DInf 2.9545781e+01 16s\n", - "\n", - " 14204 PPushes remaining with PInf 1.5510727e-01 16s\n", - " 0 PPushes remaining with PInf 9.8639908e-05 18s\n", - "\n", - " Push phase complete: Pinf 9.8639908e-05, Dinf 2.2132113e+01 18s\n", - "\n", - "\n", - "Root simplex log...\n", - "\n", - "Iteration Objective Primal Inf. Dual Inf. Time\n", - " 22166 5.2581448e+01 0.000000e+00 2.167126e+01 18s\n", - " 22486 5.2581430e+01 0.000000e+00 0.000000e+00 19s\n", - " 22486 5.2581430e+01 0.000000e+00 0.000000e+00 19s\n", - "\n", - "Root relaxation: objective 5.258143e+01, 22486 iterations, 18.27 seconds\n", - "\n", - " Nodes | Current Node | Objective Bounds | Work\n", - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", - "\n", - " 0 0 52.58143 0 9 - 52.58143 - - 18s\n", - "H 0 0 52.6586131 52.58143 0.15% - 20s\n", - "\n", - "Explored 1 nodes (22486 simplex iterations) in 20.75 seconds\n", - "Thread count was 3 (of 8 available processors)\n", - "\n", - "Solution count 1: 52.6586 \n", - "\n", - "Optimal solution found (tolerance 5.00e-03)\n", - "Warning: max constraint violation (5.9330e-06) exceeds tolerance\n", - "Best objective 5.265861313765e+01, best bound 5.258143026864e+01, gap 0.1466%\n", - "\n", - "Status: ok\n", - "Return code: 0\n", - "Message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", - "Termination condition: optimal\n", - "Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available.\n", - "Wall time: 20.75193977355957\n", - "Error rc: 0\n", - "Time: 21.905832052230835\n", - "\n", - "\n", - "Name: x96946\n", - "Lower bound: 52.581430268635515\n", - "Upper bound: 52.65861313765053\n", - "Number of objectives: 1\n", - "Number of constraints: 85942\n", - "Number of variables: 55618\n", - "Number of binary variables: 52\n", - "Number of integer variables: 52\n", - "Number of continuous variables: 55566\n", - "Number of nonzeros: 264787\n", - "Sense: minimize\n", - "\n", - "Solve time: 28.89843249320984 sec.\n", - "\n", - "Processing optimization output...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "for SourceSinkModel ... (1.6979sec)\n", - "for ConversionModel ... (1.3208sec)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " c:\\users\\t.gross\\documents\\programming\\jugit\\fine\\FINE\\storage.py:1767: UserWarning:Charge and discharge at the same time for component Li-ion batteries\n", - " c:\\users\\t.gross\\documents\\programming\\jugit\\fine\\FINE\\storage.py:1767: UserWarning:Charge and discharge at the same time for component Pumped hydro storage\n", - " c:\\users\\t.gross\\documents\\programming\\jugit\\fine\\FINE\\storage.py:1767: UserWarning:Charge and discharge at the same time for component Salt caverns (biogas)\n", - " c:\\users\\t.gross\\documents\\programming\\jugit\\fine\\FINE\\storage.py:1767: UserWarning:Charge and discharge at the same time for component Salt caverns (hydrogen)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "for StorageModel ... (3.7737sec)\n", - "for TransmissionModel ... (4.1164sec)\n", - "\t\t(10.9316 sec)\n", - "\n" - ] - } - ], - "source": [ - "# The `optimizationSpecs` only work with the Gurobi solver. If you are using another solver you need to choose\n", - "# specs spcecific to this solver or no specs.\n", - "esM.optimize(\n", - " timeSeriesAggregation=True,\n", - " optimizationSpecs=\"OptimalityTol=1e-3 method=2 cuts=0 MIPGap=5e-3\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 9. Selected results output\n", - "\n", - "Plot locations (GeoPandas required)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# Import the geopandas package for plotting the locations\n", - "import geopandas as gpd" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "locFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"clusteredRegions.shp\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = fn.plotLocations(locFilePath, plotLocNames=True, indexColumn=\"index\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sources and Sink\n", - "\n", - "Show optimization summary" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
    ComponentPropertyUnit
    Biogas purchaseTAC[1e9 Euro/a]0.2642230.2537510.1767490.2441620.3514530.1555940.1648730.0659848
    commodCosts[1e9 Euro/a]0.2642230.2537510.1767490.2441620.3514530.1555940.1648730.0659848
    operation[GW$_{biogas,LHV}$*h/a]4884.884691.283267.6745146497.552876.583048.131219.91
    [GW$_{biogas,LHV}$*h]4884.884691.283267.6745146497.552876.583048.131219.91
    Electricity demandoperation[GW$_{el}$*h/a]1339636611612479740441.277229.341413.428088.815851.1
    [GW$_{el}$*h]1339636611612479740441.277229.341413.428088.815851.1
    Existing run-of-river plantsTAC[1e9 Euro/a]0.1443160.00779090.00794990.009062890.556096000.0674723
    capacity[GW$_{el}$]0.6938280.03745630.03822070.04357162.67354NaNNaN0.324386
    operation[GW$_{el}$*h/a]3167.33170.988174.478198.90412204.7NaNNaN1480.82
    [GW$_{el}$*h]3167.33170.988174.478198.90412204.7NaNNaN1480.82
    opexCap[1e9 Euro/a]0.1443160.00779090.00794990.009062890.556096NaNNaN0.0674723
    Hydrogen demandoperation[GW$_{H_{2},LHV}$*h/a]15855.211007.116452.65264.1310078.35747.423710.051453.12
    [GW$_{H_{2},LHV}$*h]15855.211007.116452.65264.1310078.35747.423710.051453.12
    PVTAC[1e9 Euro/a]3.092681.909972.491091.44972.194011.469770.9337030.366282
    capacity[GW$_{el}$]41.854425.848433.712919.619429.692519.89112.63624.95704
    capexCap[1e9 Euro/a]2.548571.573942.052821.194651.808011.211180.7694330.30184
    invest[1e9 Euro]27.205416.801421.913412.752619.300112.92918.213523.22208
    operation[GW$_{el}$*h/a]3828826556.227269.819637.429797.620922.39413.695178.23
    [GW$_{el}$*h]3828826556.227269.819637.429797.620922.39413.695178.23
    opexCap[1e9 Euro/a]0.5441080.3360290.4382680.2550520.3860020.2585820.164270.0644416
    Wind (offshore)TAC[1e9 Euro/a]01.1210414.85381.68156005.044680
    capacity[GW$_{el}$]NaN4536NaNNaN18NaN
    capexCap[1e9 Euro/a]NaN0.9370412.41581.40556NaNNaN4.21668NaN
    invest[1e9 Euro]NaN9.2121.913.8NaNNaN41.4NaN
    operation[GW$_{el}$*h/a]NaN12672.821393721072.4NaNNaN70724.1NaN
    [GW$_{el}$*h]NaN12672.821393721072.4NaNNaN70724.1NaN
    opexCap[1e9 Euro/a]NaN0.1842.4380.276NaNNaN0.828NaN
    Wind (onshore)TAC[1e9 Euro/a]001.9821200.25776403.684780
    capacity[GW$_{el}$]0014.787801.92308027.49070
    capexCap[1e9 Euro/a]001.6567900.21545703.079980
    invest[1e9 Euro]0016.266602.11539030.23970
    operation[GW$_{el}$*h/a]003400602293.90707640
    [GW$_{el}$*h]003400602293.90707640
    opexCap[1e9 Euro/a]000.32533100.042307700.6047950
    \n", - "
    " - ], - "text/plain": [ - " cluster_0 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.264223 \n", - " commodCosts [1e9 Euro/a] 0.264223 \n", - " operation [GW$_{biogas,LHV}$*h/a] 4884.88 \n", - " [GW$_{biogas,LHV}$*h] 4884.88 \n", - "Electricity demand operation [GW$_{el}$*h/a] 133963 \n", - " [GW$_{el}$*h] 133963 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.144316 \n", - " capacity [GW$_{el}$] 0.693828 \n", - " operation [GW$_{el}$*h/a] 3167.33 \n", - " [GW$_{el}$*h] 3167.33 \n", - " opexCap [1e9 Euro/a] 0.144316 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 15855.2 \n", - " [GW$_{H_{2},LHV}$*h] 15855.2 \n", - "PV TAC [1e9 Euro/a] 3.09268 \n", - " capacity [GW$_{el}$] 41.8544 \n", - " capexCap [1e9 Euro/a] 2.54857 \n", - " invest [1e9 Euro] 27.2054 \n", - " operation [GW$_{el}$*h/a] 38288 \n", - " [GW$_{el}$*h] 38288 \n", - " opexCap [1e9 Euro/a] 0.544108 \n", - "Wind (offshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Wind (onshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operation [GW$_{el}$*h/a] 0 \n", - " [GW$_{el}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 \n", - "\n", - " cluster_1 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.253751 \n", - " commodCosts [1e9 Euro/a] 0.253751 \n", - " operation [GW$_{biogas,LHV}$*h/a] 4691.28 \n", - " [GW$_{biogas,LHV}$*h] 4691.28 \n", - "Electricity demand operation [GW$_{el}$*h/a] 66116 \n", - " [GW$_{el}$*h] 66116 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.0077909 \n", - " capacity [GW$_{el}$] 0.0374563 \n", - " operation [GW$_{el}$*h/a] 170.988 \n", - " [GW$_{el}$*h] 170.988 \n", - " opexCap [1e9 Euro/a] 0.0077909 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 11007.1 \n", - " [GW$_{H_{2},LHV}$*h] 11007.1 \n", - "PV TAC [1e9 Euro/a] 1.90997 \n", - " capacity [GW$_{el}$] 25.8484 \n", - " capexCap [1e9 Euro/a] 1.57394 \n", - " invest [1e9 Euro] 16.8014 \n", - " operation [GW$_{el}$*h/a] 26556.2 \n", - " [GW$_{el}$*h] 26556.2 \n", - " opexCap [1e9 Euro/a] 0.336029 \n", - "Wind (offshore) TAC [1e9 Euro/a] 1.12104 \n", - " capacity [GW$_{el}$] 4 \n", - " capexCap [1e9 Euro/a] 0.93704 \n", - " invest [1e9 Euro] 9.2 \n", - " operation [GW$_{el}$*h/a] 12672.8 \n", - " [GW$_{el}$*h] 12672.8 \n", - " opexCap [1e9 Euro/a] 0.184 \n", - "Wind (onshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operation [GW$_{el}$*h/a] 0 \n", - " [GW$_{el}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 \n", - "\n", - " cluster_2 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.176749 \n", - " commodCosts [1e9 Euro/a] 0.176749 \n", - " operation [GW$_{biogas,LHV}$*h/a] 3267.67 \n", - " [GW$_{biogas,LHV}$*h] 3267.67 \n", - "Electricity demand operation [GW$_{el}$*h/a] 124797 \n", - " [GW$_{el}$*h] 124797 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.0079499 \n", - " capacity [GW$_{el}$] 0.0382207 \n", - " operation [GW$_{el}$*h/a] 174.478 \n", - " [GW$_{el}$*h] 174.478 \n", - " opexCap [1e9 Euro/a] 0.0079499 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 16452.6 \n", - " [GW$_{H_{2},LHV}$*h] 16452.6 \n", - "PV TAC [1e9 Euro/a] 2.49109 \n", - " capacity [GW$_{el}$] 33.7129 \n", - " capexCap [1e9 Euro/a] 2.05282 \n", - " invest [1e9 Euro] 21.9134 \n", - " operation [GW$_{el}$*h/a] 27269.8 \n", - " [GW$_{el}$*h] 27269.8 \n", - " opexCap [1e9 Euro/a] 0.438268 \n", - "Wind (offshore) TAC [1e9 Euro/a] 14.8538 \n", - " capacity [GW$_{el}$] 53 \n", - " capexCap [1e9 Euro/a] 12.4158 \n", - " invest [1e9 Euro] 121.9 \n", - " operation [GW$_{el}$*h/a] 213937 \n", - " [GW$_{el}$*h] 213937 \n", - " opexCap [1e9 Euro/a] 2.438 \n", - "Wind (onshore) TAC [1e9 Euro/a] 1.98212 \n", - " capacity [GW$_{el}$] 14.7878 \n", - " capexCap [1e9 Euro/a] 1.65679 \n", - " invest [1e9 Euro] 16.2666 \n", - " operation [GW$_{el}$*h/a] 34006 \n", - " [GW$_{el}$*h] 34006 \n", - " opexCap [1e9 Euro/a] 0.325331 \n", - "\n", - " cluster_3 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.244162 \n", - " commodCosts [1e9 Euro/a] 0.244162 \n", - " operation [GW$_{biogas,LHV}$*h/a] 4514 \n", - " [GW$_{biogas,LHV}$*h] 4514 \n", - "Electricity demand operation [GW$_{el}$*h/a] 40441.2 \n", - " [GW$_{el}$*h] 40441.2 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.00906289 \n", - " capacity [GW$_{el}$] 0.0435716 \n", - " operation [GW$_{el}$*h/a] 198.904 \n", - " [GW$_{el}$*h] 198.904 \n", - " opexCap [1e9 Euro/a] 0.00906289 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 5264.13 \n", - " [GW$_{H_{2},LHV}$*h] 5264.13 \n", - "PV TAC [1e9 Euro/a] 1.4497 \n", - " capacity [GW$_{el}$] 19.6194 \n", - " capexCap [1e9 Euro/a] 1.19465 \n", - " invest [1e9 Euro] 12.7526 \n", - " operation [GW$_{el}$*h/a] 19637.4 \n", - " [GW$_{el}$*h] 19637.4 \n", - " opexCap [1e9 Euro/a] 0.255052 \n", - "Wind (offshore) TAC [1e9 Euro/a] 1.68156 \n", - " capacity [GW$_{el}$] 6 \n", - " capexCap [1e9 Euro/a] 1.40556 \n", - " invest [1e9 Euro] 13.8 \n", - " operation [GW$_{el}$*h/a] 21072.4 \n", - " [GW$_{el}$*h] 21072.4 \n", - " opexCap [1e9 Euro/a] 0.276 \n", - "Wind (onshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operation [GW$_{el}$*h/a] 0 \n", - " [GW$_{el}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 \n", - "\n", - " cluster_4 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.351453 \n", - " commodCosts [1e9 Euro/a] 0.351453 \n", - " operation [GW$_{biogas,LHV}$*h/a] 6497.55 \n", - " [GW$_{biogas,LHV}$*h] 6497.55 \n", - "Electricity demand operation [GW$_{el}$*h/a] 77229.3 \n", - " [GW$_{el}$*h] 77229.3 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.556096 \n", - " capacity [GW$_{el}$] 2.67354 \n", - " operation [GW$_{el}$*h/a] 12204.7 \n", - " [GW$_{el}$*h] 12204.7 \n", - " opexCap [1e9 Euro/a] 0.556096 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 10078.3 \n", - " [GW$_{H_{2},LHV}$*h] 10078.3 \n", - "PV TAC [1e9 Euro/a] 2.19401 \n", - " capacity [GW$_{el}$] 29.6925 \n", - " capexCap [1e9 Euro/a] 1.80801 \n", - " invest [1e9 Euro] 19.3001 \n", - " operation [GW$_{el}$*h/a] 29797.6 \n", - " [GW$_{el}$*h] 29797.6 \n", - " opexCap [1e9 Euro/a] 0.386002 \n", - "Wind (offshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Wind (onshore) TAC [1e9 Euro/a] 0.257764 \n", - " capacity [GW$_{el}$] 1.92308 \n", - " capexCap [1e9 Euro/a] 0.215457 \n", - " invest [1e9 Euro] 2.11539 \n", - " operation [GW$_{el}$*h/a] 2293.9 \n", - " [GW$_{el}$*h] 2293.9 \n", - " opexCap [1e9 Euro/a] 0.0423077 \n", - "\n", - " cluster_5 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.155594 \n", - " commodCosts [1e9 Euro/a] 0.155594 \n", - " operation [GW$_{biogas,LHV}$*h/a] 2876.58 \n", - " [GW$_{biogas,LHV}$*h] 2876.58 \n", - "Electricity demand operation [GW$_{el}$*h/a] 41413.4 \n", - " [GW$_{el}$*h] 41413.4 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 5747.42 \n", - " [GW$_{H_{2},LHV}$*h] 5747.42 \n", - "PV TAC [1e9 Euro/a] 1.46977 \n", - " capacity [GW$_{el}$] 19.891 \n", - " capexCap [1e9 Euro/a] 1.21118 \n", - " invest [1e9 Euro] 12.9291 \n", - " operation [GW$_{el}$*h/a] 20922.3 \n", - " [GW$_{el}$*h] 20922.3 \n", - " opexCap [1e9 Euro/a] 0.258582 \n", - "Wind (offshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Wind (onshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operation [GW$_{el}$*h/a] 0 \n", - " [GW$_{el}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 \n", - "\n", - " cluster_6 \\\n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.164873 \n", - " commodCosts [1e9 Euro/a] 0.164873 \n", - " operation [GW$_{biogas,LHV}$*h/a] 3048.13 \n", - " [GW$_{biogas,LHV}$*h] 3048.13 \n", - "Electricity demand operation [GW$_{el}$*h/a] 28088.8 \n", - " [GW$_{el}$*h] 28088.8 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 3710.05 \n", - " [GW$_{H_{2},LHV}$*h] 3710.05 \n", - "PV TAC [1e9 Euro/a] 0.933703 \n", - " capacity [GW$_{el}$] 12.6362 \n", - " capexCap [1e9 Euro/a] 0.769433 \n", - " invest [1e9 Euro] 8.21352 \n", - " operation [GW$_{el}$*h/a] 9413.69 \n", - " [GW$_{el}$*h] 9413.69 \n", - " opexCap [1e9 Euro/a] 0.16427 \n", - "Wind (offshore) TAC [1e9 Euro/a] 5.04468 \n", - " capacity [GW$_{el}$] 18 \n", - " capexCap [1e9 Euro/a] 4.21668 \n", - " invest [1e9 Euro] 41.4 \n", - " operation [GW$_{el}$*h/a] 70724.1 \n", - " [GW$_{el}$*h] 70724.1 \n", - " opexCap [1e9 Euro/a] 0.828 \n", - "Wind (onshore) TAC [1e9 Euro/a] 3.68478 \n", - " capacity [GW$_{el}$] 27.4907 \n", - " capexCap [1e9 Euro/a] 3.07998 \n", - " invest [1e9 Euro] 30.2397 \n", - " operation [GW$_{el}$*h/a] 70764 \n", - " [GW$_{el}$*h] 70764 \n", - " opexCap [1e9 Euro/a] 0.604795 \n", - "\n", - " cluster_7 \n", - "Component Property Unit \n", - "Biogas purchase TAC [1e9 Euro/a] 0.0659848 \n", - " commodCosts [1e9 Euro/a] 0.0659848 \n", - " operation [GW$_{biogas,LHV}$*h/a] 1219.91 \n", - " [GW$_{biogas,LHV}$*h] 1219.91 \n", - "Electricity demand operation [GW$_{el}$*h/a] 15851.1 \n", - " [GW$_{el}$*h] 15851.1 \n", - "Existing run-of-river plants TAC [1e9 Euro/a] 0.0674723 \n", - " capacity [GW$_{el}$] 0.324386 \n", - " operation [GW$_{el}$*h/a] 1480.82 \n", - " [GW$_{el}$*h] 1480.82 \n", - " opexCap [1e9 Euro/a] 0.0674723 \n", - "Hydrogen demand operation [GW$_{H_{2},LHV}$*h/a] 1453.12 \n", - " [GW$_{H_{2},LHV}$*h] 1453.12 \n", - "PV TAC [1e9 Euro/a] 0.366282 \n", - " capacity [GW$_{el}$] 4.95704 \n", - " capexCap [1e9 Euro/a] 0.30184 \n", - " invest [1e9 Euro] 3.22208 \n", - " operation [GW$_{el}$*h/a] 5178.23 \n", - " [GW$_{el}$*h] 5178.23 \n", - " opexCap [1e9 Euro/a] 0.0644416 \n", - "Wind (offshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operation [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Wind (onshore) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operation [GW$_{el}$*h/a] 0 \n", - " [GW$_{el}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 " - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot installed capacities" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = fn.plotLocationalColorMap(esM, \"PV\", locFilePath, \"index\", perArea=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot operation time series (either one or two dimensional)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
    ComponentPropertyUnit
    ElectrolyzerTAC[1e9 Euro/a]0.01853940.05900470.5457230.128660.09950870.1782950.9977860
    capacity[GW$_{el}$]0.2130610.67816.271611.47861.143582.0490311.46690
    capexCap[1e9 Euro/a]0.01587620.05052850.4673280.1101780.08521390.1526830.854450
    invest[1e9 Euro]0.106530.339053.135810.7393020.5717921.024515.733430
    operation[GW$_{el}$*h/a]721.2112063.4625579.65993.923673.278210.6153140.60
    [GW$_{el}$*h]721.2112063.4625579.65993.923673.278210.6153140.60
    opexCap[1e9 Euro/a]0.002663260.008476260.07839520.01848260.01429480.02561280.1433360
    New CCGT plants (biogas)TAC[1e9 Euro/a]0.05747160.2317940.3144740.1360310.0764450.089064500.00717623
    capacity[GW$_{el}$]0.702622.83383.84461.663050.9345791.0888600.0877331
    capexCap[1e9 Euro/a]0.04271660.1722840.2337370.1011070.05681880.066198400.00533383
    invest[1e9 Euro]0.4918341.983662.691221.164140.6542050.76220200.0614132
    operation[GW$_{el}$*h/a]1266.824875.836876.012843.821694.371812.250160.903
    [GW$_{el}$*h]1266.824875.836876.012843.821694.371812.250160.903
    opexCap[1e9 Euro/a]0.0147550.05950980.08073660.03492410.01962620.02286600.0018424
    \n", - "
    " - ], - "text/plain": [ - " cluster_0 cluster_1 \\\n", - "Component Property Unit \n", - "Electrolyzer TAC [1e9 Euro/a] 0.0185394 0.0590047 \n", - " capacity [GW$_{el}$] 0.213061 0.6781 \n", - " capexCap [1e9 Euro/a] 0.0158762 0.0505285 \n", - " invest [1e9 Euro] 0.10653 0.33905 \n", - " operation [GW$_{el}$*h/a] 721.211 2063.46 \n", - " [GW$_{el}$*h] 721.211 2063.46 \n", - " opexCap [1e9 Euro/a] 0.00266326 0.00847626 \n", - "New CCGT plants (biogas) TAC [1e9 Euro/a] 0.0574716 0.231794 \n", - " capacity [GW$_{el}$] 0.70262 2.8338 \n", - " capexCap [1e9 Euro/a] 0.0427166 0.172284 \n", - " invest [1e9 Euro] 0.491834 1.98366 \n", - " operation [GW$_{el}$*h/a] 1266.82 4875.83 \n", - " [GW$_{el}$*h] 1266.82 4875.83 \n", - " opexCap [1e9 Euro/a] 0.014755 0.0595098 \n", - "\n", - " cluster_2 cluster_3 \\\n", - "Component Property Unit \n", - "Electrolyzer TAC [1e9 Euro/a] 0.545723 0.12866 \n", - " capacity [GW$_{el}$] 6.27161 1.4786 \n", - " capexCap [1e9 Euro/a] 0.467328 0.110178 \n", - " invest [1e9 Euro] 3.13581 0.739302 \n", - " operation [GW$_{el}$*h/a] 25579.6 5993.92 \n", - " [GW$_{el}$*h] 25579.6 5993.92 \n", - " opexCap [1e9 Euro/a] 0.0783952 0.0184826 \n", - "New CCGT plants (biogas) TAC [1e9 Euro/a] 0.314474 0.136031 \n", - " capacity [GW$_{el}$] 3.8446 1.66305 \n", - " capexCap [1e9 Euro/a] 0.233737 0.101107 \n", - " invest [1e9 Euro] 2.69122 1.16414 \n", - " operation [GW$_{el}$*h/a] 6876.01 2843.82 \n", - " [GW$_{el}$*h] 6876.01 2843.82 \n", - " opexCap [1e9 Euro/a] 0.0807366 0.0349241 \n", - "\n", - " cluster_4 cluster_5 \\\n", - "Component Property Unit \n", - "Electrolyzer TAC [1e9 Euro/a] 0.0995087 0.178295 \n", - " capacity [GW$_{el}$] 1.14358 2.04903 \n", - " capexCap [1e9 Euro/a] 0.0852139 0.152683 \n", - " invest [1e9 Euro] 0.571792 1.02451 \n", - " operation [GW$_{el}$*h/a] 3673.27 8210.61 \n", - " [GW$_{el}$*h] 3673.27 8210.61 \n", - " opexCap [1e9 Euro/a] 0.0142948 0.0256128 \n", - "New CCGT plants (biogas) TAC [1e9 Euro/a] 0.076445 0.0890645 \n", - " capacity [GW$_{el}$] 0.934579 1.08886 \n", - " capexCap [1e9 Euro/a] 0.0568188 0.0661984 \n", - " invest [1e9 Euro] 0.654205 0.762202 \n", - " operation [GW$_{el}$*h/a] 1694.37 1812.25 \n", - " [GW$_{el}$*h] 1694.37 1812.25 \n", - " opexCap [1e9 Euro/a] 0.0196262 0.022866 \n", - "\n", - " cluster_6 cluster_7 \n", - "Component Property Unit \n", - "Electrolyzer TAC [1e9 Euro/a] 0.997786 0 \n", - " capacity [GW$_{el}$] 11.4669 0 \n", - " capexCap [1e9 Euro/a] 0.85445 0 \n", - " invest [1e9 Euro] 5.73343 0 \n", - " operation [GW$_{el}$*h/a] 53140.6 0 \n", - " [GW$_{el}$*h] 53140.6 0 \n", - " opexCap [1e9 Euro/a] 0.143336 0 \n", - "New CCGT plants (biogas) TAC [1e9 Euro/a] 0 0.00717623 \n", - " capacity [GW$_{el}$] 0 0.0877331 \n", - " capexCap [1e9 Euro/a] 0 0.00533383 \n", - " invest [1e9 Euro] 0 0.0614132 \n", - " operation [GW$_{el}$*h/a] 0 160.903 \n", - " [GW$_{el}$*h] 0 160.903 \n", - " opexCap [1e9 Euro/a] 0 0.0018424 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "scrolled": true, - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (biogas)\", \"cluster_2\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Storage\n", - "\n", - "Show optimization summary" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
    ComponentPropertyUnit
    Li-ion batteriesTAC[1e9 Euro/a]0.1856870.4052390.8311210.1468110.1851130.1728640.1198260.128359
    capacity[GW$_{el}$*h]11.050924.117349.46318.7372411.016810.28787.131297.6391
    capexCap[1e9 Euro/a]0.1635850.3570040.7321950.1293360.163080.1522880.1055630.11308
    invest[1e9 Euro]1.668693.641717.468941.319321.663531.553451.076821.1535
    operationCharge[GW$_{el}$*h/a]3510.498949.3716294.92727.43795.014027.272810.622967.37
    [GW$_{el}$*h]3510.498949.3716294.92727.43795.014027.272810.622967.37
    operationDischarge[GW$_{el}$*h/a]3166.858073.31147002460.563423.813633.462535.612677.15
    [GW$_{el}$*h]3166.858073.31147002460.563423.813633.462535.612677.15
    opexCap[1e9 Euro/a]0.02210190.04823460.09892630.01747450.02203350.02057550.01426260.0152782
    Pumped hydro storageTAC[1e9 Euro/a]0.002436370.00118590.0001251540.003001860.001100070.00383029.18e-050
    capacity[GW$_{el}$*h]15.9247.7510.81819.627.1925.0340.6NaN
    operationCharge[GW$_{el}$*h/a]7125.223930.38414.1158408.453204.4610248.2292.785NaN
    [GW$_{el}$*h]7125.223930.38414.1158408.453204.4610248.2292.785NaN
    operationDischarge[GW$_{el}$*h/a]5517.63043.59320.6816511.292481.467935.94226.719NaN
    [GW$_{el}$*h]5517.63043.59320.6816511.292481.467935.94226.719NaN
    opexCap[1e9 Euro/a]0.002436370.00118590.0001251540.003001860.001100070.00383029.18e-05NaN
    Salt caverns (biogas)TAC[1e9 Euro/a]00.02898660.05635190.017082400.011235700
    capacity[GW$_{biogas,LHV}$*h]NaN2138.754157.861260.41NaN829.0150NaN
    capexCap[1e9 Euro/a]NaN0.007599170.01477330.00447834NaN0.002945570NaN
    invest[1e9 Euro]NaN0.08554990.1663150.0504162NaN0.03316060NaN
    operationCharge[GW$_{biogas,LHV}$*h/a]NaN6163.8512463.319806.1NaN2315.060NaN
    [GW$_{biogas,LHV}$*h]NaN6163.8512463.319806.1NaN2315.060NaN
    operationDischarge[GW$_{biogas,LHV}$*h/a]NaN6163.8512463.319806.1NaN2315.060NaN
    [GW$_{biogas,LHV}$*h]NaN6163.8512463.319806.1NaN2315.060NaN
    opexCap[1e9 Euro/a]NaN0.02138750.04157860.0126041NaN0.008290150NaN
    Salt caverns (hydrogen)TAC[1e9 Euro/a]00.6206150.6397970.17705300.2122260.6326080
    capacity[GW$_{H_{2},LHV}$*h]NaN1070.451103.53305.384NaN366.0511091.13NaN
    capexCap[1e9 Euro/a]NaN0.01045940.01078270.00298391NaN0.003576690.0106615NaN
    invest[1e9 Euro]NaN0.1177490.1213890.0335922NaN0.04026560.120025NaN
    operationCharge[GW$_{H_{2},LHV}$*h/a]NaN8214.379211.493210.36NaN3039.711360.9NaN
    [GW$_{H_{2},LHV}$*h]NaN8214.379211.493210.36NaN3039.711360.9NaN
    operationDischarge[GW$_{H_{2},LHV}$*h/a]NaN8214.379211.493210.36NaN3039.711360.9NaN
    [GW$_{H_{2},LHV}$*h]NaN8214.379211.493210.36NaN3039.711360.9NaN
    opexCap[1e9 Euro/a]NaN0.6101560.6290150.174069NaN0.2086490.621947NaN
    \n", - "
    " - ], - "text/plain": [ - " cluster_0 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.185687 \n", - " capacity [GW$_{el}$*h] 11.0509 \n", - " capexCap [1e9 Euro/a] 0.163585 \n", - " invest [1e9 Euro] 1.66869 \n", - " operationCharge [GW$_{el}$*h/a] 3510.49 \n", - " [GW$_{el}$*h] 3510.49 \n", - " operationDischarge [GW$_{el}$*h/a] 3166.85 \n", - " [GW$_{el}$*h] 3166.85 \n", - " opexCap [1e9 Euro/a] 0.0221019 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.00243637 \n", - " capacity [GW$_{el}$*h] 15.924 \n", - " operationCharge [GW$_{el}$*h/a] 7125.22 \n", - " [GW$_{el}$*h] 7125.22 \n", - " operationDischarge [GW$_{el}$*h/a] 5517.6 \n", - " [GW$_{el}$*h] 5517.6 \n", - " opexCap [1e9 Euro/a] 0.00243637 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{biogas,LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{H_{2},LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "\n", - " cluster_1 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.405239 \n", - " capacity [GW$_{el}$*h] 24.1173 \n", - " capexCap [1e9 Euro/a] 0.357004 \n", - " invest [1e9 Euro] 3.64171 \n", - " operationCharge [GW$_{el}$*h/a] 8949.37 \n", - " [GW$_{el}$*h] 8949.37 \n", - " operationDischarge [GW$_{el}$*h/a] 8073.31 \n", - " [GW$_{el}$*h] 8073.31 \n", - " opexCap [1e9 Euro/a] 0.0482346 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.0011859 \n", - " capacity [GW$_{el}$*h] 7.751 \n", - " operationCharge [GW$_{el}$*h/a] 3930.38 \n", - " [GW$_{el}$*h] 3930.38 \n", - " operationDischarge [GW$_{el}$*h/a] 3043.59 \n", - " [GW$_{el}$*h] 3043.59 \n", - " opexCap [1e9 Euro/a] 0.0011859 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0.0289866 \n", - " capacity [GW$_{biogas,LHV}$*h] 2138.75 \n", - " capexCap [1e9 Euro/a] 0.00759917 \n", - " invest [1e9 Euro] 0.0855499 \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] 6163.85 \n", - " [GW$_{biogas,LHV}$*h] 6163.85 \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] 6163.85 \n", - " [GW$_{biogas,LHV}$*h] 6163.85 \n", - " opexCap [1e9 Euro/a] 0.0213875 \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0.620615 \n", - " capacity [GW$_{H_{2},LHV}$*h] 1070.45 \n", - " capexCap [1e9 Euro/a] 0.0104594 \n", - " invest [1e9 Euro] 0.117749 \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 8214.37 \n", - " [GW$_{H_{2},LHV}$*h] 8214.37 \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 8214.37 \n", - " [GW$_{H_{2},LHV}$*h] 8214.37 \n", - " opexCap [1e9 Euro/a] 0.610156 \n", - "\n", - " cluster_2 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.831121 \n", - " capacity [GW$_{el}$*h] 49.4631 \n", - " capexCap [1e9 Euro/a] 0.732195 \n", - " invest [1e9 Euro] 7.46894 \n", - " operationCharge [GW$_{el}$*h/a] 16294.9 \n", - " [GW$_{el}$*h] 16294.9 \n", - " operationDischarge [GW$_{el}$*h/a] 14700 \n", - " [GW$_{el}$*h] 14700 \n", - " opexCap [1e9 Euro/a] 0.0989263 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.000125154 \n", - " capacity [GW$_{el}$*h] 0.818 \n", - " operationCharge [GW$_{el}$*h/a] 414.115 \n", - " [GW$_{el}$*h] 414.115 \n", - " operationDischarge [GW$_{el}$*h/a] 320.681 \n", - " [GW$_{el}$*h] 320.681 \n", - " opexCap [1e9 Euro/a] 0.000125154 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0.0563519 \n", - " capacity [GW$_{biogas,LHV}$*h] 4157.86 \n", - " capexCap [1e9 Euro/a] 0.0147733 \n", - " invest [1e9 Euro] 0.166315 \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] 12463.3 \n", - " [GW$_{biogas,LHV}$*h] 12463.3 \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] 12463.3 \n", - " [GW$_{biogas,LHV}$*h] 12463.3 \n", - " opexCap [1e9 Euro/a] 0.0415786 \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0.639797 \n", - " capacity [GW$_{H_{2},LHV}$*h] 1103.53 \n", - " capexCap [1e9 Euro/a] 0.0107827 \n", - " invest [1e9 Euro] 0.121389 \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 9211.49 \n", - " [GW$_{H_{2},LHV}$*h] 9211.49 \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 9211.49 \n", - " [GW$_{H_{2},LHV}$*h] 9211.49 \n", - " opexCap [1e9 Euro/a] 0.629015 \n", - "\n", - " cluster_3 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.146811 \n", - " capacity [GW$_{el}$*h] 8.73724 \n", - " capexCap [1e9 Euro/a] 0.129336 \n", - " invest [1e9 Euro] 1.31932 \n", - " operationCharge [GW$_{el}$*h/a] 2727.4 \n", - " [GW$_{el}$*h] 2727.4 \n", - " operationDischarge [GW$_{el}$*h/a] 2460.56 \n", - " [GW$_{el}$*h] 2460.56 \n", - " opexCap [1e9 Euro/a] 0.0174745 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.00300186 \n", - " capacity [GW$_{el}$*h] 19.62 \n", - " operationCharge [GW$_{el}$*h/a] 8408.45 \n", - " [GW$_{el}$*h] 8408.45 \n", - " operationDischarge [GW$_{el}$*h/a] 6511.29 \n", - " [GW$_{el}$*h] 6511.29 \n", - " opexCap [1e9 Euro/a] 0.00300186 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0.0170824 \n", - " capacity [GW$_{biogas,LHV}$*h] 1260.41 \n", - " capexCap [1e9 Euro/a] 0.00447834 \n", - " invest [1e9 Euro] 0.0504162 \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] 19806.1 \n", - " [GW$_{biogas,LHV}$*h] 19806.1 \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] 19806.1 \n", - " [GW$_{biogas,LHV}$*h] 19806.1 \n", - " opexCap [1e9 Euro/a] 0.0126041 \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0.177053 \n", - " capacity [GW$_{H_{2},LHV}$*h] 305.384 \n", - " capexCap [1e9 Euro/a] 0.00298391 \n", - " invest [1e9 Euro] 0.0335922 \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 3210.36 \n", - " [GW$_{H_{2},LHV}$*h] 3210.36 \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 3210.36 \n", - " [GW$_{H_{2},LHV}$*h] 3210.36 \n", - " opexCap [1e9 Euro/a] 0.174069 \n", - "\n", - " cluster_4 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.185113 \n", - " capacity [GW$_{el}$*h] 11.0168 \n", - " capexCap [1e9 Euro/a] 0.16308 \n", - " invest [1e9 Euro] 1.66353 \n", - " operationCharge [GW$_{el}$*h/a] 3795.01 \n", - " [GW$_{el}$*h] 3795.01 \n", - " operationDischarge [GW$_{el}$*h/a] 3423.81 \n", - " [GW$_{el}$*h] 3423.81 \n", - " opexCap [1e9 Euro/a] 0.0220335 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.00110007 \n", - " capacity [GW$_{el}$*h] 7.19 \n", - " operationCharge [GW$_{el}$*h/a] 3204.46 \n", - " [GW$_{el}$*h] 3204.46 \n", - " operationDischarge [GW$_{el}$*h/a] 2481.46 \n", - " [GW$_{el}$*h] 2481.46 \n", - " opexCap [1e9 Euro/a] 0.00110007 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{biogas,LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{H_{2},LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "\n", - " cluster_5 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.172864 \n", - " capacity [GW$_{el}$*h] 10.2878 \n", - " capexCap [1e9 Euro/a] 0.152288 \n", - " invest [1e9 Euro] 1.55345 \n", - " operationCharge [GW$_{el}$*h/a] 4027.27 \n", - " [GW$_{el}$*h] 4027.27 \n", - " operationDischarge [GW$_{el}$*h/a] 3633.46 \n", - " [GW$_{el}$*h] 3633.46 \n", - " opexCap [1e9 Euro/a] 0.0205755 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0.0038302 \n", - " capacity [GW$_{el}$*h] 25.034 \n", - " operationCharge [GW$_{el}$*h/a] 10248.2 \n", - " [GW$_{el}$*h] 10248.2 \n", - " operationDischarge [GW$_{el}$*h/a] 7935.94 \n", - " [GW$_{el}$*h] 7935.94 \n", - " opexCap [1e9 Euro/a] 0.0038302 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0.0112357 \n", - " capacity [GW$_{biogas,LHV}$*h] 829.015 \n", - " capexCap [1e9 Euro/a] 0.00294557 \n", - " invest [1e9 Euro] 0.0331606 \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] 2315.06 \n", - " [GW$_{biogas,LHV}$*h] 2315.06 \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] 2315.06 \n", - " [GW$_{biogas,LHV}$*h] 2315.06 \n", - " opexCap [1e9 Euro/a] 0.00829015 \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0.212226 \n", - " capacity [GW$_{H_{2},LHV}$*h] 366.051 \n", - " capexCap [1e9 Euro/a] 0.00357669 \n", - " invest [1e9 Euro] 0.0402656 \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 3039.7 \n", - " [GW$_{H_{2},LHV}$*h] 3039.7 \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 3039.7 \n", - " [GW$_{H_{2},LHV}$*h] 3039.7 \n", - " opexCap [1e9 Euro/a] 0.208649 \n", - "\n", - " cluster_6 \\\n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.119826 \n", - " capacity [GW$_{el}$*h] 7.13129 \n", - " capexCap [1e9 Euro/a] 0.105563 \n", - " invest [1e9 Euro] 1.07682 \n", - " operationCharge [GW$_{el}$*h/a] 2810.62 \n", - " [GW$_{el}$*h] 2810.62 \n", - " operationDischarge [GW$_{el}$*h/a] 2535.61 \n", - " [GW$_{el}$*h] 2535.61 \n", - " opexCap [1e9 Euro/a] 0.0142626 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 9.18e-05 \n", - " capacity [GW$_{el}$*h] 0.6 \n", - " operationCharge [GW$_{el}$*h/a] 292.785 \n", - " [GW$_{el}$*h] 292.785 \n", - " operationDischarge [GW$_{el}$*h/a] 226.719 \n", - " [GW$_{el}$*h] 226.719 \n", - " opexCap [1e9 Euro/a] 9.18e-05 \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{biogas,LHV}$*h] 0 \n", - " capexCap [1e9 Euro/a] 0 \n", - " invest [1e9 Euro] 0 \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] 0 \n", - " [GW$_{biogas,LHV}$*h] 0 \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] 0 \n", - " [GW$_{biogas,LHV}$*h] 0 \n", - " opexCap [1e9 Euro/a] 0 \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0.632608 \n", - " capacity [GW$_{H_{2},LHV}$*h] 1091.13 \n", - " capexCap [1e9 Euro/a] 0.0106615 \n", - " invest [1e9 Euro] 0.120025 \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] 11360.9 \n", - " [GW$_{H_{2},LHV}$*h] 11360.9 \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] 11360.9 \n", - " [GW$_{H_{2},LHV}$*h] 11360.9 \n", - " opexCap [1e9 Euro/a] 0.621947 \n", - "\n", - " cluster_7 \n", - "Component Property Unit \n", - "Li-ion batteries TAC [1e9 Euro/a] 0.128359 \n", - " capacity [GW$_{el}$*h] 7.6391 \n", - " capexCap [1e9 Euro/a] 0.11308 \n", - " invest [1e9 Euro] 1.1535 \n", - " operationCharge [GW$_{el}$*h/a] 2967.37 \n", - " [GW$_{el}$*h] 2967.37 \n", - " operationDischarge [GW$_{el}$*h/a] 2677.15 \n", - " [GW$_{el}$*h] 2677.15 \n", - " opexCap [1e9 Euro/a] 0.0152782 \n", - "Pumped hydro storage TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{el}$*h] NaN \n", - " operationCharge [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " operationDischarge [GW$_{el}$*h/a] NaN \n", - " [GW$_{el}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Salt caverns (biogas) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{biogas,LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " operationDischarge [GW$_{biogas,LHV}$*h/a] NaN \n", - " [GW$_{biogas,LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN \n", - "Salt caverns (hydrogen) TAC [1e9 Euro/a] 0 \n", - " capacity [GW$_{H_{2},LHV}$*h] NaN \n", - " capexCap [1e9 Euro/a] NaN \n", - " invest [1e9 Euro] NaN \n", - " operationCharge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " operationDischarge [GW$_{H_{2},LHV}$*h/a] NaN \n", - " [GW$_{H_{2},LHV}$*h] NaN \n", - " opexCap [1e9 Euro/a] NaN " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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ZSpspcVLi95r/8HxqXmV1+944KOdXHteZzLtJ26Fs2j4+3H2u7jWnqkxxv0qJN/V6UvG62u/rflW9lD7VqTrPhbjF/pevFULd8LgYp+vck3CdUm6n7k7M09cXve/WXHctN5XDpdXz3EadrrrS1JjUjVFZeb5i15nlOZi2qSNGNK88h84y7d0ROyVubG6r1gh1BpmnmNSbXKyWdEhp2wslrWklCwAAAADYAqS+g/UeSd8ys69LWiFpd0mvkvRnw0oMAAAAACZN0jtY7n6upEMl3SBpsaTlkg7Lt/dkZrub2Q/M7AYzu87M3ppvX2pm3zGzm/N/m3/2CAAAAADGROo7WHL3681suaSdJN3n7psbtPOkpNPc/Soz21bSlWb2HUknSfqeu3/YzE6XdLqkdzaICwAAAABjI+kdLDNbbGafkbRW2a3Z15rZOWa2XUp9d7/X3a/K/35c2Tthu0o6VtI5ebFzJP1us/QBAAAAYHykvoP1MUkLJf2apDsl7SnpQ/n2NzZp0Mz2kvQ8SZdJ2snd75WyRZiZ7VhR52RJJ0vS3KVLJElrD16bPfnzzjXiftt332Xm+oOf3fF4qm7B/asXSZKevX3nHXbKdevE4vaKX7RqyHcRDGMT61M5r5BLbDx/enDWl5Q+lcfkwFK8Yp/LZevi35/nMHe79PusdM3Po0+pLFPOs+jBRxdKkjY9slXjuk3M3W6DpOm+1o1zuU5MbC7Lz4W5Tel/ijBGdcdGUFWmLu9++5sqFj/EHfbxWmeQvtWN2SDaittWnF5jNOj+MaxxbEs5v1Hm26TtUDZlfoa1z5Vf25u02XS/CuWrziv9jkOTPJqM49qDN0qqPkcU2y1fK4TX+PBYmj6vlc8909cp03eGq+pTqBs7p4fz59T5JVKmrO58FfK7V+l3mtuvpk7Ir0rxvB/GJGxLuT4tX5eE81ax3fIcTMnHKjYe5XnqKJMwxkGv/tfFrZvbqjVCnbp56kfqAusYSfu4e7iavcnM/kjSrU0aM7NFkr4q6W3u/piZJdVz9zMlnSlJW+25uzdpEwAAAABmSurSbp2k8g3yl0lKvsG8mc1Xtrj6nLt/Ld98n5ntnD+/s6RVqfEAAAAAYNykLrD+Q9J3zOwUM3uFmZ0i6dvK31XqxbK3qj4l6QZ3/8fCUxdq+iOGb5R0QWI+AAAAADB2Uj8i+CFJKyX9oaRd8r//TtJZifVfLOlESb80s1/k294l6cOSvmxmb5Z0l6TXJsYDAAAAgLGTtMByd1e2mEpdUJXrXyqp6gtXL+0nJgAAAACMm8oFlpmdGH5I2MzeVFXO3ftadPVt3mbNXbJe2ywId71Z0DjENtvEvjrWPE5a3PT4c5dU1Z87UJ3qMv3kUuxnZ5/+ZOmVXWW/ss3zktuZntOgOv75OrI2biyfci5rrPt+Kd05VCuPUV3dkMs5Sw4NtXvGa2Iqvg7tUTI+T29b/UpJvfbh5kKfwtjE2g7C/JTLhNxidUN/6/pU12YvsfghbujbIPE72lpS3ZdymaBJ2fI+11beU+31ue/1Eyclfq/5D8+n5lVWt++Ng3J+5XGdybybtD1I2Zh+9rm6Y7tXfsX9KiWXcvkm54Emr/t1OTSpX3WOCHGL/SlfK1RdO9TFTRmPUDd2Tpe2qShTfQ4e5DqgeZ1tGtdvojyu0213t1suG8YqZTyKdctjXD9G9f2Px+2sG5v/JmuEqvwGuSaT6t/BOkHSufnfJ1aUCe9sAQAAAMCsV7nAcvdXSlM3qHizpLvc/cmZSgwAAAAAJk3Puwjm37/6paTNw08HAAAAACZX6m3afy5p/2EmAgAAAACTLvU27T+U9C0z+7SkFcq+eyVpBDe5AAAAAIAxlbrAerGk2yW9pLSdm1wAAAAAQC71d7B63xN7hr18z+WSpKtUfxtwSXr4kM5bTB6X1y0675CDk+rWicXtFX8UmvQpJox9uU87zFlYWTa4d93innGDuvihD9U3sO/Opxz//OXPrcyhLs8qdXVjY5MqZb6axK8rO0j/64S4KW1Xlamr2+9zqYYdv994g7Tddt7j0GYsfkqbkzaOTfRzPA3bTO3jg9Zve98Z5WtFWzlUnSPqzv/hXH5cxbVDLG7KOb5cN3ZOD3GOqylTFS+m7XNjk+uxctmUusdVzFesbtU1Umw8yvNUvP5NGeO6PIrq4tbNbZM1wrCkfgdLZraDmZ1oZu/IH+9iZrsNLzUAAAAAmCxJCywze4mkGyW9TtJ78837SfrEkPICAAAAgImT+g7WRyX9gbsfIyn8FtZlkg4ZRlIAAAAAMIlSF1h7ufv38r/DHQQ3KP0mGQAAAACwxUtdYF1vZi8vbXuZsh8gBgAAAAAo/R2o0yRdZGbfkLTAzD4p6bclHTu0zCrMn7tZO27/uN6yw48kSW8p3SHkmKXda74XH3ZLx+OjF97YVWafw1ZJkrads7a2bp1Y3F7xi257dIeKZ5ZU1tlx+8d71gllwtjE+lTOK+QSG8/DFtwlKa1PYZ6Cn67dI9pOrGxd/L887FuSpI/84mWVbffK5cfb711Zppxn0U/v2EtS99in1J2us6Tmucyqh7eVNN3XunFOEZvL8nNhblP6nyL0aXrsF1WWrSpTl3edfuulxq0+XvtTfSx3l0npW3e8JX1kNbl6jdGw9g+0Y5Tzk3IsVmmadyhfdV4Zt9e/Xue5Yrvla4VwjRQeS9PntfK5J1ynnHvnodHYRaFu7Jx+4p6XdbQ9XWZJV9nu81W3kN/Z2rejTp2Qd6xOyK9K8bwfxiRsS7k+Df0OeYfzVrHd8hwEYaxi41Gep+L1b3mM68aoV//jcTvrds+tKtcIMeXzapO5laTbKrYnvYPl7j+VdKCk65T97tXtkg5x958ltQ4AAAAAs0Dyd6jc/R4z+3tJyyQ94O7eqw4AAAAAzCapt2lfYmbnSlor6VeS1prZuWa2dKjZAQAAAMAESb3JxdmSFkh6nqRt83+3UvZxQQAAAACA0j8ieKSknd09fNvuBjM7SdLKoWQFAAAAABModYF1o6S9JN1Q2LZHvn0k9p2X3Wnsjlc9pWfZU7a7p7Sl+05m++ZlPvd456ceu+vWqb5DWlX8FHV93CmhTrlMrE9N8gpjn9KnUDb4aULcqcc18UMfPtIz2+r4dWXq8myjbmxOq+bylAH2naYG6X9K3EHLoH9hnzMNdjdKALNXk3NE+VohXCPtW7gGCee1ctxw3ju3QTsxp5TaDurOwXXxQn6hftV5OyZWp9c1ZvG8X74WSLs+jc/XKTVzUBbbXp6n2PVvyhj17kPzuZWarRHK+fUztzGpC6zvSbo4/x7WCkm7S3q9pHPN7E2hkLvzkUEAAAAAs1bqAutwSbfk/x6eb7tV0ovy/yTJVfGdLDM7S9KrJK1y9wPybe+X9D8l3Z8Xe5e7f7Nh/gAAAAAwNpIWWO5+5IDtfFrSv0j6TGn7P7n7PwwYGwAAAADGQupdBAfi7pdIemgm2gIAAACAUZmRBVaNU83sGjM7y8y2rypkZieb2RVmdsXGR9fMZH4AAAAAkCz1O1jD8AlJH1D23a0PKLsh3JtiBd39TElnStKi/Xd2SfrW2t53BqkSq3vMgg19x6uL22b8cRH6mdKnJvNULtv2mKXk0vZ+NWmG1YeUfabJfgUAmHn9nNPDa3rda/ywz72DXItMsjb6Pexrp0HjxsqMwxz2fAfLzOaY2VFm1mq27n6fu29y982S/l3SIW3GBwAAAICZ1nOBlS+ALnD3Vv+3spntXHj4aknXthkfAAAAAGZa6kcELzGzw9y9r98fNbMvSDpC0jIzu1vS+yQdYWYHKfuI4B2S3tJPbAAAAAAYF6kLrDsl/V8zu0DZDw17eMLd39ursrufENn8qcS2AQAAAGAipC6wFkj6z/zv3YaTCgAAAABMttQfGv6jYSfS1IoNO0iSbFP/dTssuHfAjCrithC/nz72UyfVVD9Lfbp54xORsvt1PN5mzvrecYOa+PvNX9Qzz3I+5VzqcqjLs5+65Vzanp/Y2Dcru1TSYP2vE+LePO/mmjL7VZTJcqvrY12fmoxNWvylCW231VZ7Zcv7XNt5pxh2m7F5qm5zeh4HyWsU49hEVX6jzHtY+3jb9VPqdpdZGi1XHa+6fPO2h1OnqOocMR13aVfZcC6vunaoi9skpyZl6s7BdfFCfuN2XValybhW9Tu2vRwvVqaN/vYzt8Vto5yn5Nu0m9mzJB0naSd3P9XMniFpK3e/pp1UAAAAAGCyJf3QsJm9VtIlknaV9IZ887aS/nFIeQEAAADAxElaYEk6Q9LR7n6KpPDm2dWSDhxKVgAAAAAwgVIXWDsqW1BJ03cQ9MLfAAAAADDrpS6wrpR0Ymnb8ZIubzcdAAAAAJhcqTe5+HNJF5vZmyUtNLNvS9pf0m8OLbMe/v7qoyVJ21/Xuf1jzzqqq+whB3w6WrfoiMM/ntW/5bjauik5xVTFL5o3d3N0e7mPHQ6Ib+6ok5f52C3Z2MT6VM4r5BLqFD38+DaSuvu013YPd5X9+d27dua17ZpoO1L3+NXF/+DuF2TxLl7Q1Wbw7t2Prc3lqUu676Y0tV+V8iyaavMPHk+uWx6b6JyW5jK0c/OLsjzr9p0Qv248Prb4qGguknTHo9tLmp7bpP6nyMcojM13d3t2ZdEwP+UyIbdY3iGX8lz3qpcqFj/EDfvuu1d0tz2IlHjhuGzSt6l97rrqMRtEyn6R0maj/asmfq/5D8/XlalTt++Ng3J+5XGdybybtB3K1r1eVcWN6Wufy1+3Ysdir74U96uyutfeqvNK7BxcjhcbhzB+KTk0Oeb+3uLnuRC32P/ytUI4D4TH0vR5rXzuiV+nxPsU6sbO6eHuhqHtUKbuHFx3LRfym6pfcQ1WFPKO1el1V8fieT+MSdiWcn1avi4J561iu+U5CMJYxcajPE/FMl1jXDNGvfofi1uuW57b4rba6+egdI3cZG7rpN6mfbmZPVPSqyRdpOzHhi9y9/G+Py0AAAAAzKDk27S7+xoz+7Gk2yWtZHEFAAAAAJ1Sb9O+h5n9SNIdkr4h6Q4zu9TM9hxmcgAAAAAwSVJvcnGOshtdLHH3HSVtL+ln+XYAAAAAgNI/Ivh8Sb/p7hslyd2fMLN3SnpwaJkBAAAAwIRJXWD9VNIhkn5c2PYCST9pPaNE825YmP/V+VNcDy5f1lX27F1fVFG3UOaZL4rWL9dNy6lbVfyinZ6zKrmtfoS2Y30q5xVyieX7lMcsi1Pq05pVT+0qO2/rUjuLO+9SU+xzefzq4p99Qvbcgofid16UpOu/vn9tLjq8+2uEIYdynkW75m0+2aBuyH3xkfdVxi0LfTv74d77Toi/9KFNlWXuqZmntTtmx1GY25T+pwhjFMbm+p/vX1k2zE+5TMgtlnfob3mue9VLFYsf4oZ9N9Z2P8K+URcvlKk75sply8J+1VbeQd2+F6S0mRInJX6v+Q/P15WpU7fvjYNyfuVxncm8m7Qdyta9XlXFjelnnwuvW7G6vfpS3K/K6l57q84r/b7u31NRr+51NMUTFee5ELfY//K1QjgPhMfSdP/K556U65RyO7Fzejh/Tl1fRMqU1V3LhfyW9oxSqJPnHasT8utVV5oek7prubLydUk4bxXbLc/BlHysYuNRnqeOMgljHPTqf13curmtWiPUqZunfqQusG6V9E0z+4ayOwjuLumVkj5vZmeEQu7+3pbyAgAAAICJk7rA2lrS1/K/d5S0XtL5khYoW2xJTZaJAAAAALAFSv0drD8adiIAAAAAMOlS7yIIAAAAAOiBBRYAAAAAtMTcJ+urU9tts4sfvv8fSxuzu948cNgOHc9bpDuPvarzziN7v2d9V5nbP7CVJGnxRYtq69aJxe0Vv2iHqx6Jbn/g+Usq6yy7snedUObBg7NtsT6V8wq5hDodz12W3Z2/3Kcln+6+qeTcZ3XeYenBQzvnq6PPGzvvZFQX/67zfk2StOO51Xe72/qCy2pz0fy53ZXyHMp5dsR9OLvb08LbH0uuG3Kf+9xnSYrPaXkuV++9WJK06sQ1kur3nRB/3bGHVpZZt/2cjrJFj5x0uKTpuU3pf4qpMcrHZtMNN1WWDfNTLhNyi+Ud+lue6171UsXiT41Vvu9uuuaGvuMXhX2jLl4oE47Lur6FskHY58JdBGNjNoi6fS9IaTMlTkr8XvMfnq8rU6du3xsH5fzK4zqTeTdpO5Ste72qihvTzz4XXrdix2KvvhT3q7K6196q80rsHFyOFxuHMH4pOTQ55hbe9HCWV+kcEeIW+1++VgjXSOGxNH1eK597wnXK3mdM31exaixC3dg5/fb3zutoO5SpPQdvrL6rYsjPrVSnRsg7VifkV6V43g9jEralXJ+Gfoe8w3mr2G55DqaE8YyMR9c8Fa9/S2NcN0a9+h+LW65bnttizuU1Qkz5GrnJ3ErSt6/+wJXu/oLydt7BAgAAAICWpN5FUGZ2lKQTJO0iaaWkL7r794aVGAAAAABMmqR3sMzsLyR9UdJDkr4h6UFlv4F1WmL9s8xslZldW9i21My+Y2Y35/9u30f+AAAAADA2Uj8ieJqko9z9ne7+cXc/XdJR+fYUn5Z0TGnb6ZK+5+77Sfpe/hgAAAAAJlaT72DdUnp8mxJ/XNjdL1H27lfRsZLOyf8+R9LvNsgFAAAAAMZO6new3i/pU2b2fkl3S9pd0nskvc/MphZp7p5+ezFpJ3e/N693r5ntWFXQzE6WdLIkbT1/u2zb+g3RsssuvqNr26NP37sz3vrHu8r48mV5/dtr69aJxe0Vv6PMsiXJbfUjjE2sT+W8Qi6x8dTW2R2Ayn16srtk1zyV4xX7XC5bF9+XbytJWnhbed0+rXzfm6748xdU5hvtd6i3w3Z5YUuuGxubXhbe9mjW3vKledzqfSfED3ViFj34aGUuUznnc5vU/xT5GFUdrx1FexzTsbxDf2P3fKqrlyoWP8Qd9vFaZ5C+1Y3ZIOr2vSClzZQ4KfF7jVFxHx+ncWxLOb/yuM5k3k3aDmXrXq+q4sb0tc+VXtvrypbj17121r32Vp1XUuLFxiGMX0oOTY65qvNciNuxvXStEK6RwuOs/O0dZbuvUx4ulO1ss6udyDk9XCtMtR0pU1Z3vgo53P/yvXrGSakT8quuO33eD2MStqVcn4Z+l89bxXbLczCVWz5WsfEoz1Px+jdljKfK9uh/Xdy6uU255ijrZ27rpC6wPpn/e4Kyd63CK8Dr8ucs3x657/Xg3P1MSWdK2W3ah9EGAAAAAAwqdYGV/jZOuvvMbOf83audJa0aQhsAAAAAMGOSFljufqck5R8HnPpo34AulPRGSR/O/72ghZgAAAAAMDKpt2lfYmafl7RO+c0uzOx3zOyDifW/IOknkp5hZneb2ZuVLayONrObJR2dPwYAAACAiZX6EcF/U/ZNwz0lXZ9v+4mkj0h6d6/K7n5CxVMvTWwfAAAAAMZe6gLrpZJ2cfeNZuaS5O731935b1h87TptuuYGzdtnr+jzT67s/vTiojt7f4Vs0Z3x+il1U1TF71D13PMPr6yy6ZobetYpl4n1qSuvmjzD2Cf1qUE75Tmtix+eG0Rs7EIOdX2am9/tqVw/pe5UncicluPN/bVnSupvnGPq6ofnmvQ/RehT1fGaot9+DzpePeO2HL/yWG5YprJszevIlqjX/A9r/0A7Rjk/TY6zsqZ5h/JV55WZyqOt+E9Gzunl83XxcdW5J1ynpIxHqBsru+jQF3U8TjkH152vpvtX3Wa17jrl/Krbmx6T8hilKJ+3iu2W5yCoG4+6HMpjXDdGvfofjVtRt/h8k2uO7vz2qtjeTOrvYD0qaVlxg5ntIYkzFAAAAADkUhdY/yHpq2Z2pKQ5Zna4sh8H/rehZQYAAAAAEyb1I4L/v7IbXPyrpPmSzlL2+1f/PKS8AAAAAGDipC6wdnL3j0r6aHGjmT1N0q9azgkAAAAAJlLqRwRvqth+fcV2AAAAAJh1UhdY1rXBbLGkze2mAwAAAACTq/Yjgma2QpJLWmBmd5We3kHSF4aVWKqn/ui+jsdPRsosvmtjzzhVZVLqphgkTrmPRbH+luuUy4xDnwaNH57zm+4Yag4xbbQZm9PyPIV2Fu+ybXLcmRiPUYx5lWHnMk59HVTY5zavWDmU+G2N1aSM+bjnWc5vlPk2aXtYZcetzTbjDppTk/q269Maxy+fywe5Jhu07SDlHFwn1B+0TpN+9jOOqbGGESdljIbdh7rr56CcXz9zG9PrO1ivV/bu1TclnVjY7pLuc/cbB2wfAAAAALYYtQssd/8vSTKzZe6+ZmZSAgAAAIDJlPodrFPM7CBJMrPDzOwuM7vNzNJ/ghkAAAAAtnCpC6z/Len2/O+/lfSPkj4k6Z+GkRQAAAAATKLU38Hazt0fNbNtJR0o6WXuvsnMPjLE3AAAAABgoqQusFbkHwd8jqRL8sXVYkmbhpdaexbc+UhymfJdQ1LqNslh0LuStCHWp37yGnaf6uJPPbd+3ZBar7Z5htoM7TQZ55nIbab6n2LYuYxTX9syrD61FXdSxnzc8yznN8p8m7Q9rLLj1mabcQfNqUn91I89FZWvOYqPq85rbV1ftHUNNyy98iv2vzwmg/QtZQ6axpnp+uM+t6kLrHdIOk/SBkmvybe9StLlw0gKAAAAACZR0gLL3b8paZfS5q/k/wEAAAAAlP4OlszsWZKOk7STu58qaV9JT5F0zZByAwAAAICJkvRxWjN7raRLJO0q6Q355kXK7iYIAAAAAFD69xXPkHS0u5+i6RtbXK3sjoIAAAAAAKV/RHBHZQsqSfLCvx4vPl6eXH5zx+N5++zVs0yv7TGxuP3EGba2chl2n+rij9N4Dtts6isAYMsSzmHhGinlnNb2dUrd9dkoNelnuWxK3ap+j8v49soj5bp6XOc29R2sKyWdWNp2vLiLIAAAAABMSX0H688lXWxmb5a00My+LWl/Sb85aAJmdoekx5V99PBJd3/BoDEBAAAAYBRSb9O+3Myeqey3ry6StELSRe7+REt5HOnuD7QUCwAAAABGIvk27e6+RtKXh5gLAAAAAEy0pAWWme0h6X2Snqfs9uxT3H3/AXNwZR8/dEmfdPczI+2fLOlkSdpa2wzYHAAAAAAMR+o7WF+RtFzSeyWtbTmHF7v7SjPbUdJ3zGy5u19SLJAvus6UpMW2NLtz4br12ZPbLGjeYqjbtmHFHTcV/Zyz1dbJZZvEjcXfvH5dz3Bd+aTkMsgc1tQNuaTk3Y8m8WPzNFVvyMdGdB/pUSbkVpd30+dSxWKUx3iQ+L3aqiqT0navfaGtvFPbS21zkGMkNk9VbRbb6Wcs2ti/hqmc37D22ya5pLTd5j7eq36vOLWvlRVl6nJKiddEP+eVgXPocf7viFUuW3d+GdK5t6/4W9K1XBv9Hva106BxY2UGWSO0JHWB9UxJh7v75rYTcPeV+b+rzOx8SYco+1FjAAAAAJgoqbdp/7qkl7TduJktNLNtw9/K7kp4bdvtAAAAAMBMaHKb9v82s1sl3Vd8wt3fNED7O0k638xCLp93928NEA8AAAAARiZ1gXW2st+pukEtfgfL3W+TdGBb8QAAAABglFIXWEdJ2sXdHx9mMgAAAAAwyVIXWNdI2kHS2CywNj/4sCRpTh93CAl12zasuOOmST+HVbYfKfEHyWFLmP9RHhtbwvgBwJZskHN6Xd1hn3vH6VpkJrXR71Gev/tte5A1QltSF1jfV/ZbVWer+ztYZ7WeFQAAAABMoNQF1q9LukfZXf6KXBILLAAAAABQ4gLL3Y8cdiIAAAAAMOkqF1hmZu7u+d+Vv5c1jB8fBgAAAIBJVPcO1qOSFud/P6ns44BFlm+bO4S8AAAAAGDi1C2wnlP4e+9hJ9LU5vXrJElzHn2s77ptG1Zc9dHHvuokatLPYZXtR0r8QXJoVHeI8zOIYR8bc7baeqAyGMCY7nMAJscg5/S6usM+93aVqXk9TMplzK7LqrRxDdb3tVML/e237UHWCG3NU91H/1YUHr7W3e8s/yfpNa1kAQAAAABbgMoFVsl7K7a/u61EAAAAAGDS1d5F0MyOyv+ca2ZHKvveVbCPxuiHhwEAAABg1Hrdpv1T+b9bq/P3rlzSryT9r2EkBQAAAACTqHaB5e57S5KZfcbd3zAzKQEAAADAZEr9oeGxXVz52uHeeW4c9NPH2TAuk4z5wUxjnwOAzKCvh1yX9TYO/R3lPKXe5AIAAAAA0AMLLAAAAABoCQssAAAAAGgJCywAAAAAaAkLLAAAAABoibn7qHNoZLEt9UPtpaNOAwAAAMAs9l0/70p3f0F5O+9gAQAAAEBLWGABAAAAQEtGvsAys2PM7EYzu8XMTh91PgAAAADQr5EusMxsrqR/lfQKSc+WdIKZPXuUOQEAAABAv0b9DtYhkm5x99vcfYOkL0o6dsQ5AQAAAEBf5o24/V0lrSg8vlvSoeVCZnaypJPzh09818+7cQZyAwAAAIAqe8Y2jnqBZZFtXfeNd/czJZ05/HQAAAAAoH+j/ojg3ZJ2LzzeTdLKEeUCAAAAAAMZ9QLrZ5L2M7O9zewpko6XdOGIcwIAAACAvoz0I4Lu/qSZnSrp25LmSjrL3a8bZU4AAAAA0C9z7/rKEwAAAACgD6P+iCAAAAAAbDFYYAEAAABAS1hgAQAAAEBLWGABAAAAQEtYYAEAAABAS1hgAQAAAEBLWGABAAAAQEtYYAEARsLMrjOzI0adx0wyMzez1Wb2oVHnEmNmd5jZy2qe/76ZrTOzS2cyLwCYJCywAGDCmNmvm9l/m9mjZvaQmf3YzF6YP1d7gTxO3P057v7DUeYwovE60N3/upTH8WZ2Wb74WpX//aeW+Ssz+2ap/M0V245PTcLMTmq6wHX3oySd0qQOAMw2LLAAYIKY2WJJF0n6P5KWStpV0t9IWt9C7HmDxphN2hovMztN0j9L+ntJT5O0k7JFzIslPUXSJZJebGZz8/JPkzRf0sGlbU/Py/Zq7y1m9urph3Zy4TEAYEAssABgsuwvSe7+BXff5O5r3f1id7/GzM6VtIekr5vZE2b2l5JkZs8ysx+a2SP5x/J+JwTL38F5p5ldI2m1mc0zs9PN7FYze9zMri9efJvZwWb28/y5r5jZl8zsg4XndzGzr5rZ/WZ2u5n9eVVHyu8e5Y/fbmbX5O/OfcnMts7zOa9U95/N7GMpbdbEbWO83mFmXy2193/M7KM9ZzIru52kMyT9qbuf5+6Pe+bn7v46d18v6WfKFlQH5dX+h6QfSLqxtO1Wd1+Z0OxZkvaV9DZJ/5+kzZIuKDx/UHmsUvoCAMiwwAKAyXKTpE1mdo6ZvcLMtg9PuPuJku6S9Nvuvsjd/87M5kv6uqSLJe0o6X9J+pyZPaMQ8wRJvyVpibs/KelWSb8haTtl74591sx2NrOnSDpf0qeVvXv2BUnFxdecvK2rlb2z9lJJbzOzlzfo3+9LOkbS3pKeK+mkvJ1X5u/eKX/X5vclfb5Bm11x2xgvSZ+VdIyZLclzmyfpDySdm9jfwyVtpc4FTgd33yDpMmWLKOX//kjSpaVtPd+9KoYt/Lup8FiKzwEAIBELLACYIO7+mKRfV3ZB/O+S7jezC81sp4oqh0laJOnD7r7B3b+v7COGJxTKfMzdV7j72ryNr7j7Snff7O5fknSzpEPyWPPy8hvd/WuSLi/EeaGkp7r7GXlbt+U5Jn8vKI+90t0fUrbQOcjd75R0laTfzcscJWmNu/+0QZtdcdsYL3e/V9nC5rX5c8dIesDdr0zs77K8/JNhg2Xfr3vEzNaaWVhA/ZemF1O/oWyB9aPStv+qa8jM/snMXiDpTZJul/RRSX+t7GOIx5b6lzJWAIAIFlgAMGHc/QZ3P8ndd5N0gKRdlF0sx+wiaYW7by5su1PZuz3BimIFM3uDmf0iv8h/JG9jWR7rHnf3irp7Stol1MvrvkvZd4pS/arw9xplix1J+rymFzl/mD9u0mZV3LLG4yXpHEmvz/9+vdLfvZKkByUtK36fy91f5O5L8ufCefoSSb+ev2P5VHe/WdJ/S3pRvu0A9X4H69mSrnP3T+aL47w5/6S7/2ehXOpYAQAiWGABwARz9+XKPrJ3QNhUKrJS0u75R+mCPSTdUwwT/jCzPZW9A3SqpB3yC/1rJZmkeyXtamZWqLt74e8Vkm539yWF/7Z191f227+Cr0g6wsx2U/axxLDAGrTNgcYr95+SnmtmB0h6laTPJbYtST9RdoOSYxPKbSfpZEk/lqbezVyZb1vp7reHwmZ2omW3VL/CzI7MNy8I71Lm9T896rs4AsCWiAUWAEwQM3ummZ2WLzRkZrsre2fnp3mR+yTtU6hymaTVkv7SzOZbdlvu35b0xYomFipbQNyfx/8jTS/efqLs+zqn5jfDOFbZRweDyyU9lt8EYoGZzTWzAyy/hfwg3P1+ST+UdLayBdUNLbU56HjJ3ddJOk/Zou9yd7+rQb8eUfY9t4+b2XFmtsjM5pjZQcrmIpRbK+kKSX+h7KOBwaX5tql3r/KF3iuUfR/tKElvN7Md874CAIaMBRYATJbHJR0q6TIzW61sYXWtpNPy5/9W0rvzj8u9Pb9Bwu8ou+B+QNLHJb0hf+eri7tfL+kjyhZT90n6NU2/Y7JB0u9JerOkR5R9HO4i5beId/dNyhYjByn7js8Dkv5D2Tsvbfi8pJdp+t2rNtocaLwKzlE2Vk0+Hhj68HfKFkl/KWmVsnH/pKR3KvsYYPBfym68UfyR3x/l24ofD3y1so8D/kDShZIezXP7ZdPcAADNWedH6QEASGdml0n6N3c/e9S5jJKZ7SFpuaSn5R/dqyq3TtmC9GPu/p4h5fIRSee7+6X543nKPvJ5R+m7Vv3E/o6yG4Fc7u4vHTRXANgS8aOSAIBkZvYSZb+/9ICk1ym7jfe3RprUiOXf1/oLSV+sW1xJkrvPxG9KfVLSWWa2UdJGSW9Q9g7WhYMGdvejB40BAFs6FlgAgCaeIenLyu4sd6uk4/Jblc9KZrZQ2Uf67lR2i/aRc/eblN3Kv+jNo8gFAGYjPiI4yyyzp/kGbYg/md8YzKLPVT6Ixqh+PjVOP+UiG2vTSS0Xq9Kkn908FEhtNzl2Wp86jvrWcyiWs+72BopX/VS0jSH0rZV2+shroDlraRxq57GfNgao78PcbweqWxilEc1T7Uz18bIXK2i9j+qsXM9GvDqXnrl251DXXlXOdS/rsTqxJjpPWal1UvOvHqPKPjXui0e2FeMNUDe13R45DxQ7VreFPvXcF1Jjd+zr3RFS26mvG/tLuvKa9d9297H4n0NbEt7BmmU2aIMOtZdK+R2IbU7xqK7epo5t+UJszpyubYpuKxzUxTsfh+3WHTsaJ1ousi21jeK2SDmfamO6WG0uDerUlfOO2JE25kS2RcqFuh3x5kS2TZWL5NwRu/D8HOt8rkecgcrNiWzrq43pP2N9im+zpHJ17SXn1SuXQpXkOg1zbT1eQV3dZu15dXuJuRYvgKMLtWifvCZepFyDOBbJKxpbsbqp27rbCOUsNV4xrR5x5kxta15ujhLL1Wybo+5250TKddSJ5NCzXKS96XKbG5ebGy23uavduapuozNO9/NzY9uKcRTZlv89N5JXfFt9uyF2sZ+dOWzu6Eexfsd4RcpN1+0uF2uvI/9o3e55n2o3Mp/FbZ3PR7ZN5aJCHet4Lns+bLNIOYuUm64dnp9rxW3Tf8/d+eZlQuu4iyAAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0hAUWAAAAALSEBRYAAAAAtIQFFgAAAAC0xNx91DlgBpnZtZLWjToPjNwySQ+MOgmMBfYFBOwLkNgPZpsH3P2YUSexpZk36gQw49a5+wtGnQRGy8yuYD+AxL6AaewLkNgPgDbwEUEAAAAAaAkLLAAAAABoCQus2efMUSeAscB+gIB9AQH7AiT2A2Bg3OQCAAAAAFrCO1gAAAAA0BIWWAAAAADQEhZYAAAAANASFlizhJktNbPzzWy1md1pZn846pwwfGZ2qpldYWbrzezTpedeambLzWyNmf3AzPYcUZoYMjPbysw+lR/7j5vZz83sFYXn2RdmETP7rJnda2aPmdlNZvbHhefYF2YZM9vPzNaZ2WcL29gPgAGwwJo9/lXSBkk7SXqdpE+Y2XNGmxJmwEpJH5R0VnGjmS2T9DVJ75G0VNIVkr4049lhpsyTtELSSyRtp2zev2xme7EvzEp/K2kvd18s6XckfdDMns++MGv9q6SfhQfsB8DguIvgLGBmCyU9LOkAd78p33aupHvc/fSRJocZYWYflLSbu5+UPz5Z0knu/qL88UJJD0h6nrsvH1mimDFmdo2kv5G0g9gXZi0ze4akH0p6q6QlYl+YVczseEm/J+l6SU9399dzfgAGxztYs8P+kjaFxVXuakm8gzV7PUfZPiBJcvfVkm4V+8SsYGY7KXtduE7sC7OSmX3czNZIWi7pXknfFPvCrGJmiyWdIem00lPsB8CAWGDNDoskPVra9qikbUeQC8YD+8QsZWbzJX1O0jn5/41mX5iF3P1Plc3xbyj7ONh6sS/MNh+Q9Cl3X1Hazn4ADIgF1uzwhKTFpW2LJT0+glwwHtgnZiEzmyPpXGXfxzw138y+MEu5+yZ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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Salt caverns (biogas)\",\n", - " \"cluster_2\",\n", - " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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    cluster_0cluster_1cluster_2cluster_3cluster_4cluster_5cluster_6cluster_7
    ComponentPropertyUnitLocationIn
    AC cablescapacity[GW$_{el}$]cluster_0NaN711NaN184NaN2.5
    cluster_17NaN10NaNNaN36NaN
    cluster_21110NaNNaNNaNNaNNaN3
    cluster_3NaNNaNNaNNaNNaN122.4NaN
    cluster_418NaNNaNNaNNaN4NaNNaN
    ....................................
    Pipelines (hydrogen)operation[GW$_{H_{2},LHV}$*h]cluster_0NaN1526.810NaN7506.980NaN0
    cluster_124384.2NaN00NaN05744.77NaN
    cluster_200NaNNaNNaNNaNNaN1453.12
    cluster_3NaN0NaNNaNNaN0125.215NaN
    cluster_6NaN38164.8NaN1193.6NaNNaNNaNNaN
    \n", - "

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    " - ], - "text/plain": [ - " cluster_0 cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 \\\n", - "cluster_0 NaN 2.960445 1.299364 NaN 1.892217 0.0 \n", - "cluster_1 2.960445 NaN 0.000000 0.000000 NaN 0.0 \n", - "cluster_2 1.299364 0.000000 NaN NaN NaN NaN \n", - "cluster_3 NaN 0.000000 NaN NaN NaN 0.0 \n", - "cluster_4 1.892217 NaN NaN NaN NaN 0.0 \n", - "cluster_5 0.000000 0.000000 NaN 0.000000 0.000000 NaN \n", - "cluster_6 NaN 5.416355 NaN 0.215147 NaN NaN \n", - "cluster_7 0.000000 NaN 0.305141 NaN NaN NaN \n", - "\n", - " cluster_6 cluster_7 \n", - "cluster_0 NaN 0.000000 \n", - "cluster_1 5.416355 NaN \n", - "cluster_2 NaN 0.305141 \n", - "cluster_3 0.215147 NaN \n", - "cluster_4 NaN NaN \n", - "cluster_5 NaN NaN \n", - "cluster_6 NaN NaN \n", - "cluster_7 NaN NaN " - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = esM.componentModelingDict[\"TransmissionModel\"].capacityVariablesOptimum\n", - "df.loc[\"Pipelines (biogas)\"] + df.loc[\"Pipelines (hydrogen)\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot installed transmission capacities" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"AClines.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"AC cables\", transFilePath, loc0=\"bus0\", loc1=\"bus1\", fig=fig, ax=ax\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"DClines.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"DC cables\", transFilePath, loc0=\"cluster0\", loc1=\"cluster1\", fig=fig, ax=ax\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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    cluster_0NaN2.9604451.299364NaN1.8922170.0NaN0.000000
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    " - ], - "text/plain": [ - " cluster_0 cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 \\\n", - "cluster_0 NaN 2.960445 1.299364 NaN 1.892217 0.0 \n", - "cluster_1 2.960445 NaN 0.000000 0.000000 NaN 0.0 \n", - "cluster_2 1.299364 0.000000 NaN NaN NaN NaN \n", - "cluster_3 NaN 0.000000 NaN NaN NaN 0.0 \n", - "cluster_4 1.892217 NaN NaN NaN NaN 0.0 \n", - "cluster_5 0.000000 0.000000 NaN 0.000000 0.000000 NaN \n", - "cluster_6 NaN 5.416355 NaN 0.215147 NaN NaN \n", - "cluster_7 0.000000 NaN 0.305141 NaN NaN NaN \n", - "\n", - " cluster_6 cluster_7 \n", - "cluster_0 NaN 0.000000 \n", - "cluster_1 5.416355 NaN \n", - "cluster_2 NaN 0.305141 \n", - "cluster_3 0.215147 NaN \n", - "cluster_4 NaN NaN \n", - "cluster_5 NaN NaN \n", - "cluster_6 NaN NaN \n", - "cluster_7 NaN NaN " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = esM.componentModelingDict[\"TransmissionModel\"].capacityVariablesOptimum\n", - "df.loc[\"Pipelines (biogas)\"] + df.loc[\"Pipelines (hydrogen)\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot installed transmission capacities" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"AClines.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"AC cables\", transFilePath, loc0=\"bus0\", loc1=\"bus1\", fig=fig, ax=ax\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"DClines.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"DC cables\", transFilePath, loc0=\"cluster0\", loc1=\"cluster1\", fig=fig, ax=ax\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"transmissionPipeline.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"Pipelines (hydrogen)\", transFilePath, loc0=\"loc1\", loc1=\"loc2\", fig=fig, ax=ax\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "transFilePath = os.path.join(\n", - " cwd, \"InputData\", \"SpatialData\", \"ShapeFiles\", \"transmissionPipeline.shp\"\n", - ")\n", - "\n", - "fig, ax = fn.plotLocations(locFilePath, indexColumn=\"index\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM, \"Pipelines (biogas)\", transFilePath, loc0=\"loc1\", loc1=\"loc2\", fig=fig, ax=ax\n", - ")" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "interpreter": { - "hash": "8e874521e84aaa9a25c57584b09ac89843ebb84ca57251a2b3336b66b8670439" - }, - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.15" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/PerfectForesight/perfectForesight_example.ipynb b/examples/PerfectForesight/perfectForesight_example.ipynb deleted file mode 100644 index 0276c9a2..00000000 --- a/examples/PerfectForesight/perfectForesight_example.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"markdown","metadata":{},"source":[" # Workflow for a transformation pathway of a single node energy system with perfect foresight\n","\n"," In this application of the FINE framework, a transformation pathway of a energy system is modeled and optimized.\n","\n"," All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.\n","\n"," The workflow is structures as follows:\n"," 1. Required packages are imported and the input data path is set\n"," 2. An energy system model instance is created\n"," 3. Commodity sources are added to the energy system model\n"," 4. Commodity conversion components are added to the energy system model\n"," 5. Commodity storages are added to the energy system model\n"," 7. Commodity sinks are added to the energy system model\n"," 8. The energy system model is optimized\n"," 9. Selected optimization results are presented\n"]},{"cell_type":"markdown","metadata":{},"source":[" # 1. Import required packages and set input data path\n","\n"," The FINE framework is imported which provides the required classes and functions for modeling the energy system."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import fine as fn\n","from getData import getData\n","\n","import os\n","\n","cwd = os.getcwd()\n","data = getData()"]},{"cell_type":"markdown","metadata":{},"source":[" # 2. Create an energy system model instance\n","\n"," The structure of the energy system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n","\n"," The commodities are specified by a unit (i.e. 'GW_electric', 'GW_H2lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["locations = {\"GermanyRegion\"}\n","commodityUnitDict = {\"electricity\": r\"GW$_{el}$\", \"hydrogen\": r\"GW$_{H_{2},LHV}$\"}\n","commodities = {\"electricity\", \"hydrogen\"}\n","numberOfTimeSteps = 8760\n","hoursPerTimeStep = 1"]},{"cell_type":"markdown","metadata":{},"source":[" # 2.1 define Transformation Pathway parameters\n","\n"," Transformation Pathway Analyses can be run by setting a number of investment periods\n"," larger than 1, which is the default value and results in a single year optimization."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["numberOfInvestmentPeriods=3\n","startYear=2020\n","interval=5"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM = fn.EnergySystemModel(\n"," locations=locations,\n"," commodities=commodities,\n"," numberOfInvestmentPeriods=numberOfInvestmentPeriods,\n"," startYear=startYear, \n"," investmentPeriodInterval=interval,\n"," numberOfTimeSteps=8760,\n"," commodityUnitsDict=commodityUnitDict,\n"," hoursPerTimeStep=1,\n"," costUnit=\"1e9 Euro\",\n"," lengthUnit=\"km\",\n"," verboseLogLevel=0,\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 3. Add commodity sources to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 3.1. Electricity sources"]},{"cell_type":"markdown","metadata":{},"source":[" ### Wind onshore"]},{"cell_type":"markdown","metadata":{},"source":[" change weather conditions for the different investment periods"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["operationRateMax={}\n","operationRateMax[2020]=1.2*data[\"Wind (onshore), operationRateMax\"]\n","operationRateMax[2025]=0.7*data[\"Wind (onshore), operationRateMax\"]\n","operationRateMax[2030]=1*data[\"Wind (onshore), operationRateMax\"]"]},{"cell_type":"markdown","metadata":{},"source":[" define existing stock for wind onshore turbines"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["stockWindCommissioning={\n"," 2010:5,\n"," 2015:10,\n","}"]},{"cell_type":"markdown","metadata":{},"source":[" define invest and opex per capacity for wind onshore turbines"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["investPerCapacityWind={\n"," 2010:1.5, \n"," 2015:1.25, \n"," 2020:1.1, \n"," 2025:1, \n"," 2030:0.95\n"," }\n","\n","opexPerCapacityWind={\n"," 2010:1.5*0.02, \n"," 2015:1.25*0.02, \n"," 2020:1.1*0.02, \n"," 2025:1*0.02, \n"," 2030:0.95*0.02\n"," }"]},{"cell_type":"markdown","metadata":{},"source":[" add wind onshore source to esM"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Source(\n"," esM=esM,\n"," name=\"Wind (onshore)\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=True,\n"," operationRateMax=data[\"Wind (onshore), operationRateMax\"],\n"," capacityMax=data[\"Wind (onshore), capacityMax\"],\n"," investPerCapacity=investPerCapacityWind,\n"," opexPerCapacity=opexPerCapacityWind,\n"," interestRate=0.08,\n"," economicLifetime=20,\n"," stockCommissioning=stockWindCommissioning,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Full load hours:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data[\"Wind (onshore), operationRateMax\"].sum()"]},{"cell_type":"markdown","metadata":{},"source":[" # 4. Add conversion components to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ### New combined cycly gas turbines for hydrogen"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Conversion(\n"," esM=esM,\n"," name=\"New CCGT plants (hydrogen)\",\n"," physicalUnit=r\"GW$_{el}$\",\n"," commodityConversionFactors={\"electricity\": 1, \"hydrogen\": -1 / 0.6},\n"," hasCapacityVariable=True,\n"," investPerCapacity=0.7,\n"," opexPerCapacity={2020:0.021, 2025:0.018, 2030:0.025},\n"," interestRate=0.08,\n"," economicLifetime=30,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ### Electrolyzers"]},{"cell_type":"markdown","metadata":{},"source":[" add component with constant invest and opex per capacity"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Conversion(\n"," esM=esM,\n"," name=\"Electroylzers\",\n"," physicalUnit=r\"GW$_{el}$\",\n"," commodityConversionFactors={\"electricity\": -1, \"hydrogen\": 0.7},\n"," hasCapacityVariable=True,\n"," investPerCapacity=0.5,\n"," opexPerCapacity=0.5 * 0.025,\n"," interestRate=0.08,\n"," economicLifetime=10,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 5. Add commodity storages to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 5.1. Electricity storage"]},{"cell_type":"markdown","metadata":{},"source":[" ### Lithium ion batteries\n","\n"," The self discharge of a lithium ion battery is here described as 3% per month. The self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Storage(\n"," esM=esM,\n"," name=\"Li-ion batteries\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=True,\n"," chargeEfficiency=0.95,\n"," cyclicLifetime=10000,\n"," dischargeEfficiency=0.95,\n"," selfDischarge=1 - (1 - 0.03) ** (1 / (30 * 24)),\n"," chargeRate=1,\n"," dischargeRate=1,\n"," doPreciseTsaModeling=False,\n"," investPerCapacity=0.151,\n"," opexPerCapacity=0.002,\n"," interestRate=0.08,\n"," economicLifetime=20,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ## 5.2. Hydrogen storage"]},{"cell_type":"markdown","metadata":{},"source":[" ### Hydrogen filled salt caverns\n"," The maximum capacity is here obtained by: dividing the given capacity (which is given for methane) by the lower heating value of methane and then multiplying it with the lower heating value of hydrogen."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.add(\n"," fn.Storage(\n"," esM=esM,\n"," name=\"Salt caverns (hydrogen)\",\n"," commodity=\"hydrogen\",\n"," hasCapacityVariable=True,\n"," capacityVariableDomain=\"continuous\",\n"," capacityPerPlantUnit=133,\n"," chargeRate=1 / 470.37,\n"," dischargeRate=1 / 470.37,\n"," sharedPotentialID=\"Existing salt caverns\",\n"," stateOfChargeMin=0.33,\n"," stateOfChargeMax=1,\n"," capacityMax=data[\"Salt caverns (hydrogen), capacityMax\"],\n"," investPerCapacity={2020:0.00011,2025: 0.00009,2030:0.00009},\n"," opexPerCapacity=0.00057,\n"," interestRate=0.08,\n"," economicLifetime=30,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 7. Add commodity sinks to the energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" ## 7.1. Electricity sinks"]},{"cell_type":"markdown","metadata":{},"source":[" ### Electricity demand"]},{"cell_type":"markdown","metadata":{},"source":[" vary the demand with the years - increasing demand by 30% per year"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["electricityDemand={}\n","electricityDemand[2020]=(1+0*0.3)*data[\"Electricity demand, operationRateFix\"]\n","electricityDemand[2025]=(1+1*0.3)*data[\"Electricity demand, operationRateFix\"]\n","electricityDemand[2030]=(1+2*0.3)*data[\"Electricity demand, operationRateFix\"]\n","\n","esM.add(\n"," fn.Sink(\n"," esM=esM,\n"," name=\"Electricity demand\",\n"," commodity=\"electricity\",\n"," hasCapacityVariable=False,\n"," operationRateFix=electricityDemand,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" ## 7.2. Hydrogen sinks"]},{"cell_type":"markdown","metadata":{},"source":[" ### Fuel cell electric vehicle (FCEV) demand"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["FCEV_penetration = 0.5\n","\n","# vary the demand with the years - increasing demand by 25% per year\n","hydrogendDemand={}\n","hydrogendDemand[2020]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","hydrogendDemand[2025]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","hydrogendDemand[2030]=(1+0*0.25)*data[\"Hydrogen demand, operationRateFix\"] * FCEV_penetration\n","\n","\n","esM.add(\n"," fn.Sink(\n"," esM=esM,\n"," name=\"Hydrogen demand\",\n"," commodity=\"hydrogen\",\n"," hasCapacityVariable=False,\n"," operationRateFix=hydrogendDemand,\n"," )\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" # 8. Optimize energy system model"]},{"cell_type":"markdown","metadata":{},"source":[" All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.aggregateTemporally(numberOfTypicalPeriods=20)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")"]},{"cell_type":"markdown","metadata":{},"source":[" # 9. Selected results output"]},{"cell_type":"markdown","metadata":{},"source":[" ### Sources and Sink\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of SourceSinkModel for year {year}\")\n"," print(esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2, ip=year))"]},{"cell_type":"markdown","metadata":{},"source":[" Plot operation time series (either one or two dimensional) for different years"]},{"cell_type":"markdown","metadata":{},"source":[" Electricity demand operation for Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperation(esM, \"Electricity demand\", \"GermanyRegion\", ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Electricity demand operation for Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperation(esM, \"Electricity demand\", \"GermanyRegion\", ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Electricity demand in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"GermanyRegion\",ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Electricity demand in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"Electricity demand\", \"GermanyRegion\",ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" ### Conversion\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of ConversionMpdel for year {year}\")\n"," esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2, ip=year)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for New CCGT plants (hydrogen) in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (hydrogen)\", \"GermanyRegion\", ip=2020)"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for New CCGT plants (hydrogen) in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(esM, \"New CCGT plants (hydrogen)\", \"GermanyRegion\", ip=2030)"]},{"cell_type":"markdown","metadata":{},"source":[" ### Storage\n","\n"," Show optimization summary"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for year in [2020,2025,2030]:\n"," print(f\"\\n Results of StorageModel for year {year}\")\n"," print(esM.getOptimizationSummary(\"StorageModel\", outputLevel=2, ip=year))"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2020\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2025"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2025\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Li-ion batteries in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Li-ion batteries\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2030\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2020"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2020\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2025"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2025\n",")"]},{"cell_type":"markdown","metadata":{},"source":[" Operation color map for Salt caverns (hydrogen) in Investment Period 2030"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fig, ax = fn.plotOperationColorMap(\n"," esM,\n"," \"Salt caverns (hydrogen)\",\n"," \"GermanyRegion\",\n"," variableName=\"stateOfChargeOperationVariablesOptimum\",\n"," ip=2030\n",")"]}],"metadata":{"kernelspec":{"display_name":"FINE_dev","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":2} diff --git a/examples/Spatial_and_technology_aggregation/output_data/aggregated_regions.dbf b/examples/Spatial_and_technology_aggregation/output_data/aggregated_regions.dbf deleted file mode 100644 index f569fd32..00000000 Binary files a/examples/Spatial_and_technology_aggregation/output_data/aggregated_regions.dbf and /dev/null differ diff --git a/examples/Tutorial/Webinar Example I.ipynb b/examples/Tutorial/Webinar Example I.ipynb deleted file mode 100644 index 559124d7..00000000 --- a/examples/Tutorial/Webinar Example I.ipynb +++ /dev/null @@ -1,1043 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.filterwarnings(\n", - " \"ignore\"\n", - ") # For better visibility, warnings are turned off in this notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# FINE Webinar Part I: 2-nodal Electricity Supply System" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this application of the FINE framework, an energy supply system, consisting of two-regions, is modeled and optimized.\n", - "\n", - "The workflow is structures as follows:\n", - "- Required packages are imported\n", - "- An energy system model instance is created\n", - "- Commodity sources are added to the energy supply system model\n", - "- Commodity conversion components are added to the energy supply system model\n", - "- Commodity storages are added to the energy supply system model\n", - "- Commodity transmission components are added to the energy supply system model\n", - "- Commodity sinks are added to the energy supply system model\n", - "- The energy supply system model is optimized\n", - "- Selected optimization results are presented" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Import required packages\n", - "\n", - "The FINE framework is imported which provides the required classes and functions for modeling the energy system." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import fine as fn # Provides objects and functions to model an energy system\n", - "import pandas as pd # Used to manage data in tables\n", - "import shapely as shp # Used to generate geometric objects\n", - "import numpy as np # Used to generate random input data\n", - "\n", - "np.random.seed(\n", - " 42\n", - ") # Sets a \"seed\" to produce the same random input data in each model run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "import geopandas as gpd # Used to display geo-referenced plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model an energy system" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create an energy system model instance \n", - "\n", - "The structure of the energy supply system model is given by the considered locations, commodities, the number of time steps as well as the hours per time step.\n", - "\n", - "The commodities are specified by a unit (i.e. 'GW_electric', 'GW_naturalGas_lowerHeatingValue', 'Mio. t CO2/h') which can be given as an energy or mass unit per hour. Furthermore, the cost unit and length unit are specified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "# Input parameters\n", - "locations = {\"regionN\", \"regionS\"}\n", - "commodityUnitDict = {\n", - " \"electricity\": r\"GW$_{el}$\",\n", - " \"naturalGas\": r\"GW$_{CH_{4},LHV}$\",\n", - " \"CO2\": r\"Mio. t$_{CO_2}$/h\",\n", - "}\n", - "commodities = {\"electricity\", \"naturalGas\", \"CO2\"}\n", - "numberOfTimeSteps, hoursPerTimeStep = 8760, 1\n", - "costUnit, lengthUnit = \"1e6 Euro\", \"km\"\n", - "\n", - "# Code\n", - "esM = fn.EnergySystemModel(\n", - " locations=locations,\n", - " commodities=commodities,\n", - " numberOfTimeSteps=numberOfTimeSteps,\n", - " commodityUnitsDict=commodityUnitDict,\n", - " hoursPerTimeStep=hoursPerTimeStep,\n", - " costUnit=costUnit,\n", - " lengthUnit=lengthUnit,\n", - " verboseLogLevel=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add source components\n", - "\n", - "Source components generate commodities across the energy system's virtual boundaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = \"Wind turbines\", \"electricity\"\n", - "hasCapacityVariable = True\n", - "operationRateMax = pd.DataFrame(\n", - " [[np.random.beta(a=2, b=7.5), np.random.beta(a=2, b=9)] for t in range(8760)],\n", - " index=range(8760),\n", - " columns=[\"regionN\", \"regionS\"],\n", - ").round(6)\n", - "capacityMax = pd.Series([400, 200], index=[\"regionN\", \"regionS\"])\n", - "investPerCapacity, opexPerCapacity = 1200, 1200 * 0.02\n", - "interestRate, economicLifetime = 0.08, 20\n", - "\n", - "# If data should be read from an excel file:\n", - "writer = pd.ExcelWriter(\"windTurbineProfile.xlsx\") # writes data to an excel file\n", - "operationRateMax.to_excel(writer) # (not required if excel file\n", - "writer.close() # already exists)\n", - "operationRateMax = pd.read_excel(\n", - " \"windTurbineProfile.xlsx\", index_col=0\n", - ") # reads an excel file located in\n", - "# the current working directory\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " operationRateMax=operationRateMax,\n", - " capacityMax=capacityMax,\n", - " investPerCapacity=investPerCapacity,\n", - " opexPerCapacity=opexPerCapacity,\n", - " interestRate=interestRate,\n", - " economicLifetime=economicLifetime,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = \"PV\", \"electricity\"\n", - "hasCapacityVariable = True\n", - "dailyProfileSimple = [\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0.05,\n", - " 0.15,\n", - " 0.2,\n", - " 0.4,\n", - " 0.8,\n", - " 0.7,\n", - " 0.4,\n", - " 0.2,\n", - " 0.15,\n", - " 0.05,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - " 0,\n", - "]\n", - "operationRateMax = pd.DataFrame(\n", - " [[u, u] for day in range(365) for u in dailyProfileSimple],\n", - " index=range(8760),\n", - " columns=[\"regionN\", \"regionS\"],\n", - ")\n", - "capacityMax = pd.Series([100, 100], index=[\"regionN\", \"regionS\"])\n", - "investPerCapacity, opexPerCapacity = 800, 800 * 0.02\n", - "interestRate, economicLifetime = 0.08, 25\n", - "\n", - "# If data should be read from an excel file:\n", - "writer = pd.ExcelWriter(\"PV_Profile.xlsx\") # writes data to an excel file\n", - "operationRateMax.to_excel(writer) # (not required if excel file\n", - "writer.close() # already exists)\n", - "operationRateMax = pd.read_excel(\n", - " \"PV_Profile.xlsx\", index_col=0\n", - ") # reads an excel file located in\n", - "# the current working directory\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " operationRateMax=operationRateMax,\n", - " capacityMax=capacityMax,\n", - " investPerCapacity=investPerCapacity,\n", - " opexPerCapacity=opexPerCapacity,\n", - " interestRate=interestRate,\n", - " economicLifetime=economicLifetime,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = \"Natural gas import\", \"naturalGas\"\n", - "hasCapacityVariable = False\n", - "commodityCost = 0.03\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " commodityCost=commodityCost,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add conversion components\n", - "\n", - "Conversion components convert m commodities into n other commodities." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, physicalUnit = \"Gas power plants\", r\"GW$_{el}$\"\n", - "commodityConversionFactors = {\n", - " \"electricity\": 1,\n", - " \"naturalGas\": -1 / 0.63,\n", - " \"CO2\": 201 * 1e-6 / 0.63,\n", - "}\n", - "hasCapacityVariable = True\n", - "investPerCapacity, opexPerCapacity = 650, 650 * 0.03\n", - "interestRate, economicLifetime = 0.08, 30\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=name,\n", - " physicalUnit=physicalUnit,\n", - " commodityConversionFactors=commodityConversionFactors,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " investPerCapacity=investPerCapacity,\n", - " opexPerCapacity=opexPerCapacity,\n", - " interestRate=interestRate,\n", - " economicLifetime=economicLifetime,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add storage components\n", - "\n", - "Storage components can store commodities across time steps.\n", - "\n", - "The self discharge of a storage technology is described in FINE in percent per hour. If the literature value is given in percent per month, e.g. 3%/month, the self discharge per hours is obtained using the equation (1-$\\text{selfDischarge}_\\text{hour})^{30*24\\text{h}} = 1-\\text{selfDischarge}_\\text{month}$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = \"Batteries\", \"electricity\"\n", - "hasCapacityVariable = True\n", - "chargeEfficiency, dischargeEfficiency, selfDischarge = (\n", - " 0.95,\n", - " 0.95,\n", - " 1 - (1 - 0.03) ** (1 / (30 * 24)),\n", - ")\n", - "chargeRate, dischargeRate = 1, 1\n", - "investPerCapacity, opexPerCapacity = 150, 150 * 0.01\n", - "interestRate, economicLifetime, cyclicLifetime = 0.08, 22, 12000\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " chargeEfficiency=chargeEfficiency,\n", - " cyclicLifetime=cyclicLifetime,\n", - " dischargeEfficiency=dischargeEfficiency,\n", - " selfDischarge=selfDischarge,\n", - " chargeRate=chargeRate,\n", - " dischargeRate=dischargeRate,\n", - " investPerCapacity=investPerCapacity,\n", - " opexPerCapacity=opexPerCapacity,\n", - " interestRate=interestRate,\n", - " economicLifetime=economicLifetime,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add transmission components\n", - "\n", - "Transmission components transmit commodities between regions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = \"AC cables\", \"electricity\"\n", - "hasCapacityVariable = True\n", - "capacityFix = pd.DataFrame(\n", - " [[0, 30], [30, 0]], columns=[\"regionN\", \"regionS\"], index=[\"regionN\", \"regionS\"]\n", - ")\n", - "distances = pd.DataFrame(\n", - " [[0, 400], [400, 0]], columns=[\"regionN\", \"regionS\"], index=[\"regionN\", \"regionS\"]\n", - ")\n", - "losses = 0.0001\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " capacityFix=capacityFix,\n", - " distances=distances,\n", - " losses=losses,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "distances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add sink components\n", - "\n", - "Sinks remove commodities across the energy system´s virtual boundaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [], - "scrolled": true - }, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = (\n", - " \"Electricity demand\",\n", - " \"electricity\",\n", - ")\n", - "hasCapacityVariable = False\n", - "dailyProfileSimple = [\n", - " 0.6,\n", - " 0.6,\n", - " 0.6,\n", - " 0.6,\n", - " 0.6,\n", - " 0.7,\n", - " 0.9,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 1,\n", - " 0.9,\n", - " 0.8,\n", - "]\n", - "operationRateFix = pd.DataFrame(\n", - " [\n", - " [(u + 0.1 * np.random.rand()) * 25, (u + 0.1 * np.random.rand()) * 40]\n", - " for day in range(365)\n", - " for u in dailyProfileSimple\n", - " ],\n", - " index=range(8760),\n", - " columns=[\"regionN\", \"regionS\"],\n", - ").round(2)\n", - "\n", - "# If data should be read from an excel file:\n", - "writer = pd.ExcelWriter(\"demandProfile.xlsx\") # writes data to an excel file\n", - "operationRateFix.to_excel(writer) # (not required if excel file\n", - "writer.close() # already exists)\n", - "operationRateFix = pd.read_excel(\n", - " \"demandProfile.xlsx\", index_col=0\n", - ") # reads an excel file located in\n", - "# the current working directory\n", - "\n", - "# Code\n", - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " operationRateFix=operationRateFix,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "name, commodity = (\n", - " \"CO2 to enviroment\",\n", - " \"CO2\",\n", - ")\n", - "hasCapacityVariable = False\n", - "commodityLimitID, yearlyLimit = \"CO2 limit\", 366 * (1 - 0.8)\n", - "\n", - "# Code\n", - "if yearlyLimit > 0:\n", - " esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=name,\n", - " commodity=commodity,\n", - " hasCapacityVariable=hasCapacityVariable,\n", - " commodityLimitID=commodityLimitID,\n", - " yearlyLimit=yearlyLimit,\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimize energy system model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All components are now added to the model and the model can be optimized. If the computational complexity of the optimization should be reduced, the time series data of the specified components can be clustered before the optimization and the parameter timeSeriesAggregation is set to True in the optimize call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Input parameters\n", - "numberOfTypicalPeriods = 30\n", - "\n", - "# Code\n", - "esM.aggregateTemporally(numberOfTypicalPeriods=numberOfTypicalPeriods)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Input parameters\n", - "timeSeriesAggregation = True\n", - "solver = \"glpk\"\n", - "\n", - "# Code\n", - "esM.optimize(timeSeriesAggregation=timeSeriesAggregation, solver=solver)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Output of selected results\n", - "\n", - "For the assessment of the optimization result, several result output functions are available. They can be categorized into output in form of tables, geo-referenced output visualization and the full time series visualization.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a regional shape file and visualize it\n", - "\n", - "Information on the geometrical shape of the investigated regions can either be downloaded from a website (e.g. from https://gadm.org/) or manually created. In this notebook, the geometries are manually created." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# Create two circles, representing the two regions, and store their geometries in a shape file\n", - "shpRegionS = shp.geometry.Point(0.5, 0.5).buffer(0.5)\n", - "shpRegionN = shp.geometry.Point(0.5, 1.5).buffer(0.5)\n", - "regionsGdf = gpd.GeoDataFrame(\n", - " {\"geometry\": [shpRegionN, shpRegionS], \"regionName\": [\"regionN\", \"regionS\"]},\n", - " index=[\"regionN\", \"regionS\"],\n", - " crs=\"epsg:3035\",\n", - ")\n", - "regionsGdf.to_file(\"regions.shp\")\n", - "\n", - "# Create a line, representing the connection between the two regions, and store its geometry in a\n", - "# shape file\n", - "lines = shp.geometry.LineString([(0.5, 0.5), (0.5, 1.5)])\n", - "linesGdf = gpd.GeoDataFrame(\n", - " {\n", - " \"geometry\": [lines, lines],\n", - " \"loc0\": [\"regionN\", \"regionS\"],\n", - " \"loc1\": [\"regionS\", \"regionN\"],\n", - " },\n", - " index=[\"regionN_regionS\", \"regionS_regionN\"],\n", - " crs=\"epsg:3035\",\n", - ")\n", - "linesGdf.to_file(\"lines.shp\")\n", - "\n", - "# Visualize the geometric representation of the two regions\n", - "fig, ax = fn.plotLocations(\"regions.shp\", indexColumn=\"regionName\", plotLocNames=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Display optimization summaries\n", - "\n", - "For each modeling class, an optimization summary can be stored and displayed. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "srcSnkSummary = esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=1)\n", - "display(esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "convSummary = esM.getOptimizationSummary(\"ConversionModel\", outputLevel=1)\n", - "display(esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "storSummary = esM.getOptimizationSummary(\"StorageModel\", outputLevel=1)\n", - "display(esM.getOptimizationSummary(\"StorageModel\", outputLevel=2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "transSummary = esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=1)\n", - "display(esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Display regional and operational plots\n", - "\n", - "Georeferenced plots as well as plots representing time series can be displayed for each component." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wind turbines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# If wind turbines are built, their capacities are displayed in a geo-referenced plot\n", - "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", - " fig, ax = fn.plotLocationalColorMap(\n", - " esM,\n", - " \"Wind turbines\",\n", - " \"regions.shp\",\n", - " \"regionName\",\n", - " perArea=False,\n", - " zlabel=\"Capacity\\n[GW]\\n\",\n", - " figsize=(4, 4),\n", - " )\n", - "else:\n", - " print(\"No wind turbines built.\")\n", - "\n", - "# If wind turbines are built in regionN, their operation is displayed as heatmap\n", - "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Wind turbines\",\n", - " \"regionN\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", - " )\n", - "\n", - "# If wind turbines are built in regionS, their operation is displayed as heatmap\n", - "if srcSnkSummary.loc[(\"Wind turbines\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Wind turbines\",\n", - " \"regionS\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the Day\",\n", - " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", - " orientation=\"vertical\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PV systems" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# If PV systems are built, their capacities are displayed in a geo-referenced plot\n", - "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", - " fig, ax = fn.plotLocationalColorMap(\n", - " esM,\n", - " \"PV\",\n", - " \"regions.shp\",\n", - " \"regionName\",\n", - " perArea=False,\n", - " zlabel=\"Capacity\\n[GW]\\n\",\n", - " figsize=(4, 4),\n", - " )\n", - "else:\n", - " print(\"No PV systems built.\")\n", - "\n", - "# If PV systems are built in regionS, their operation is displayed as heatmap\n", - "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"PV\",\n", - " \"regionN\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", - " )\n", - "\n", - "# If PV systems are built in regionS, their operation is displayed as heatmap\n", - "if srcSnkSummary.loc[(\"PV\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"PV\",\n", - " \"regionS\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", - " orientation=\"vertical\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gas power plants" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# If CCGT plants are built, their capacities are displayed in a geo-referenced plot\n", - "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\")].sum() > 0:\n", - " fig, ax = fn.plotLocationalColorMap(\n", - " esM,\n", - " \"Gas power plants\",\n", - " \"regions.shp\",\n", - " \"regionName\",\n", - " perArea=False,\n", - " zlabel=\"Capacity\\n[GW]\\n\",\n", - " figsize=(4, 4),\n", - " )\n", - "else:\n", - " print(\"No CCGT plants built.\")\n", - "\n", - "# If CCGT plants are built in regionS, their operation is displayed as heatmap\n", - "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\"), \"regionN\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Gas power plants\",\n", - " \"regionN\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Operation\\nin regionN\\n[GW]\",\n", - " orientation=\"vertical\",\n", - " )\n", - "\n", - "# If CCGT plants are built in regionS, their operation is displayed as heatmap\n", - "if convSummary.loc[(\"Gas power plants\", \"capacity\", \"[GW$_{el}$]\"), \"regionS\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Gas power plants\",\n", - " \"regionS\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Operation\\nin regionS\\n[GW]\",\n", - " orientation=\"vertical\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Batteries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "code_folding": [], - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# If batteries are built, their capacities are displayed in a geo-referenced plot\n", - "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\")].sum() > 0:\n", - " fig, ax = fn.plotLocationalColorMap(\n", - " esM,\n", - " \"Batteries\",\n", - " \"regions.shp\",\n", - " \"regionName\",\n", - " perArea=False,\n", - " zlabel=\"Capacity\\n[GWh]\\n\",\n", - " figsize=(4, 4),\n", - " )\n", - "else:\n", - " print(\"No batteries built.\")\n", - "\n", - "# If batteries are built in regionS, their storage inventory is displayed as heatmap\n", - "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\"), \"regionN\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Batteries\",\n", - " \"regionN\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"State of charge\\nin regionN\\n[GW]\",\n", - " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", - " orientation=\"vertical\",\n", - " )\n", - "\n", - "# If batteries are built in regionS, their storage inventory is displayed as heatmap\n", - "if storSummary.loc[(\"Batteries\", \"capacity\", \"[GW$_{el}$*h]\"), \"regionS\"] > 0:\n", - " fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Batteries\",\n", - " \"regionS\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"State of charge\\nin regionS\\n[GW]\",\n", - " variableName=\"stateOfChargeOperationVariablesOptimum\",\n", - " orientation=\"vertical\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### AC cables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-skip" - ] - }, - "outputs": [], - "source": [ - "# The built AC cable capacities are displayed\n", - "fig, ax = fn.plotLocations(\"regions.shp\", indexColumn=\"regionName\")\n", - "fig, ax = fn.plotTransmission(\n", - " esM,\n", - " \"AC cables\",\n", - " \"lines.shp\",\n", - " loc0=\"loc0\",\n", - " loc1=\"loc1\",\n", - " fig=fig,\n", - " ax=ax,\n", - " cbHeight=0.4,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Electricity demand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The electricity demand time series in regionN is displayed\n", - "fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Electricity demand\",\n", - " \"regionN\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Demand\\nin regionN\\n[GW]\",\n", - " orientation=\"vertical\",\n", - ")\n", - "\n", - "# The electricity demand time series in regionS is displayed\n", - "fig, ax = fn.plotOperationColorMap(\n", - " esM,\n", - " \"Electricity demand\",\n", - " \"regionS\",\n", - " figsize=(5, 3),\n", - " xlabel=\"Day of the year\",\n", - " ylabel=\"Hour of the day\",\n", - " zlabel=\"Demand\\nin regionS\\n[GW]\",\n", - " orientation=\"vertical\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "toc": { - "base_numbering": 1, - "nav_menu": { - "height": "431px", - "width": "510px" - }, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "399px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Water_Supply_System/Water Supply System.ipynb b/examples/Water_Supply_System/Water Supply System.ipynb deleted file mode 100644 index 5193089b..00000000 --- a/examples/Water_Supply_System/Water Supply System.ipynb +++ /dev/null @@ -1,593 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import fine as fn\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Water supply of a small mountain village\n", - "\n", - "Two new houses (house 5 and 6) were built in a small mountain village in Utopia which requires an update of the existing clean water supply system of the village which now consists of 6 houses:\n", - "\n", - "\n", - "\n", - "\n", - "### Water demand\n", - "The demand for clean water occurs in spring between 5 am and 11 pm, in summer between 4 am and 12 pm, in autumn between 5 am and 11 pm and in winter between 6 am and 11 pm. The demand for one house assumes random values between 0 to 1 Uh (Unit*hour) during the demand hours. These values are uniformly distributed and are 0 outside the demand hours.\n", - "\n", - "### Water supply \n", - "The water supply comes from a small tributary of a glacier river, which provides more water in summer and less in winter: the profile is given for each hour of the year as\n", - "\n", - "f(t) = 8 \\* sin(π*t/8760) + g(t) \n", - " \n", - "where g(t) is a uniformly distributed random value between 0 and 4.\n", - "\n", - "### Water storage\n", - "Clean water can be stored in a water tank (newly purchased). The invest per capacity is 100€/Uh, the economic lifetime is 20 years.\n", - "\n", - "### Water treatment\n", - "The river water is converted to clean water in a water treatment plant (newly purchased). The invest per capacity is 7000€/U, the economic lifetime is 20 years. Further, it needs some electricity wherefore it has operational cost of 0.05 €/U.\n", - "\n", - "### Water transmission\n", - "The clean water can be transported via water pipes, where some already exist between the houses 1-4, the water treatment plant and the\n", - "water tank, however new ones might need to\n", - "be built to connect the newly built houses or reinforce the transmission along the old pipes. The invest for new pipes per capacity is 100 €/(m\\*U), the invest if a new pipe route is built is 500 €/(m\\*U), the economic lifetime is 20 years.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "locations = [\n", - " \"House 1\",\n", - " \"House 2\",\n", - " \"House 3\",\n", - " \"House 4\",\n", - " \"House 5\",\n", - " \"House 6\",\n", - " \"Node 1\",\n", - " \"Node 2\",\n", - " \"Node 3\",\n", - " \"Node 4\",\n", - " \"Water treatment\",\n", - " \"Water tank\",\n", - "]\n", - "commodityUnitDict = {\"clean water\": \"U\", \"river water\": \"U\"}\n", - "commodities = {\"clean water\", \"river water\"}\n", - "numberOfTimeSteps = 8760\n", - "hoursPerTimeStep = 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM = fn.EnergySystemModel(\n", - " locations=set(locations),\n", - " commodities=commodities,\n", - " numberOfTimeSteps=8760,\n", - " commodityUnitsDict=commodityUnitDict,\n", - " hoursPerTimeStep=1,\n", - " costUnit=\"1e3 Euro\",\n", - " lengthUnit=\"m\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Source" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "riverFlow = pd.DataFrame(np.zeros((8760, 12)), columns=locations)\n", - "np.random.seed(42)\n", - "riverFlow.loc[:, \"Water treatment\"] = np.random.uniform(0, 4, (8760)) + 8 * np.sin(\n", - " np.pi * np.arange(8760) / 8760\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Source(\n", - " esM=esM,\n", - " name=\"River\",\n", - " commodity=\"river water\",\n", - " hasCapacityVariable=False,\n", - " operationRateMax=riverFlow,\n", - " opexPerOperation=0.05,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conversion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eligibility = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], index=locations)\n", - "esM.add(\n", - " fn.Conversion(\n", - " esM=esM,\n", - " name=\"Water treatment plant\",\n", - " physicalUnit=\"U\",\n", - " commodityConversionFactors={\"river water\": -1, \"clean water\": 1},\n", - " hasCapacityVariable=True,\n", - " locationalEligibility=eligibility,\n", - " investPerCapacity=7,\n", - " opexPerCapacity=0.02 * 7,\n", - " interestRate=0.08,\n", - " economicLifetime=20,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Storage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eligibility = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], index=locations)\n", - "esM.add(\n", - " fn.Storage(\n", - " esM=esM,\n", - " name=\"Water tank\",\n", - " commodity=\"clean water\",\n", - " hasCapacityVariable=True,\n", - " chargeRate=1 / 24,\n", - " dischargeRate=1 / 24,\n", - " locationalEligibility=eligibility,\n", - " investPerCapacity=0.10,\n", - " opexPerCapacity=0.02 * 0.1,\n", - " interestRate=0.08,\n", - " economicLifetime=20,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transmission" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Distances between eligible regions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "distances = np.array(\n", - " [\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 38, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 38, 40, 0, 105, 0, 0, 0, 0],\n", - " [0, 0, 38, 40, 0, 0, 105, 0, 100, 0, 0, 0],\n", - " [38, 40, 0, 0, 0, 0, 0, 100, 0, 30, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 30, 0, 20, 50],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 0, 0],\n", - " ]\n", - ")\n", - "\n", - "distances = pd.DataFrame(distances, index=locations, columns=locations)\n", - "distances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Old water pipes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "capacityFix = np.array(\n", - " [\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0],\n", - " [1, 1, 0, 0, 0, 0, 0, 2, 0, 4, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 4],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0],\n", - " ]\n", - ")\n", - "\n", - "capacityFix = pd.DataFrame(capacityFix, index=locations, columns=locations)\n", - "capacityFix" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The old pipes have many leckages wherefore they lose 0.1%/m of the water they transport." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "isBuiltFix = capacityFix.copy()\n", - "isBuiltFix[isBuiltFix > 0] = 1\n", - "\n", - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"Old water pipes\",\n", - " commodity=\"clean water\",\n", - " losses=0.1e-2,\n", - " distances=distances,\n", - " hasCapacityVariable=True,\n", - " hasIsBuiltBinaryVariable=True,\n", - " bigM=100,\n", - " capacityFix=capacityFix,\n", - " isBuiltFix=isBuiltFix,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## New water pipes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "incidence = np.array(\n", - " [\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", - " [0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0],\n", - " [0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0],\n", - " [1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", - " ]\n", - ")\n", - "\n", - "eligibility = pd.DataFrame(incidence, index=locations, columns=locations)\n", - "eligibility" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The new are pipes are better but still lose 0.05%/m of the water they transport." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Transmission(\n", - " esM=esM,\n", - " name=\"New water pipes\",\n", - " commodity=\"clean water\",\n", - " losses=0.05e-2,\n", - " distances=distances,\n", - " hasCapacityVariable=True,\n", - " hasIsBuiltBinaryVariable=True,\n", - " bigM=100,\n", - " locationalEligibility=eligibility,\n", - " investPerCapacity=0.1,\n", - " investIfBuilt=0.5,\n", - " interestRate=0.08,\n", - " economicLifetime=50,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sink" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "winterHours = np.append(range(8520, 8760), range(1920))\n", - "springHours, summerHours, autumnHours = (\n", - " np.arange(1920, 4128),\n", - " np.arange(4128, 6384),\n", - " np.arange(6384, 8520),\n", - ")\n", - "\n", - "demand = pd.DataFrame(np.zeros((8760, 12)), columns=list(locations))\n", - "np.random.seed(42)\n", - "demand[locations[0:6]] = np.random.uniform(0, 1, (8760, 6))\n", - "\n", - "demand.loc[winterHours[(winterHours % 24 < 5) | (winterHours % 24 >= 23)]] = 0\n", - "demand.loc[springHours[(springHours % 24 < 4)]] = 0\n", - "demand.loc[summerHours[(summerHours % 24 < 5) | (summerHours % 24 >= 23)]] = 0\n", - "demand.loc[autumnHours[(autumnHours % 24 < 6) | (autumnHours % 24 >= 23)]] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "demand.sum().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.add(\n", - " fn.Sink(\n", - " esM=esM,\n", - " name=\"Water demand\",\n", - " commodity=\"clean water\",\n", - " hasCapacityVariable=False,\n", - " operationRateFix=demand,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimize the system" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "esM.aggregateTemporally(numberOfTypicalPeriods=7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# esM.optimize(timeSeriesAggregation=True, optimizationSpecs='LogToConsole=1 OptimalityTol=1e-6 crossover=1')\n", - "esM.optimize(timeSeriesAggregation=True, solver=\"glpk\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Selected results output" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sources and Sinks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"SourceSinkModel\", outputLevel=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Storage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"StorageModel\", outputLevel=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conversion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"ConversionModel\", outputLevel=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transmission" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "esM.getOptimizationSummary(\"TransmissionModel\", outputLevel=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [ - "nbval-check-output" - ] - }, - "outputs": [], - "source": [ - "esM.componentModelingDict[\"TransmissionModel\"].operationVariablesOptimum.sum(axis=1)" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-", - "formats": "ipynb,py:percent", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/test/aggregations/spatialAggregation/test_manager.py b/test/aggregations/spatialAggregation/test_manager.py index a87978a4..65d48284 100644 --- a/test/aggregations/spatialAggregation/test_manager.py +++ b/test/aggregations/spatialAggregation/test_manager.py @@ -20,7 +20,7 @@ def test_esm_to_xr_and_back_during_spatial_aggregation( SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/clusteredRegions.shp", ) @@ -109,7 +109,7 @@ def test_error_in_reading_shp(test_esM_for_spagat): with pytest.raises(FileNotFoundError): SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles", ) @@ -127,7 +127,7 @@ def test_error_in_reading_shp(test_esM_for_spagat): with pytest.raises(ValueError): SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/three_regions.shp", ) @@ -139,7 +139,7 @@ def test_error_in_reading_shp(test_esM_for_spagat): def test_spatial_aggregation_string_based(test_esM_for_spagat): SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/clusteredRegions.shp", ) @@ -184,7 +184,7 @@ def test_spatial_aggregation_distance_based( ): SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/clusteredRegions.shp", ) @@ -221,7 +221,7 @@ def test_spatial_aggregation_parameter_based( ): SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/clusteredRegions.shp", ) @@ -247,7 +247,7 @@ def test_aggregation_of_balanceLimit(balanceLimitConstraint_test_esM): esM = balanceLimitConstraint_test_esM[0] SHAPEFILE_PATH = os.path.join( os.path.dirname(__file__), - "../../../examples/Multi-regional_Energy_System_Workflow/", + "../../../examples/03_Multi-regional_Energy_System_Workflow/", "InputData/SpatialData/ShapeFiles/clusteredRegions.shp", ) diff --git a/test/conftest.py b/test/conftest.py index 86853fc9..26014215 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -12,7 +12,7 @@ os.path.dirname(__file__), "..", "examples", - "Multi-regional_Energy_System_Workflow", + "03_Multi-regional_Energy_System_Workflow", ) ) from getData import getData