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Merge pull request #453 from PyPSA/issue-452
Sector Updates
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| Configuration Option,Configuration Value ,Description,Constraint ,Applied to | ||
| ``import.min``,0,No import requirement,Σ(p) >= 0,S2 S1 gas trade | ||
| ,0.5,Import at least half of historical year value ,Σ(p) >= 5000,S2 S1 gas trade | ||
| ,1,Import at least half of historical year value ,Σ(p) >= 10000,S2 S1 gas trade | ||
| ,2,Import at least double the historical year value ,Σ(p) >= 20000,S2 S1 gas trade | ||
| ``import.max``,0,No imports allowed,Σ(p) <= 0,S2 S1 gas trade | ||
| ,0.5,Import up to half of historical year value ,Σ(p) <= 5000,S2 S1 gas trade | ||
| ,1,Import up to the full historical year value ,Σ(p) <= 10000,S2 S1 gas trade | ||
| ,2,Import up to double the historical year value ,Σ(p) <= 20000,S2 S1 gas trade | ||
| ,inf,Unlimited Imports ,No constraint added,S2 S1 gas trade | ||
| ``export.min``,0,No exports requirement,Σ(p) >= 0,S1 S2 gas trade | ||
| ,0.5,Export at least half of historical year value ,Σ(p) >= 5000,S1 S2 gas trade | ||
| ,1,Export at least half of historical year value ,Σ(p) >= 10000,S1 S2 gas trade | ||
| ,2,Export at least double the historical year value ,Σ(p) >= 20000,S1 S2 gas trade | ||
| ``export.max``,0,No exports allowed,Σ(p) <= 0,S1 S2 gas trade | ||
| ,0.5,Export up to half of historical year value ,Σ(p) <= 5000,S1 S2 gas trade | ||
| ,1,Exportup to the historical year value ,Σ(p) <= 10000,S1 S2 gas trade | ||
| ,2,Export up to double the historical year value ,Σ(p) <= 20000,S1 S2 gas trade | ||
| ,inf,Unlimited Export ,No constraint added,S1 S2 gas trade |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,209 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import geopandas as gpd\n", | ||
| "import pandas as pd\n", | ||
| "import pypsa" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gdf = gpd.read_file(\"Geospatial/regions_onshore_s20_4m.geojson\").set_index(\"name\")\n", | ||
| "gdf[\"p\"] = gdf.geometry.centroid\n", | ||
| "gdf.plot()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "n = pypsa.Network(\"elec_s20_c4m_ec_lv1.0_8760SEG_E-G.nc\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "df = n.loads[n.loads.carrier.str.startswith(\"res\")].copy()\n", | ||
| "df[\"cluster\"] = df.bus.map(lambda x: x.split(\" res\")[0])\n", | ||
| "loads = df.index.to_list()\n", | ||
| "load_map = df[\"cluster\"].to_dict()\n", | ||
| "clusters = list(set([x for _, x in load_map.items()]))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "summed_loads = n.loads_t.p_set[df.index].rename(columns=load_map).T.groupby(level=0).sum().T\n", | ||
| "summed_loads = summed_loads.sum().to_frame(name=\"load\")\n", | ||
| "l = gdf.join(summed_loads)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "l.p.iloc[0].coords[0]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import matplotlib.pyplot as plt\n", | ||
| "\n", | ||
| "fig, axd = plt.subplot_mosaic(\n", | ||
| " [[\"left\", \"top_mid\", \"top_right\"], [\"left\", \"bot_mid\", \"bot_right\"]], figsize=(16, 8), layout=\"constrained\"\n", | ||
| ")\n", | ||
| "\n", | ||
| "l.plot(\"load\", ax=axd[\"left\"], legend=True, legend_kwds={\"label\": \"Yearly Energy Load (MWh)\", \"location\": \"left\"})\n", | ||
| "\n", | ||
| "for idx, row in l.iterrows():\n", | ||
| " axd[\"left\"].annotate(text=idx, xy=row[\"p\"].coords[0], horizontalalignment=\"center\")\n", | ||
| "\n", | ||
| "carrier_map = {\"elec\": \"Electricity\", \"space-heat\": \"Space Heating\", \"water-heat\": \"Water Heating\", \"cool\": \"Cooling\"}\n", | ||
| "\n", | ||
| "for ax_name, carrier in zip(\n", | ||
| " [\"top_mid\", \"top_right\", \"bot_mid\", \"bot_right\"], [\"elec\", \"space-heat\", \"water-heat\", \"cool\"]\n", | ||
| "):\n", | ||
| " df1 = n.loads_t.p_set[[x for x in loads if x.endswith(carrier)]]\n", | ||
| " df1 = df1.rename(columns={x: x.split(\" \")[0] for x in df1.columns})\n", | ||
| " df1 = df1.T.groupby(level=0).sum().T\n", | ||
| " df1.loc[2030].resample(\"D\").mean().plot(title=carrier_map[carrier], ax=axd[ax_name], xlabel=\"\")\n", | ||
| " axd[ax_name].legend(loc=\"upper right\", title=\"Cluster\")\n", | ||
| "\n", | ||
| "fig.savefig(\"clusterd-load.png\", dpi=300)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import seaborn as sns" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def get_hp_cop(n):\n", | ||
| " cops = n.links_t.efficiency\n", | ||
| "\n", | ||
| " ashp = cops[[x for x in cops.columns if x.endswith(\"ashp\")]]\n", | ||
| " gshp = cops[[x for x in cops.columns if x.endswith(\"gshp\")]]\n", | ||
| "\n", | ||
| " return ashp.join(gshp)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def plot_hp_cop(n: pypsa.Network, state: str = None, **kwargs) -> tuple:\n", | ||
| " \"\"\"\n", | ||
| " Plots gshp and ashp cops.\n", | ||
| " \"\"\"\n", | ||
| "\n", | ||
| " investment_period = n.investment_periods[0]\n", | ||
| "\n", | ||
| " cops = get_hp_cop(n).loc[investment_period]\n", | ||
| " cops.index = cops.index.map(lambda x: x.replace(year=2018))\n", | ||
| "\n", | ||
| " fig, axs = plt.subplots(\n", | ||
| " nrows=1,\n", | ||
| " ncols=2,\n", | ||
| " figsize=(16, 6),\n", | ||
| " sharey=True,\n", | ||
| " )\n", | ||
| "\n", | ||
| " mapper = {\"ashp\": \"Air Source Heat Pump\", \"gshp\": \"Ground Source Heat Pump\"}\n", | ||
| "\n", | ||
| " for i, hp in enumerate([\"ashp\", \"gshp\"]):\n", | ||
| "\n", | ||
| " df = cops[[x for x in cops if x.endswith(hp)]]\n", | ||
| " avg = df.mean(axis=1)\n", | ||
| "\n", | ||
| " palette = sns.color_palette([\"lightgray\"], df.shape[1])\n", | ||
| "\n", | ||
| " try:\n", | ||
| "\n", | ||
| " sns.lineplot(\n", | ||
| " df,\n", | ||
| " color=\"lightgray\",\n", | ||
| " legend=False,\n", | ||
| " palette=palette,\n", | ||
| " ax=axs[i],\n", | ||
| " )\n", | ||
| " sns.lineplot(avg, ax=axs[i])\n", | ||
| "\n", | ||
| " axs[i].set_xlabel(\"\")\n", | ||
| " axs[i].set_ylabel(\"COP\")\n", | ||
| " axs[i].set_title(mapper[hp])\n", | ||
| "\n", | ||
| " except TypeError: # no numeric data to plot\n", | ||
| " # logger.warning(f\"No COP data to plot for {state}\")\n", | ||
| " pass\n", | ||
| "\n", | ||
| " return fig, axs" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "fig, ax = plot_hp_cop(n)\n", | ||
| "fig.tight_layout()\n", | ||
| "fig.savefig(\"COP\", dpi=300)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pypsa-usa", | ||
| "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": "3.11.9" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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