The aim of this repository is to provide the details of the power systems data set and code used in paper [1]. This article has been developed by some members of the OASYS group thanks to the funding of project Flexanalytics. We suggest that you visit the related links to know more about our research 😉
This repository includes the main data of the work in [1]. This data is sourced and extracted from a Library of IEEE PES Power Grid Benchmarks.
In the following links you can download the data related to generators, lines and loads of the different power systems used in this work, where each file has extension of ".csv" format.
Also, the repository includes Python scripts that encompass the codes for the OPF model, generating the valid inequalities for the line-flow constraints, and implementing the iterative coefficient algorithm.
Finally, the repository provides the data generated for each experiment in pickle format.
- Scenarios: This file comprises scenarios of forecast errors, derived from samples drawn from a multivariate normal distribution. It consists of 10 distinct sets of randomly generated samples. The pickle file contains a nested dictionary with two keys: the first key represents the system ('24', '57', '73', '118', and '300'), while the second key corresponds to the index from the sets of randomly generated samples (ranging from 0 to 9). Each value within the nested dictionary is a data frame with dimensions of 'number of scenarios' by 'number of nodes'.
- Valid_Inequalities: The valid inequalities for the line-flow constraints were generated after running the Valid_Analysis.py script. Each pickle file represents a nested dictionary with three keys: the first key corresponds to the index of the randomly generated sample set (ranging from 0 to 9), the second key refers to the type of line-flow constraint (either the lower-bound constraint denoted as 'min' or the upper-bound constraint denoted as 'max'), and the third key corresponds to the index of the line (ranging from 0 to 'number of lines'-1). Each value within the nested dictionary is a data frame containing the slope and intercept of each valid inequality, according to Corollary 2.5 of [1].
[1] Á. Porras, C. Domínguez, J. M. Morales and S. Pineda "Tight and Compact Sample Average Approximation for Joint Chance-constrained Problems with Applications to Optimal Power Flow," INFORMS Journal on Computing, 2023.
If you want to cite this repository, please use the following bib entries:
- Repository:
@article{TCSAAJCCOPF2023,
author = {OASYS},
journal = {GitHub repository (https://github.com/groupoasys/TC{\_}SAA{\_}JCC-OPF)},
title = {{Data and Code for a Tight and Compact Model of the {SAA}-based Joint Chance-constrained {OPF}}},
url = {https://github.com/groupoasys/TC_SAA_JCC-OPF},
year = {2023}
}
Please, do it ☺ Any feedback is welcome 🤩 so feel free to ask or comment anything you want via a Pull Request in this repo. If you need extra help, you can ask Álvaro Porras ([email protected]).
Copyright 2021 Optimization and Analytics for Sustainable energY Systems (OASYS)
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