|
|
--- |
|
|
license: cc-by-sa-4.0 |
|
|
tags: |
|
|
- energy |
|
|
- optimization |
|
|
- optimal_power_flow |
|
|
- power_grid |
|
|
pretty_name: PGLearn Optimal Power Flow (small) |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
task_categories: |
|
|
- tabular-regression |
|
|
viewer: false |
|
|
--- |
|
|
|
|
|
# PGLearn optimal power flow (small) dataset |
|
|
|
|
|
This dataset contains input data and solutions for small-size Optimal Power Flow (OPF) problems. |
|
|
Original case files are based on instances from Power Grid Lib -- Optimal Power Flow ([PGLib OPF](https://github.com/power-grid-lib/pglib-opf)); |
|
|
this dataset comprises instances corresponding to systems with up to 300 buses. |
|
|
|
|
|
## Contents |
|
|
|
|
|
For each system (e.g., `14_ieee`, `118_ieee`), the dataset provides multiple OPF instances, |
|
|
and corresponding primal and dual solutions for the following OPF formulations |
|
|
* AC-OPF (nonlinear, non-convex) |
|
|
* DC-OPF approximation (linear, convex) |
|
|
* Second-Order Cone (SOC) relaxation of AC-OPF (nonlinear, convex) |
|
|
|
|
|
This dataset was created using [OPFGenerator](https://github.com/AI4OPT/OPFGenerator); |
|
|
please see the [OPFGenerator documentation](https://ai4opt.github.io/OPFGenerator/dev/) for details on mathematical formulations. |
|
|
|
|
|
## Use cases |
|
|
|
|
|
The primary intended use case of this dataset is to learn a mapping from input data to primal and/or dual solutions. |
|
|
|
