README.md

---
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.