Create README.md (#1)

- Create README.md (8623ebfa4608017b28ea325ad3cf455d71682968)


Co-authored-by: Mathieu Tanneau <[email protected]>

README.md

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+---
+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.