| | --- |
| | 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. |
| |
|
