gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation

TL;DR

gridfm-datakit-v1 is a Python library that generates diverse, realistic power flow and optimal power flow datasets, enabling better training of machine learning models for power system analysis, especially under varied and challenging conditions.

Contribution

It introduces a novel dataset generation approach that incorporates realistic stochastic perturbations, scenario diversity, and variable generator costs, scaling efficiently to large power grids.

Findings

Creates more diverse and realistic datasets for ML training.

Enables generation of data beyond standard operating limits.

Scales efficiently to large power systems with up to 10,000 buses.

Abstract

We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) lack of realistic stochastic load and topology perturbations, limiting scenario diversity; (2) PF datasets are restricted to OPF-feasible points, hindering generalization of ML solvers to cases that violate operating limits (e.g., branch overloads or voltage violations); and (3) OPF datasets use fixed generator cost functions, limiting generalization across varying costs. gridfm-datakit addresses these challenges by: (1) combining global load scaling from real-world profiles with localized noise and supporting arbitrary N-k topology perturbations to create diverse yet realistic datasets; (2) generating PF samples beyond operating limits; and (3) producing OPF data with varying generator costs. It also scales efficiently to large grids (up to 10,000 buses). Comparisons with OPFData, OPF-Learn, PGLearn, and PF$\Delta$ are provided. Available on GitHub at https://github.com/gridfm/gridfm-datakit under Apache 2.0 and via `pip install gridfm-datakit`.