PGLearn - An Open-Source Learning Toolkit for Optimal Power Flow

Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck

Keywords: optimal power flow, machine learning, dataset

TL;DR: Open-source dataset and learning toolkit for optimal power flow

Abstract: Machine learning techniques for Optimal Power Flow (OPF) problems have recently garnered significant attention, reflecting a broader trend of leveraging machine learning to approximate and/or accelerate the resolution of complex optimization problems. These developments are necessitated by the increased volatility and scale in energy production for modern and future grids. However, progress in ML for OPF is hindered by the lack of standardized datasets and evaluation metrics, from generating and solving OPF instances, to training and benchmarking machine learning models. To address this challenge, this paper introduces PGLearn, a comprehensive suite of standardized datasets and evaluation tools for ML and OPF. PGLearn implements realistic data generation procedures that capture both global and local variability, ensuring that datasets are representative of real-world conditions. In addition, it supports multiple OPF formulations, including AC, DC, and second-order cone formulations. Standardized datasets are made publicly available to democratize access to this field, reduce the burden of data generation, and enable the fair comparison of various methodologies. PGLearn also includes a robust toolkit for training, evaluating, and benchmarking machine learning models for OPF, with the goal of standardizing performance evaluation across the field. By promoting open, standardized datasets and evaluation metrics, PGLearn aims at democratizing and accelerating research and innovation in machine learning applications for optimal power flow problems.

Primary Area: datasets and benchmarks

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Submission Number: 10442