arXiv subjects

Matteo Baù

Publications and source records attributed to Matteo Baù.

2 recordsLinked to original sources

arXiv2025

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

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$Δ$ are provided. Available on GitHub at https://github.com/gridfm/gridfm-datakit under Apache 2.0 and via `pip install gridfm-datakit`.

cs.LG↗

arXiv2025

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method

Several methods have been proposed in the literature to improve the quality of AC optimal power flow (AC-OPF) datasets used in machine learning (ML) models. Yet, scalability to large power systems remains unaddressed and comparing generation approaches is still hindered by the absence of widely accepted metrics quantifying AC-OPF dataset quality. In this work, we tackle both these limitations. We provide a simple heuristic that samples load setpoints uniformly in total load active power, rather than maximizing volume coverage, and solves an AC-OPF formulation with load slack variables to improve convergence. For quality assessment, we formulate a multi-criteria framework based on three metrics, measuring variability in the marginal distributions of AC-OPF primal variables, diversity in constraint activation patterns among AC-OPF instances and activation frequency of variable bounds. By comparing four open-source methods based on these metrics, we show that our heuristic consistently outperforms uniform random sampling, whether independent or constrained to a convex polytope, scoring as best in terms of balance between dataset quality and scalability.

eess.SY↗