LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
Abstract
AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surrogates, however, often fail to generalize across network topologies, a critical gap for deployment on grids not seen during training and for routine operational what-if studies. We introduce LUMINA-Bench, a comprehensive benchmark suite for ACOPF surrogate learning covering multi-topology pretraining, transfer, and adaptation. The benchmark evaluates homogeneous and heterogeneous architectures under single- and multi-topology learning settings using unified metrics that capture both predictive accuracy and physics-informed constraint violations. We additionally compare constraint-aware training objectives, including MSE, augmented Lagrangian, and violation-based Lagrangian losses, to characterize accuracy-robustness trade-offs across settings. Data processing, training, and evaluation frameworks are open-sourced as the LUMINA suite to support reproducibility and accelerate future research on feasibility-aware OPF surrogates.
Cite
@article{arxiv.2605.02133,
title = {LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning},
author = {Hongwei Jin and Keunju Song and Zeeshan Memon and Yijiang Li and Stefano Fenu and Hongseok Kim and Liang Zhao and Kibaek Kim},
journal= {arXiv preprint arXiv:2605.02133},
year = {2026}
}