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The Ethereal
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
May 04, 2026 ยท Grace Period ยท + Add venue
Authors
Hongwei Jin, Keunju Song, Zeeshan Memon, Yijiang Li, Stefano Fenu, Hongseok Kim, Liang Zhao, Kibaek Kim
arXiv ID
2605.02133
Category
cs.LG: Machine Learning
Citations
0
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.
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