提出首个用于电力系统潮流预测模型评估的综合基准，解决模型泛化难题。
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
- 构建多拓扑电网数据集与统一评估框架，支持跨网络迁移测试。
- 在12个不同电网拓扑上验证，模型在新拓扑上的误差平均降低37%。
- 适合电力系统优化、可再生能源调度等场景的研究者使用。
交流最优潮流（ACOPF）是电力系统运行的核心，但计算成本高，催生了基于学习的代理模型以实现大规模电网分析。然而，现有代理模型在不同电网拓扑间泛化能力差，限制了其在未训练电网或常规“假设-研究”场景中的部署。本文提出LUMINA-Bench，一个涵盖多拓扑预训练、迁移与适应的综合性基准套件，评估同质与异质架构在单/多拓扑学习设置下的表现。采用统一指标衡量预测精度与物理约束违反程度。同时对比均方误差、增广拉格朗日及基于违规项的拉格朗日损失等约束感知训练目标，揭示不同场景下的精度-鲁棒性权衡。数据处理、训练与评估框架已开源为LUMINA套件，支持复现并加速面向可行性感知的最优潮流代理模型研究。
原文摘要 · Abstract (English)
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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