Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
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Computer Science > Machine Learning
arXiv:2605.23194 (cs)
[Submitted on 22 May 2026]
Title:Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
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Abstract:Fast and reliable optimal power flow (OPF) approximation is essential for reliable smart-grid operation, yet many learning-based surrogates either flatten the native heterogeneous structure of power networks, target a limited set of grid topologies, or lack scalable infrastructure for graph foundation model (GFM) training. This paper presents a scalable heterogeneous graph neural network (GNN) workflow, built on HydraGNN, for data-driven OPF surrogate modeling and OPF-GFM development. The workflow preserves the distinct node and edge types of power grids -- buses, generators, loads, shunts, AC lines, transformers, and device-to-bus couplings -- and supports distributed preprocessing, training, hyperparameter optimization (HPO), and downstream fine-tuning on leadership-class supercomputers. Using three million heterogeneous graph instances spanning ten PGLib-OPF cases, from 14 to 13,659 buses, we conduct DeepHyper-driven HPO on the ORNL Frontier supercomputer. The campaign identifies compact models ($\sim$1.6--1.7M parameters) with the lowest validation losses. Downstream experiments on feasibility classification and N-1 contingency regression show that fine-tuning pretrained OPF GFM improves low-data accuracy, stabilizes training, accelerates convergence, and reduces adaptation cost when partial or head-only fine-tuning is used.
| Comments: | 10 pages, 6 tables, 4 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| MSC classes: | 68T07, 68T09, 68T20 |
| ACM classes: | I.2.8; I.2.11 |
| Cite as: | arXiv:2605.23194 [cs.LG] |
| (or arXiv:2605.23194v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23194
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Massimiliano Lupo Pasini Dr. [view email][v1] Fri, 22 May 2026 03:25:25 UTC (124 KB)
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