GridSFM is a 15‑million‑parameter physics‑inspired graph neural network that serves as a foundation model for solving AC Optimal Power Flow (AC‑OPF) across diverse grid topologies. Pretrained on 54 topologies ranging from 500 to 4,000 buses, it achieves a 2.45 % zero‑shot generation‑cost error on a held‑out 10,000‑bus case and adapts to unseen grids with only 100 solved instances using a physics‑informed fine‑tuning scheme based on Newton’s method. The authors address the disconnected feasible set of AC‑OPF by lifting and relaxing constraints with logarithmically penalized slacks, proving the resulting elastic feasible set is contractible and that solutions can be projected back onto the original feasible set.
By Luke Bhan, Weiwei Yang, Margaret Capetz, Baosen Zhang
The paper introduces Penalty + Sequential Linearized Feasibility Seeking (SLFS), a self‑supervised learning framework for solving multiphase AC optimal power flow (AC‑OPF) in distribution systems with topology reconfiguration. SLFS trains directly from the AC‑OPF objective and constraints using a differentiable fixed‑point power flow solver, avoiding the need for labeled optimal solutions. It achieves negligible optimality gaps and near‑zero constraint violations on IEEE feeders up to 8,500 nodes, delivering up to three orders of magnitude speedups over IPOPT while maintaining robustness to large distributional shifts.
By Hoang T. Nguyen, Shaohui Liu, Reetam Sen Biswas, Varsha Pendyala, Nurali Virani, Deepjyoti Deka, Priya L. Donti
arXiv:2609.16738v1 Announce Type: cross
Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computati...
By Ferran Bohigas-Daranas, Hamid Latif-Mart\'inez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Pere Barlet-Ros
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
By Martin Sadric, Sebastian P\"utz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Sch\"afer
arXiv:2606. 06300v1 Announce Type: new Abstract: We propose MResOpt, a staged residual neural network architecture for constrained optimization problems.
By Merve Karakas, Christopher J. Williams, Emmanuel O. Balogun, Sadegh Sadeghi Tabas, Christian Brown, Nikhil Rao
arXiv:2605.23194v2 Announce Type: replace-cross
Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...
By Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim, Teja Kuruganti
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
By Wangkun Xu, Zhongda Chu, Fei Teng
The paper extends the neural network verification framework to graph neural networks by introducing GraphStar sets, which model uncertainty over both node and edge features. This allows sound propagation of linear message‑passing operations and ReLU nonlinearities for GCN and GINE layers. Experiments on power system tasks (PF, OPF, CFA) and graph classification benchmarks (ENZYMES, PROTEINS) show that the method, called GNNV, yields tighter robustness guarantees than CORA and provides, for the first time, edge‑aware guarantees for GINE‑based models under joint node and edge perturbations.
By Anne M. Tumlin, Ben Wooding, Zhenxuan Shao, Diego Manzanas Lopez, Tyler Derr, Taylor T. Johnson
arXiv:2602. 17975v2 Announce Type: replace Abstract: This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations.
By Robert Parker
arXiv:2607. 22788v1 Announce Type: cross Abstract: AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily in electricity market operations.
By Zhilin Huang
arXiv:2608. 09921v1 Announce Type: new Abstract: Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced.
By Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy, Mangaliso Mngomezulu, H\'ector Maeso-Garc\'ia, Thomas Tolhurst, Javad Bayazi, Ali Moeini, Naomi Simumba, Celia Cintas, David Nelischer, Romeo Kienzler, Jonas Weiss, Anna Varbella, Florian D\"orfler, Gabriela Hug, Martin Mevissen, Juan Bernab\'e-Moreno, Fran\c{c}ois Mirall\`es, Hendrik F. Hamann, Etienne Vos, Thomas Brunschwiler
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
By Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng