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