GridSFM: A Foundation Model for Solving AC Optimal Power Flow
Abstract
We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale. It is a $15$ million parameter physics-inspired graph neural network pretrained across $54$ topologies of $500$ to 4,000 buses. Our model attains a 2.45% zero-shot generation-cost error on a 10,000 bus case held-out operating conditions with no degradation as system size grows. Building on this, we pair the pretrained backbone with a physics-informed fine-tuning design based on Newton's method for power flow. With only $100$ solved instances, GridSFM adapts to unseen grids up to 10,000 buses. We show it out performs single topology, dedicated neural network models that are trained more data, both in terms of cost and solver iterations when deployed as warm starting points. In designing this foundation model, we overcome the fact that the feasible set for AC-OPF can be disconnected. This is an obstruction that prevents any continuous neural network from approximating the solution map. To do so, we lift the problem and relax its constraints with logarithmically penalized slacks. We prove that the resulting elastic feasible set is contractible, that the AC-OPF minimizers remain minimizers of the elastic problem above an explicit penalty threshold, and that projecting an approximate solution back onto the AC-OPF feasible set is well posed. We release all models, data, and code so that the community can build on a shared starting point for AC-OPF.
Related
- A New Perspective on Optimal Power Flow: From Control Theory to Numerical Optimization
- Strengthening AC optimal power flow relaxation through offline bound tightening and linear approximations
- Reciprocal-Manifold Annealed KKT Flows for Constrained Optimization: Application to the Nonconvex AC Optimal Power Flow
- An Accurate Zeroth-Order Perturbation Theory for Solving Power-Law Potentials within the Framework of the Asymptotic Iteration Method
- A Formalism for Merging Power Series in Solving Multinomial Equations: Master Series and Master Numbers
BibTeX
@misc{bhan2026gridsfm,
title = {{GridSFM: A Foundation Model for Solving AC Optimal Power Flow}},
author = {Luke Bhan and Weiwei Yang and Margaret Capetz and Baosen Zhang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.30173},
eprint = {2609.30173},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30173},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - GridSFM: A Foundation Model for Solving AC Optimal Power Flow
AU - Luke Bhan
AU - Weiwei Yang
AU - Margaret Capetz
AU - Baosen Zhang
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2609.30173
UR - https://arxiv.org/abs/2609.30173
AB - We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale. It is a $15$ million parameter physics-inspired graph neural network pretrained across $54$ topologies of $500$ to 4,000 buses. Our model attains a 2.45% zero-shot generation-cost error on a 10,000 bus case held-out operating conditions with no degradation as system size grows. Building on this, we pair the pretrained backbone with a physics-informed fine-tuning design based on Newton's method for power flow. With only $100$ solved instances, GridSFM adapts to unseen grids up to 10,000 buses. We show it out performs single topology, dedicated neural network models that are trained more data, both in terms of cost and solver iterations when deployed as warm starting points. In designing this foundation model, we overcome the fact that the feasible set for AC-OPF can be disconnected. This is an obstruction that prevents any continuous neural network from approximating the solution map. To do so, we lift the problem and relax its constraints with logarithmically penalized slacks. We prove that the resulting elastic feasible set is contractible, that the AC-OPF minimizers remain minimizers of the elastic problem above an explicit penalty threshold, and that projecting an approximate solution back onto the AC-OPF feasible set is well posed. We release all models, data, and code so that the community can build on a shared starting point for AC-OPF.
ER -