GridSFM: A Foundation Model for Solving AC Optimal Power Flow
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 $10{,}000$ bus...
| Authors | Luke Bhan, Weiwei Yang, Margaret Capetz et al. |
|---|---|
| Primary category | eess.SY |
| Published | 24 September 2026 |
| Last updated at source | 6 October 2026 |
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