GridSFM: A Foundation Model for Solving AC Optimal Power Flow
GridSFM, a 15-million-parameter graph model, solves AC optimal power flow on grids up to 10,000 buses.
GridSFM is a 15-million-parameter physics-inspired graph neural network pretrained across 54 grid topologies of 500 to 4,000 buses for AC optimal power flow. It reports a 2.45% zero-shot generation-cost error on held-out operating conditions for a 10,000-bus case, with no degradation as system size grows. Physics-informed fine-tuning based on Newton's method adapts it to unseen grids up to 10,000 buses using 100 solved instances and beats more heavily trained single-topology models as a warm start. The authors release models, data, and code and prove an elastic relaxation whose feasible set is contractible.
- 15M-parameter GNN pretrained on 54 topologies of 500 to 4,000 buses.
- Zero-shot generation-cost error was 2.45% on a 10,000-bus case.
- Newton-based fine-tuning adapts to new grids with 100 solved instances.
- Authors release models, data, and code and prove an elastic relaxation.
From the original article
1 min read at arxiv.orgWe 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.
By Luke Bhan · arxiv.org
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