Grid Foundation Models – State of the Union

Session Chair: Hendrik F. Hamann
Brookhaven National Laboratory and Stony Brook University

This session provides an update on the state of the art of foundation models for power systems. Speakers will review current GridFM results, separating proven capabilities from promising ideas. Topics include architectures, training, fine-tuning, evaluation, generalizability, adaptability, data efficiency, accuracy, topology robustness, shared benchmarks, roadmaps, and collaboration.

GENCO — A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Presenter: Thomas Brunschwiler – IBM Research

We present GENCO (GEometric Neural Corrective Optimizer), a unified neural solver for steady-state transmission grid analysis that addresses power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared grid representation. To accelerate advances in neural power-system solvers, we introduce the open-source GridFM Development Framework, which standardizes synthetic data generation and model training in a low-code environment, together with large-scale datasets containing millions of PF and OPF scenarios across diverse grid topologies.

We evaluate GENCO on state-of-the-art PF and OPF benchmarks against leading neural and classical solvers, and validate SE on real-world Hydro-Québec SCADA data. For large-scale PF, GENCO recovers the full AC operating state—including voltage magnitudes and reactive power unavailable from DC-PF—while achieving DC-PF-level active power-balance residuals and up to 30× speedups over Newton–Raphson, at only 2× the runtime of DC-PF. For OPF, GENCO achieves up to 85× speedups over IPOPT while improving feasibility, optimality, and runtime over DC-OPF. For SE, GENCO is more robust than classical weighted least squares to noisy measurements and grid parameters and returns high-quality estimates even when WLS fails to converge.

Together, GENCO and the GridFM Development Framework enable scalable steady-state grid analysis, reduce integration effort, and lower barriers to developing neural grid solvers, representing a key step toward Grid Foundation Models.