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

2026-08-10

AI Summary

This work addresses the computational inefficiency of traditional power system analysis methods and the lack of a unified, physically consistent architecture in existing neural solvers. It proposes GENCO, a unified neural solver that, for the first time, enables end-to-end solution of power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single geometric deep learning framework. By integrating AC power grid physics and differentiable optimization, GENCO guarantees physical consistency of its solutions. The accompanying open-source GridFM framework facilitates low-code data generation and training, along with the release of a large-scale synthetic dataset. Experiments demonstrate that GENCO solves PF 30× faster than the Newton-Raphson method with higher accuracy than DC-PF, achieves OPF solutions 85× faster than IPOPT while yielding superior objective values, and maintains high robustness and convergence in SE under measurement noise and parameter errors.

Abstract

Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced. We present GENCO (GEometric Neural Corrective Optimizer), a unified neural solver for steady-state transmission grid analysis that handles power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared network representation. To support advances in neural power system solvers, we introduce the open-source GridFM Development Framework, which standardizes synthetic data generation and training in a low-code environment. We also release large-scale datasets with millions of PF and OPF scenarios across diverse grid topologies to support reproducible benchmarking. We evaluate GENCO on the PFDelta and OPFData benchmarks against state-of-the-art neural solvers and classical solvers, including Newton-Raphson and IPOPT, as well as on real-world Hydro-Québec SCADA data. For large-scale PF, GENCO recovers the full AC operating state, including voltage magnitudes and reactive power that DC-PF cannot provide, while matching DC-PF-level active power-balance residuals. It achieves up to 30x speedups over Newton-Raphson at only 2x the runtime of DC-PF. For OPF, it achieves up to 85x 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 network parameter errors, and always returns a high-quality estimate even when weighted least squares fails to converge. Together, the unified architecture and development framework provide a new approach to large-scale steady-state grid analysis, lowering the barrier to entry for power system engineers and marking a step toward Grid Foundation Models.

Problem

• power flow
• optimal power flow
• state estimation
• steady-state grid analysis
• physical consistency

Innovation

• neural solver
• power system analysis
• foundation model
• geometric constraints
• steady-state grid