GENCO：面向稳态电网分析的统一神经求解器与 GridFM 开发框架
GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
GENCO（GEometric Neural Corrective Optimizer）是单一架构内同时处理潮流（PF）、最优潮流（OPF）和状态估计（SE）的统一神经求解器，并配套开源 GridFM 开发框架与百万级 PF/OPF 场景数据集。
Authors:Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy, Mangaliso Mngomezulu, Héctor Maeso-García, Thomas Tolhurst, Javad Bayazi, Ali Moeini, Naomi Simumba, Celia Cintas, David Nelischer, Romeo Kienzler, Jonas Weiss, Anna Varbella, Florian Dörfler, Gabriela Hug, Martin Mevissen, Juan Bernabé-Moreno, François Mirallès, Hendrik F. Hamann, Etienne Vos, Thomas Brunschwiler
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 grid 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 grid 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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.09921 [cs.AI] |
| (or arXiv:2608.09921v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.09921 arXiv-issued DOI via DataCite |
Submission history
From: Alban Puech [view email]
[v1]
Mon, 10 Aug 2026 17:57:49 UTC (4,207 KB)
[v2]
Thu, 20 Aug 2026 16:00:29 UTC (4,198 KB)
[v3]
Sat, 24 Oct 2026 11:42:19 UTC (4,203 KB)
来源：arXiv cs.AI · arxiv.org