发表机构
IBM Research; Hydro-Québec Research Institute; ETH Zurich; Stony Brook University; Brookhaven National Laboratory（IBM研究院; 魁北克水电研究院; 苏黎世联邦理工学院; 石溪大学; 布鲁克海文国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员推出统一神经求解器GENCO及开源GridFM开发框架，其可处理电网PF、OPF、SE任务，在基准测试中提速显著且性能优于经典求解器，降低电网分析门槛。
AI 中文摘要
基础模型正在变革业务工作流并提升生产力，但在电力系统分析等工程领域仍基本缺失，这类领域必须严格保证物理一致性。我们提出GENCO（几何神经校正优化器，GEometric Neural Corrective Optimizer），一款用于稳态输电电网分析的统一神经求解器，可在单一架构与共享网络表示中处理潮流计算（PF）、最优潮流（OPF）和状态估计（SE）。为支持神经电力系统求解器的进展，我们推出开源GridFM开发框架，在低代码环境中标准化合成数据生成与训练；同时发布包含数百万个PF与OPF场景的大规模数据集，覆盖多样电网拓扑，以支持可复现的基准测试。我们在PFDelta与OPFData基准上，针对Newton-Raphson、IPOPT等经典求解器及最优神经求解器，结合实际Hydro-Québec SCADA数据评估GENCO：在大规模潮流计算中，GENCO可恢复全交流运行状态（含电压幅值与无功功率，这是直流潮流（DC-PF）无法提供的），同时达到DC-PF级的有功功率平衡残差，相比Newton-Raphson最高提速30倍，仅为DC-PF运行时间的2倍；在最优潮流中，相比IPOPT最高提速85倍，且在可行性、最优性和运行时间上优于DC-OPF；在状态估计中，GENCO对含噪测量与网络参数误差的鲁棒性优于经典加权最小二乘，即使加权最小二乘无法收敛时，也总能返回高质量估计。统一架构与开发框架为大规模稳态电网分析提供新方法，降低电力系统工程师的入门门槛，迈向电网基础模型迈出一步。
英文摘要
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.
