基础模型正在改变业务工作流程并提高生产力,，但它们在电力系统分析, 等必须强制执行严格物理一致性的工程领域中仍然基本上缺席。我们推出了 GENCO (GEometric 神经校正优化器),，这是一种用于稳态输电网分析的统一神经解算器，可在单一架构和共享电网表示中处理潮流 (PF), 最优潮流 (OPF), 和状态估计 (SE)。为了支持神经电源系统求解器, 的进步，我们引入了开源 GridFM 开发框架,，该框架标准化了低代码环境中的合成数据生成和训练。我们还发布了跨不同网格拓扑的包含数百万个 PF 和 OPF 场景的大规模数据集，以支持可重复的基准测试。我们根据 PFDelta 和 OPFData 基准对最先进的神经求解器和经典求解器,（包括 Newton-Raphson 和 IPOPT,）以及真实的 Hydro-Québec SCADA 数据进行评估。对于大规模 PF,，GENCO 恢复完整的 AC 运行状态,，包括 DC-PF 无法提供, 的电压幅度和无功功率，同时匹配 DC-PF 级有功功率平衡残差。它的速度比 Newton-Raphson 快 30 倍，而运行时间仅为 DC-PF 的 2 倍。对于 OPF,，它比 IPOPT 实现了高达 85 倍的加速，同时比 DC-OPF 提高了可行性, 最优性, 和运行时间。对于 SE,，GENCO 对噪声测量和网格参数误差, 比经典加权最小二乘更稳健，并且即使加权最小二乘无法收敛，也始终返回高质量估计。 , 统一架构和开发框架为大规模稳态电网分析提供了一种新方法, 降低了电力系统工程师的进入门槛，标志着向电网基础模型迈出了一步。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)