---
title: "GENCO：面向稳态电网分析的统一神经求解器与开发框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.09921v1"
permalink: "https://feed.astralor.com/items/itm_cc98d0f69a8d475ff6ab01f6"
published: "2026-08-10T17:57:49.000Z"
score: 77
channel: "ai"
tags: ["arXiv","GENCO","GridFM","电力系统分析","神经求解器"]
---

# GENCO：面向稳态电网分析的统一神经求解器与开发框架

## AI 点评

> 这篇论文值得电力AI从业者关注，它证明了神经求解器在物理一致性要求严苛的电网分析中可以兼顾速度与可靠性。对于大规模电网运营商和开发电力基础模型的研究团队，GENCO提供的统一架构和开放工具链可能会显著降低试验成本。

## 速览

GENCO（几何神经校正优化器）是一个统一的神经求解器，在单一架构和共享网络表示下处理潮流计算、最优潮流和状态估计三类稳态电网分析任务。配套开源的GridFM开发框架提供低代码的合成数据生成与训练环境，并发布覆盖多种电网拓扑的百万级数据集。在基准测试中，GENCO在大规模潮流计算上恢复完整交流运行状态，速度比牛顿-拉夫逊法快30倍；最优潮流比IPOPT快85倍，同时可行性、最优性均优于DC-OPF；状态估计在噪声和参数误差下比加权最小二乘更鲁棒。整体方案降低了电力工程师的使用门槛，推进了电网基础模型的发展。

## 关键点

- GENCO用统一架构同时解决潮流计算、最优潮流和状态估计三类稳态电网分析任务，共享网络表示。
- GridFM开发框架提供低代码合成数据生成与训练，并发布百万级场景的开放数据集。
- 大规模PF上比牛顿-拉夫逊快30倍，恢复完整交流状态；OPF比IPOPT快85倍并提升可行性与最优性。
- 状态估计在测量噪声和网络参数误差下比加权最小二乘更鲁棒，即使WLS不收敛也能给出高质量估计。

## 全文

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.

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