用图神经网络优化电网开关配置，提速千倍还提升18%输电能力
Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization
- 分两阶段设计神经网络：先估潮流，再预测开关状态
- 在千节点电网上实现18%输电能力提升，推理快至毫秒级
- 融合物理定律约束，适合电力系统优化与工业应用
本文提出OptiGridML，一种用于电力网络离散拓扑优化的机器学习框架。该任务旨在选择变电站开关配置以最大化跨区域电力输出，传统上被建模为混合整数规划（MIP），对大规模网络而言是NP难且计算不可行的。OptiGridML用两阶段神经架构替代重复求解MIP：第一阶段使用线图神经网络（LGNN）近似给定拓扑下的直流潮流；第二阶段采用异构图神经网络（HeteroGNN）在结构与物理约束下预测开关状态。通过物理信息一致性损失，强制预测潮流满足基尔霍夫定律。在包含最多1000个开关的合成网络上实验表明，OptiGridML相较基线拓扑可实现最高18%的功率输出提升，推理时间从小时级降至毫秒级。结果表明，结构化、流感知的图神经网络在加速物理网络系统的组合优化方面具有巨大潜力。
原文摘要 · Abstract (English)
This paper introduces OptiGridML, a machine learning framework for discrete topology optimization in power grids. The task involves selecting substation breaker configurations that maximize cross-region power exports, a problem typically formulated as a mixed-integer program (MIP) that is NP-hard and computationally intractable for large networks. OptiGridML replaces repeated MIP solves with a two-stage neural architecture: a line-graph neural network (LGNN) that approximates DC power flows for a given network topology, and a heterogeneous GNN (HeteroGNN) that predicts breaker states under structural and physical constraints. A physics-informed consistency loss connects these components by enforcing Kirchhoff's law on predicted flows. Experiments on synthetic networks with up to 1,000 breakers show that OptiGridML achieves power export improvements of up to 18% over baseline topologies, while reducing inference time from hours to milliseconds. These results demonstrate the potential of structured, flow-aware GNNs for accelerating combinatorial optimization in physical networked systems.
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