用图神经网络加速电网拓扑优化，实现快速精准的阻塞管理。
Transferable Graph Learning for Transmission Congestion Management via Busbar Splitting
- 设计异构边感知的GNN模型，预测母线分裂节点
- 在2000节点系统上提速万倍，1分钟内出解，误差仅2.3%
- 支持不同电网、运行状态下的跨系统迁移应用
通过母线分裂进行网络拓扑优化（NTO）可缓解输电拥堵并降低再调度成本。然而，现有求解器难以在近实时条件下处理大规模系统的混合整数非线性问题。机器学习方法虽具潜力，但对未见拓扑、运行条件及系统间的泛化能力有限。本文基于线性化交流潮流建模NTO问题，提出一种图神经网络（GNN）加速方法。设计异构边感知的消息传递GNN，用于预测母线分裂的有效节点作为候选解。该GNN捕捉局部潮流模式，提升对未知拓扑变化的泛化能力，并增强跨系统迁移性能。案例研究显示，在GOC 2000节点系统上实现高达4个数量级的速度提升，1分钟内生成满足交流潮流可行性的解，优化差距仅为2.3%。结果表明，该方法显著推进了大规模系统近实时NTO的发展，具备拓扑与跨系统泛化能力。
原文摘要 · Abstract (English)
Network topology optimization (NTO) via busbar splitting can mitigate transmission grid congestion and reduce redispatch costs. However, solving this mixed-integer nonlinear problem for large-scale systems in near-real-time is currently intractable with existing solvers. Machine learning (ML) approaches have emerged as a promising alternative, but they have limited generalization to unseen topologies, varying operating conditions, and different systems, which limits their practical applicability. This paper formulates NTO for congestion management considering linearized AC power flow, and proposes a graph neural network (GNN)-accelerated approach. We develop a heterogeneous edge-aware message passing GNN to predict effective nodes for busbar splitting actions as candidate NTO solutions. The proposed GNN captures local flow patterns, improves generalization to unseen topology changes, and enhances transferability across systems. Case studies show up to 4 orders-of-magnitude speed-up, delivering AC-feasible solutions within one minute and a 2.3% optimality gap on the GOC 2000-bus system. These results demonstrate a significant step toward near-real-time NTO for large-scale systems with topology and cross-system generalization.
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