用图神经网络加速电网拓扑重构最优潮流，提升实时调度效率。
Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow
- 用图神经网络预测最优电网拓扑，替代传统耗时优化。
- 预处理与后处理层使模型更小、训练更快、预测更准。
- 适合电力系统实时调度场景，尤其对大规模电网有效。
最优潮流（OPF）已用于电网实时运行。以往研究显示，利用动态拓扑灵活性可提升电网效率，但会将线性OPF转化为混合整数线性规划的网络重构最优潮流（NR-OPF）问题，显著增加计算时间。为此，本文提出一种基于机器学习（尤其是图神经网络，GNN）的方法，以加速求解过程。GNN模型在离线阶段训练，用于预测优化前的最佳拓扑结构。此外，本文还设计了离线预处理ML过滤层，以减小GNN模型规模并缩短训练时间，同时提高准确率；并提出了在线后处理选择层，分析GNN预测结果，筛选出高置信度的若干候选解决方案。案例研究验证了所提方法在结合预/后处理层后的优越性能。
原文摘要 · Abstract (English)
Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear programming network-reconfigured OPF (NR-OPF) problem, substantially increasing the computing time. Thus, a machine learning (ML)-based approach, particularly utilizing graph neural network (GNN), is proposed to accelerate the solution process. The GNN model is trained offline to predict the best topology before entering the optimization stage. In addition, this paper proposes an offline pre-ML filter layer to reduce GNN model size and training time while improving its accuracy. A fast online post-ML selection layer is also proposed to analyze GNN predictions and then select a subset of predicted NR solutions with high confidence. Case studies have demonstrated superior performance of the proposed GNN-accelerated NR-OPF method augmented with the proposed pre-ML and post-ML layers.
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