To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task.

翻译：为维持电网可靠性，需要针对动态重构等复杂问题开发快速决策算法。动态重构实时优化配电网开关状态，以最小化网损并调度可用发电资源供电负荷。该问题属于混合整数规划问题，在大规模电网及快速时间尺度下可能产生计算不可解性。本文提出GraPhyR——一种专为动态重构定制的物理信息图神经网络框架。我们将关键运行约束与连通性约束直接嵌入图神经网络框架，并采用端到端训练方式。结果表明，GraPhyR能够学习优化动态重构任务。