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：为了维护可靠的电网，我们需要针对动态重构（DyR）等复杂问题开发快速决策算法。DyR实时优化配电网开关配置，以最小化电网损耗，并调度可用发电资源向负荷供电。DyR属于混合整数规划问题，对于大规模电网及快速时间尺度下的求解而言，在计算上可能难以处理。我们提出GraPhyR——一种专为DyR量身定制的物理信息图神经网络（GNNs）框架。该框架将关键运行约束和连通性约束直接融入GNN架构中，并采用端到端训练方式。结果表明，GraPhyR能够学习并优化DyR任务。