Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems

arXiv:2310.00728v2 Announce Type: replace
Abstract: 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 …

abstract algorithms arxiv cs.lg cs.sy decision distribution dynamic eess.sy graph graph neural network grid losses making math.oc mixed network neural network physics physics-informed power real-time resources stat.ml systems type