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

TL;DR

This paper introduces GraPhyR, a physics-informed graph neural network framework designed for real-time dynamic reconfiguration of power grids, effectively handling complex optimization problems with operational constraints.

Contribution

It presents a novel GNN-based approach that incorporates operational constraints for fast, reliable power grid reconfiguration, addressing computational challenges in large systems.

Findings

GraPhyR successfully learns to optimize dynamic reconfiguration tasks.

The framework incorporates operational and connectivity constraints directly.

Results demonstrate effective real-time decision-making capabilities.

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 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.