Physics-informed Graphical Representation-enabled Deep Reinforcement Learning for Robust Distribution System Voltage Control

Abstract

The anomalous measurements and inaccurate distribution system physical models cause huge challenges for distribution system optimization. This paper proposes a robust voltage control method that can deal with them by systematically integrating a representation network, the deep reinforcement learning (DRL) method, and the surrogate model. The partial observation of the distribution network is first represented as a graph with tree topology that is processed by a physics-informed global graph attention network (GGAT) and a deep auto-encoder (DAE) to achieve informative and robust representation of the real-time and pseudo-measurements. The extracted features are then fed into the soft actor-critic algorithm, during the training of which a graphical-learning-based power flow surrogate model is developed to provide a reward signal for the DRL algorithm. This allows the proposed method to reduce reliance on accurate distribution system parameters. The embedding of the structural information by the GGAT and the informative features extracted by the DAE further enhances the robustness of the proposed method against anomalous measurements. The proposed method is validated using IEEE 33-node and 119-node systems. Simulation results show the robustness of the proposed method against anomalous measurements.