Deep Reinforcement Learning Approach for Dynamic Distribution Network Reconfiguration Based on Sequential Masking.

Abstract

Dynamic distribution network reconfiguration (DDNR) is a widely used technique for the secure and economic operation of power distribution networks (PDNs), especially in the presence of high-penetration renewable energy sources (RESs). DDNR is realized by controlling the on/off status of remotely controlled switches (RCSs) equipped at power lines in PDNs to optimize power flows. Thanks to the enhanced data availability of PDNs, data-driven solutions to DDNR, such as deep reinforcement learning (DRL), have gained growing attention recently. However, DDNR solves a sequence of combinatorial problems featuring a vast and sparse action space incurred by a so-called "radiality constraint," which is highly challenging for DRLs to handle. Existing DRL methods are either unscalable to large-scale problems or potentially restrict optimality. Hence, we propose a sequential masking strategy to decompose its complex action space into a sequence of maskable sub-action spaces. A gated recurrent unit (GRU)-based agent and an adapted soft actor critic (SAC) algorithm are designed accordingly, producing a data-efficient, safety-guaranteed, and scalable DRL solution to the DDNR problem. Comprehensive comparisons with existing data-driven methods and model-based benchmarks are conducted via various case studies, demonstrating the advantages of the proposed method in both algorithmic performance and scalability.