Safe Deep Reinforcement Learning-Based Real-Time Operation Strategy in Unbalanced Distribution System

Abstract

Unbalanced voltages are one of the voltage quality issues affecting customer devices in distribution systems. Conventional optimization methods are time-consuming to mitigate unbalanced voltage in real time because these approaches must solve each scenario after observation. Deep reinforcement learning (DRL) is effectively trained offline for real-time operations that overcome the time-consumption problem in practical implementation. This paper proposes a safe deep reinforcement learning (SDRL) based distribution system operation method to mitigate unbalanced voltage for real-time operation and satisfy operational constraints. The proposed SDRL method incorporates a learning module (LM) and a constraint module (CM), controlling the energy storage system (ESS) to improve voltage balancing. The proposed SDRL method is compared with the hybrid optimization (HO) and typical DRL models regarding time consumption and voltage unbalance mitigation. For this purpose, the models operate in modified IEEE-13 node and IEEE-123 node test feeders.