A Customized Voltage Control Strategy for Electric Vehicles in Distribution Networks With Reinforcement Learning Method

Abstract

The increasing electric vehicles (EVs) at charging stations will impose great challenges on the conventional voltage control in distribution networks. In this article, a two-stage voltage control strategy based on deep reinforcement learning is proposed to mitigate voltage violations caused by the uncertainty of EVs and load. In the first stage, the charging demand of EVs is predicted based on trip chain theory and simulated by Monte Carlo simulation. The optimal power flow is then performed to determine the day-ahead dispatch of on-load tap changer and capacitor banks. In the second stage, the real-time voltage control problem is formulated as a Markov Game considering both reactive power control and vehicle to grid modes of EVs. The problem is solved by the deep deterministic policy gradient algorithm to develop a well-trained control strategy that can be implemented online. Moreover, a novel customized charging criterion is proposed to conduct the charging behavior of EVs and guarantee full charging at the departure time. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the effectiveness in addressing voltage problems.