Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach

Abstract

This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed method achieves a desirable performance in terms of reducing the operation costs of electric vehicle charging stations.