A Multiagent Federated Reinforcement Learning Approach for Plug-In Electric Vehicle Fleet Charging Coordination in a Residential Community
Yunfei Chu, Zhinong Wei, Xicheng Fang, Sheng Chen, Yizhou Zhou
Abstract
The increasing penetration of distributed renewable energy and electric vehicles (EV) in local microgrids/residential-community has brought a great challenge to balancing system stability and economic benefits. This paper proposes a decentralized framework based on an efficient federated deep reinforcement learning method for plug-in electric vehicle (PEV) fleet charging management in a residential community, which is equipped with a photovoltaic and battery energy storage system and connected to a local transformer. Firstly, the framework of PEV charging management is described as a virtual EV charging station coordinating charging tasks through sharing public information with distributed agents. Then, an individual preference model of PEV is developed considering heterogenous PEV charging anxiety, battery degradation, and collective penalty. Subsequently, we propose an attention-weighted federated soft-actor-critic method to efficiently seek the co-ordinational scheduling of the PEV fleet charging in a distributed way, where scalability and privacy protection can be ensured with attention-based information sharing. Finally, a real-world case study is conducted to validate the effectiveness and feasibility of the proposed approach.