Modeling Practically Private Wireless Vehicle to Grid System With Federated Reinforcement Learning

The Smart Grid (SG) infrastructure plan offers growth opportunities for the electric vehicle (EV) industry and aims to reduce dependence on fossil fuels. Surprisingly, the literature lacks comprehensive research on data privacy issues within the EV-SG ecosystem. In response, this paper presents an efficient federated reinforcement learning (FRL) framework tailored to cost-effectively preserve privacy in wireless vehicle-to-grid (V2G) systems. Our approach involves the use of a small auxiliary battery to generate noise, conceal the true energy demand of electric vehicles, and learn the time-varying dynamics of energy usage for wireless EV charging through a federated process. Within this framework, we employ deep Q-learning to concurrently minimize costs and maximize privacy rewards, while exploring innovative techniques to enhance learning speed and communication efficiency through a global FRL approach.