Federated Learning Based Demand Reshaping for Electric Vehicle Charging

Abstract

Though electric vehicles (EVs) are efficient in power consumption, EV charging is time consuming and hence EV users may experience long delay for charging during peak hours in urban areas. Reshaping of heterogeneous EV charging demand enhances user experience and charging stations' profit. This study proposes a demand reshaping framework, in which each charging station announces different hourly charging prices ahead of time and EV users can freely select their charging destinations. The optimal charging prices should minimize the waiting duration for charging and maximize charging stations' profit. To this end, charging stations train a deep neural network model to predict hourly charging demand at distinct charging stations. Subsequently, the optimal prices are numerically computed by leveraging the trained neural network. We show that peak demand for EV charging is smoothed out both spatially and tem-porarily for improved quality of service via monetary incentives. Consequently, EV users benefit from decreased charging duration and charging stations obtain profit from increased service quality.