Intelligent Electric Vehicle Charging Scheduling in Transportation-Energy Nexus with Distributional Reinforcement Learning

Dear Editor, This letter is concerned with electric vehicle (EV) charging scheduling problem in transportation-energy nexus using an intelligent decision-making strategy with probabilistic self-adaptability features. In order to accommodate the coupling effects of stochastic EV driving behavior on transport network and distribution network, a risk-captured distributional reinforcement learning solution is presented by using explicit probabilistic information for action and reward function in Markov decision process (MDP) model, where the Bellman equation is extended to a more generalized version. Scheduling EV charging in a transportation-energy nexus, according to both transport and distribution network conditions, is an important topic in recent studies to improve the driving and charging energy efficiency, especially considering the high penetration rate of EV nowadays and even more extremely higher one in the future [1]. In order to accommodate the coupling effects of stochastic EV driving behavior and battery state-of-charge (SoC) on transport and distribution network, various methods have been developed for designing the smart charging scheduling strategy with consideration of electricity price, renewable energy adoption, road conditions and many others.