Real-time energy management of photovoltaic-assisted electric vehicle charging station by markov decision process
Yu Wu, Jian Zhang, Alexandre Ravey, Daniela Chrenko, Abdellatif Miraoui
Abstract
With the rapid development of electric vehicles (EVs), the dramatic rise in the demand for electricity is creating heavy pressure on local grids. The combination of renewable energy and EV charging stations (EVCSs) provides a promising solution for alleviating the scarcity of electricity. In this paper, a finite-horizon Markov decision process (MDP) model is proposed for the optimal control of a photovoltaic (PV)-assisted EVCS in a university campus. The proposed model employs the vehicle-to-grid (V2G) technology to provide ancillary services and takes dynamic electricity price and the uncertainty of the EV owners’ parking behaviors into consideration. To guarantee computational efficiency, an adapted bounded real-time dynamic programming (BRTDP) algorithm is developed as the solution technique of the MDP model. Numerical simulations based on the dynamic electricity price of France, real PV data, and the vehicle parking patterns in a university campus are conducted to demonstrate the effectiveness of the proposed energy management system (EMS). Simulation results show that the EMS can reduce the total costs by more than 55% and 29% in summer and winter compared to the conventional charging policy, while guaranteeing the satisfaction rate of the demand for EV-charging without knowing the departure times of EVs a priori.