Real-time energy management of photovoltaic-assisted electric vehicle charging station by markov decision process

Yu Wu, Jian Zhang (Corresponding author), Alexandre Ravey, Daniela Chrenko, Abdellatif Miraoui

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)


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.
Original languageEnglish
Article number228504
Number of pages16
JournalJournal of Power Sources
Publication statusPublished - 15 Nov 2020


  • EV smart charging
  • Markov decision process
  • PV-Assisted EV charging station
  • Adapted bounded real-time dynamic programming
  • Uncertain EV departure time


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