Zhang, TianChen, WeiHan, ZhuCao, Zhigang2020-05-112020-05-1112/20/2013Copyright 2013 IEEE Transactions on Vehicular Technology. This is a pre-print version of a published paper that is available at: https://ieeexplore.ieee.org/abstract/document/6690262. Recommended citation: Zhang, Tian, Wei Chen, Zhu Han, and Zhigang Cao. "Charging scheduling of electric vehicles with local renewable energy under uncertain electric vehicle arrival and grid power price." IEEE Transactions on Vehicular Technology 63, no. 6 (2013): 2600-2612. doi: 10.1109/TVT.2013.2295591. This item has been deposited in accordance with publisher copyright and licensing terms and with the author's permission.https://hdl.handle.net/10657/6474In this paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grids. The uncertainty of the arrival of the EV, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the required charging energy for each EV is random. The goal is to minimize the mean waiting time for EVs under the long-term constraint on the cost. We propose queue mapping to convert the EV queue to the charging demand queue, and we prove the equivalence between the minimization of the two queues' average length. Then, we focus on the minimization for the average length of the charging demand queue under the long-term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this constrained stochastic optimization problem. The system state includes the charging demand queue length, the charging demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally, the number of charging demands and the allocated energy from the storage battery compose the 2-D policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the 2-D policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally, we investigate the proposed policies numerically.Charging schedulingelectric vehicle (EV)Markov decision process (MDP)renewable energyArticle