Learning-based scheduling of integrated charging-storage-discharging station for minimizing electric vehicle users' cost

Abstract

Charging cost is an important concern for electric vehicle (EV) users. The ordered charging behavior, such as the reasonable selection of charging period and charging power, can greatly decrease users' charging cost. Towards the integrated charging-storage-discharging station (ICSDS), a learning-based method is proposed in this paper to minimize EV users' cost. The physical constraints of ICSDS and the user's demand are first built, and the charging scheduling problem of ICSDS is formulated as a Markov Decision Process (MDP) with unknown transition probability. Second, in order to generate optimal schedule through the learning network, the deep features of the future electricity price are extracted by using a long short-term memory (LSTM) network. Third, a twofold deep deterministic policy gradient (TDDPG) algorithm is proposed to generate the continuous charging actions and avoid the Q value overestimation. In addition, the TDDPG-based scheduling strategy is designed on the basis of the extracted features of electricity price. Finally, the validations through the real-world data demonstrate that the features of electricity price are extracted with a satisfied accuracy. Moreover, compared with many benchmarked methods, the experimental results demonstrate that the charging scheduling by TDDPG possesses excellent performance in minimizing EV users' cost.