Smart techno-economic operation of electric vehicle charging station in Egypt

Stochastic fast-charging of electric vehicles (EVs) affect the security and economic operation of the distribution power network. Aggregator awareness in the electric power industry is fast growing in tandem with the growing number of EVs. This paper proposes a novel smart techno-economic operation of the electric vehicle charging station (EVCS) in Egypt controlled by the aggregator based on a hierarchal model. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station and flat the surplus power supplied to the utility grid. Mixed-integer linear programming (MILP) is used to solve the first stage where the peak demand value is reduced to 48.17% (4.5 kW) without using any extra battery storage systems. The second challenging stage is to maximize the charging station profit whilst minimizing the EV charging tariff, which needs a trade-off. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted an increase in EVCS revenue by 28.88% and 20.10%, respectively. However, the EVs charging tariff is increased by 21.19%, and 15.03%, respectively. Hence, MDP-RL is an adequate algorithm for such a complex model. The outcomes reveal a sufficient techno-economic hierarchal model concerning the normal operation stated in the literature.