Optimal charging strategy for electric vehicles in residential charging station under dynamic spike pricing policy

Nowadays, much attention has been drawn to environmental protection. Traveling by electric vehicles (EVs) instead of conventional fuel cars is strongly supported by national governments for the sustainable development of urban transportation. However, the increasing EV charging load in residential areas have brought a heavy burden to the distribution transformer. Therefore, coordinated charging of EVs in the residential charging station (RCS) is essential to relieve the power supply pressure. In this paper, an optimal charging strategy based on dynamic spike pricing (DSP) policy is proposed to reduce the charging cost of EVs and ensure the normal operation of the distribution transformer. First of all, the load model of EVs on four kinds of typical days is established with consideration of the seasonal and holiday characteristics of EV charging demands. Then, a new DSP policy based on Time-of-Use (TOU) mechanism is designed with an additional spike time period and a spike price to transfer peak loads in rush hours. To protect EV users from financial losses and prevent transformer overload, an optimal charging model is formulated to minimize the charging cost of EVs with considering the power margin of the distribution transformer. Ultimately, the genetic algorithm (GA) is used to solve the model. The simulation results show that the optimal charging strategy proposed in this paper is effective in peak shaving and reducing charging cost.