Long-time scale vehicle-to-grid scheduling strategy considering psychological effect based on Weber-Fechner law

Short-time scale charging is a common assumption in current electric vehicle studies. However, this assumption reduces the complexity of charging behavior by ignoring the fact that a large amount of electric vehicles only require to charge once every few days. Furthermore, it can be trapped in local optimization. Therefore, we propose a long-time scale vehicle-to-grid scheduling strategy for electric vehicles considering the psychological effect of range anxiety. The long-time scale vehicle-to-grid scheduling strategy includes 2 models to optimize the comprehensive benefits of electric vehicles in long-time scale, namely the day-ahead scheduling model and the real-time optimization model. The day-ahead scheduling model completes long-time scheduling, while the realtime optimization model follows the day-ahead scheduling results to complete real-time optimization. In addition, the psychological effect of range anxiety based on Weber-Fechner law and the scheduling cost of electric vehicle users are quantified in the two models to further quantify the comprehensive benefits of electric vehicles. Finally, two methods are used to deal with the strategy. The rolling horizon optimization method is used to solve the real-time optimization process of the strategy. The non-dominated sorting genetic algorithm is used to deal with the multi-objective problem. Simulation results show that the proposed strategy can make electric vehicles more responsive to participate in the vehicle-to-grid process to improve users' satisfaction, and enhance the enthusiasm and participation of receiving scheduling.