Spatial–Temporal Optimal Pricing for Charging Stations: A Model-Driven Approach Based on Group Price Response Behavior of EVs

To adapt to the “dual carbon” goals and effectively guide the charging of electric vehicles (EVs) while reducing issues such as long queueing times and underutilization of charging stations (CSs) due to unreasonable pricing by CS operators (CSOs), this article proposes an optimal spatial–temporal pricing strategy for CSs based on the group price response behavior of EVs. First, this study predicts the spatial–temporal distribution of EV load demand using trip chain and probability theory. Then, the Monte Carlo method is employed to simulate the spatial–temporal distribution of EV load demand and group charging behavior. Subsequently, an EV-CSO two-layer pricing demand response model is established, comprising an upper layer pricing model for CSOs and a lower layer charging decision model for the EV group. Finally, the model is solved to obtain the optimal pricing strategy. In addition, the decision behavior of EVs is simplified through node clustering, and the optimal spacing is obtained through the iterative search algorithm. The results show that compared to traditional pricing strategies, the proposed method improves the total profit of CSs and the average utilization rate of charging piles. Furthermore, the node clustering method significantly improves the computational efficiency of the pricing model, providing theoretical guidance for complex traffic network analysis of large-scale EVs.