Optimization of Time-of-Use Electricity Price of Electric Vehicles Charging Load Based on Spatial-temporal Distribution

According to the mobility of electric vehicles (EVs) charging load, it is important to have a reasonable pricing mechanism that can help EVs users adjust their charging plans. Additionally, charging EVs in different periods and regions can optimize the spatial and temporal distribution of EVs charging load. This study proposes an optimization strategy for EVs charging load based on spatial-temporal distribution and time-of-use pricing. To begin, we combine the travel chain and urban functional areas to simulate the mobility characteristics of EVs. By employing the Monte Carlo approach, we develop a probability distribution model of the charging behavior of EVs to determine the charging load curves in various functional areas. Next, we establish a charging demand elasticity model for EVs, taking into consideration the state of charge (SOC), to measure the relationship between EV charging demand and charging price. Furthermore, we study the relationship between the price elasticity of time-of-use pricing and EVs charging load, with the aim of coordinating the interests of the power grid and EVs users. We establish a time-of-use pricing guidance model for EVs charging and solve it using the particle swarm optimization algorithm. Finally, the results of our example demonstrate that the implementation of time-of-use electricity pricing can effectively guide EVs users in adjusting their charging plans, optimizing the spatial and temporal distribution of EVs charging load, and reducing the fluctuation of charging load and charging cost. These findings provide strong evidence for the effectiveness and guidance of our proposed model.