Shanghai University of Electric Power
The movement and parking of electric vehicles between different types of planned land have certain rules. Based on the different charging and discharging needs of residential areas, industrial areas, and commercial areas, a mobile energy storage model for electric vehicles has been established. In addition, in order to better reflect the advantages of mobile energy storage for electric vehicles in load management, comprehensively considering the power grid, vehicle owners, and charging and discharging places, a multi-objective optimization model is constructed with the goal of minimizing the load standard deviation and maximizing economic benefits. The Pareto optimal front surface is obtained through the fast non-dominated sorting genetic algorithm (NSGA-II), and the fuzzy membership method is used to find the compromise optimal solution. Finally, a simulation of the charging and discharging case of mobile energy storage for electric vehicles containing three residential areas, one industrial area and one commercial area is carried out to verify the effectiveness of the proposed model and strategy.