Optimal Charging Strategy of Dynamic Electricity Price for Electric Vehicles Based on Improved Particle Swarm Optimization

In recent years, there has been explosive growth in the number of electric vehicles (EV), and large-scale disorderly charging of EV may pose potential safety hazards to the stable operation of the power system. Effective charging control strategies need to be adopted to meet the operational needs of the power system. This paper proposed an EV charging optimization strategy based on dynamic electricity prices. First, Monte Carlo is used to simulate the disorderly charging load of EV based on the vehicle usage characteristics of EV users on working days and rest days. Besides, a dynamic electricity price model is established by regional load distribution, and an optimization function is constructed with the goal of minimizing load fluctuation and minimizing EV charging costs. Under specified conditions, the improved particle swarm optimization (IPSO) is used to resolve the problem. In this improved algorithm, logistic mapping chaos is introduced into inertia weight to expand the search range, the linear method is used to dynamically adjust learning factors to speed up learning efficiency, and breed mutation operators are added to determine the particle search direction. The results show that the IPSO has better advantages in solving this problem, while the power system is relatively stable, and the economic benefits for EV users are improved.