Multi-objective Coordinated Charging Strategy for Electric Vehicles Considering Dynamic Time-of-Use Electricity Price

Large-scale electric grid connection of electric vehicles will affect the reliable and economic operation of power grid. Meanwhile, the traditional time-of-use price is relatively fixed and not flexible. Aiming at the above problems, the paper comprehensively considers the time-of-use price, load variation and users’ willingness to charge, and proposes a multi-objective coordinated charging strategy for electric vehicles (EVs) considering dynamic time-of-use electricity price. Firstly, the EVs charging behavior characteristics are analyzed using the measured EVs charging data of a public charging station in Hunan Province, and the dynamic time-of-use price model is given. Secondly, an EV orderly charging optimization model is constructed, which is aimed at minimum peak-valley difference, minimum daily load variance, minimum user charging cost of distribution network. Then, a Genetic Simulated Annealing Particle Swarm Optimization (GSAPSO) algorithm based on adaptive inertial weight is proposed. Finally, the rationality and effectiveness of the proposed strategy are verified by simulation of typical scenarios. The simulation results show that the coordinated charging strategy solved by dynamic time-of-use price and GSAPSO algorithm can better reduce the peak-valley difference, the daily load variance, and the charging cost of users. As the responsiveness of electric vehicles participating in the coordinated charging strategy increases, the optimization effect is more obvious.