Study on Coordinated Charging Strategy for Electric Vehicles Based on Genetic Algorithm

作者：Longyu Qin, Yujiao Liu, Xiaohan Shi, Fang Shi · 年份：2020 · DOI：10.1109/icpsasia48933.2020.9208478 · 被引用次数：3 · 研究领域：Electric Vehicles and Infrastructure、Advanced Battery Technologies Research、Electric and Hybrid Vehicle Technologies

As the number of electric vehicles (EVs) increases, so might the impacts on the power system performance. Coordinated charging of EVs is a possible solution to the problem. In this work, a coordinated charging strategy for EVs based on genetic algorithm is proposed. First, through analyzing the charging modes and charging time of different types of EVs, the charging loads of different kinds of EVs are calculated by using Monte Carlo method. Then, the mathematical optimization model of EVs coordinated charging is established with the minimum peak-valley difference as the objective function and the genetic algorithm is used to solve the problem. On this basis, an orderly charging strategy is proposed. Finally, a distribution network is taken as an example to carry out simulation analysis. The simulation results show that the proposed strategy can effectively reduce the peak-valley difference and restrain the load fluctuation of the power grid.