When multiple electric vehicles need to be charged, it will take more time and money for the electric vehicles to randomly enter the charging stations during the disorderly scheduling process. In the meantime, the utilization rate of charging piles is different, and the load of power grid is heavier. In this paper, a charging scheduling strategy is designed considering of the requests of multiple electric vehicles, which schedule in a way of overall parallel. In this charging scheduling strategy, electric vehicles will cost less time and money, the utilization rate of charging piles is more equal, and the power grid has minimum load. According to the charging scheduling strategy, a vehicle charging scheduling model is established based on multi-objective optimization. Technique for order preference by similarity to ideal solution is used to eliminate the dimensions of multiple objectives, and the genetic algorithm is used to solve the model. The simulation results show that the charging scheduling strategy can select appropriate charging stations for electric vehicles and achieve the goal of multi-objective optimization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.