Electric Vehicle Charging Scheduling Strategy based on Genetic Algorithm

Abstract

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.

Research on Orderly Charging Strategy for Electric Vehicles Based on Electricity Price Guidance and Reliability Evaluation of Microgrid

“…The genetic algorithm (GA) is a method for searching for optimal solutions by simulating the natural process of evolution. In GA, species undergo operations such as selection, crossover, and variation to achieve the "survival of the fittest" [28,29]. The EGA is an improved genetic algorithm that performs well in solving complex optimization problems by retaining excellent solutions, and improving convergence speed and precision.…”

Status and Development of Research on Orderly Charging and Discharging of Electric Vehicles

“…An improved genetic algorithm solves the model to minimize users' charging time and the grid's peak-to-valley load difference. The authors in [61] used the genetic algorithm as an optimization method to perform multiobjective optimization and guide owners to regulate and orderly charging. The objectives of reducing charging time, reducing cost, and reducing load peak-to-valley differences are achieved.…”

A DQN-based Two-Stage Scheduling Method for Real-Time Large-Scale EVs Charging Service

“…The GA, as a third-party to cooperate with CSs and EVs, offers scheduling information from a global perspective under large-scale EV charging requests scenarios. Some studies have proposed the heuristic algorithm-based EV charging scheduling method to deal with the large-scale surging requests according to the information offered by GA, e.g., genetic algorithm [7][8][9], PSO (particle swarm optimization) [10][11][12], artificial bee colony [13]. However, these methods are easily trapped into local optimization and need more time to get a suboptimal solution under a large solution space of problems with surge charging demand.…”