An Improved NSGA-II for Coordinated Charging of Community Electric Vehicle Charging Station

Abstract

The uncontrolled electric vehicles (EVs) charging may pose a threat to the security and stable operation of the power system. In this paper, an improved nondominated sorting genetic algorithm II (NSGA-II) is developed to coordinate the EVs charging in community EV charging station. The coordinated charging scheme minimizes both the charging cost per unit electric energy and the grid load variance associated with the constraints of the EVs charging capacity and the distribution transformer capacity. To overcome the traditional NSGA-II’s deficiency of producing initial population subjected to all constraints with difficulty and uneven distribution of Pareto front, a novel NSGA-II is applied to solve the optimization model based on improved initial population generation method and modified crowded-comparison operator. The optimal compromise scheme is selected from Pareto front using technique for order performance by similarity to ideal solution (TOPSIS). The proposed EVs charging strategy is evaluated in community EV charging station based upon IEEE 33 node system. The simulation results show that the proposed algorithm has greater improvement on grid side load level and charging cost over the traditional NSGA-II and multiple objectives particle swarm optimization (MOPSO) method.