基于改进粒子群算法的电动出租车快充调度研究OA

The popularization of pure electric vehicles still faces the challenges of uneven charging infrastructure layout and low service efficiency,and the load impact formed by large-scale disorderly charging will lead to voltage shift and increased network loss in the distribution network.As an important application type of pure electric vehicles,electric taxi charging demand is frequent and has the potential for regulation.In this paper,we study the scheduling process of fast charging,taking into account the scheduling feasibility of individuals through time and space aspects,introduces the fast charging virtual load to realize the dynamic change of charging reservation mechanism,and establishes a multi-objective optimization model by using the grid load profile situation and the fast charging monetary cost.At the same time,we also investigate the compensation mechanism based on the value of the power deviation,and the spatial load scheduling by taking into consideration of the balance of the utilization rate of the charging station and the fast charging time cost.In view of the defects of the classical Particle Swarm Optimization(PSO)algorithm,such as premature convergence of particles and that it is easy to fall into the local optimal solution,we propose the Genetic-Particle Swarm Optimization of Normal Distribution Decay Inertia Weight(NDGAPSO)by combining with the crossover mutation mechanism.The overall performance of the NDGAPSO algorithm is proved to be better than other improved PSO algorithms through simulation experiments in terms of solution quality,convergence performance,and running speed.Finally,the algorithm is used to solve the fast charging scheduling model,and the experiment proves that the scheduling optimization research in this paper can effectively take into account the interests of electric taxi charging users and power grid operators.