Research On Electric Vehicle Charge Discharge Scheduling Based On Improved Particle Swarm Optimization Algorithm

In recent years,environmental pollution and energy crisis are becoming more and more serious.The development and utilization of clean energy has become a research hotspot.Electric vehicle replaces traditional fossil energy with clean and efficient electric energy,which has the characteristics of green environmental protection and energy conservation.The electric vehicle industry is developing rapidly in China.However,a large number of electric vehicles connected to the distribution network will reduce the performance and stability of the power grid and increase the user cost.Aiming at the above problems,a charge discharge scheduling strategy of electric vehicle based on improved particle swarm optimization algorithm is proposed to improve the efficiency of charge discharge scheduling,which is verified by simulation experiments.The main research contents of this thesis are as follows:(1)For V2G(vehicle to grid)technology,that is,the two-way flow of information and energy between electric vehicle and power grid.This thesis expounds the classification,charging mode and scheduling strategy of electric vehicles,and constructs the basic model and Multi-objective optimal charge discharge scheduling model of electric vehicle lithium-ion battery.(2)Aiming at the high cost of traditional electric vehicle charge and discharge scheduling,this thesis designs an electric vehicle charge and discharge scheduling strategy based on simulated annealing particle swarm optimization algorithm on the basis of standard particle swarm optimization algorithm.The time of use price constraint is introduced,and the charging cost is added to the objective function to solve the problem of high user cost.Simulation results show that the scheduling strategy of standard particle swarm optimization algorithm and simulated annealing particle swarm optimization algorithm can reduce the user cost,and the simulated annealing particle swarm optimization algorithm is better.(3)Aiming at the problem that the traditional simulated annealing particle swarm optimization algorithm falls into local optimization in charge and discharge scheduling,resulting in large power grid fluctuation and cost,this thesis proposes two scheduling strategies: electric vehicle charge and discharge strategy based on improved particle swarm optimization algorithm and chaotic mutation small mirror particle swarm optimization,introduces node power balance and single and double number restriction constraints to ensure power grid stability,and takes user discharge income and peak valley difference of power grid load as optimization objects,In order to avoid the local optimization problem of traditional algorithm,the improved particle swarm optimization algorithm simplifies the speed term of particle swarm optimization algorithm,and adopts dynamic inertia weight and Levy flight strategy.Chaotic mutation and micro mirror operation can better promote population evolution.Simulation results show that the improved particle swarm optimization algorithm and chaotic mutation niche particle swarm optimization scheduling strategy can significantly improve the stability of power grid and the enthusiasm of users to participate in scheduling,and the chaotic mutation niche particle swarm optimization can better meet the requirements of user side and distribution network side.