Research On Distributed Power Optimization Configuration Of Electric Vehicles

In recent years,facing the increasingly serious energy crisis and environmental problems,the problems caused by the traditional centralized power supply mechanism of large power grid are gradually attracting people’s attention.As a new way of energy utilization,distributed generation and electric vehicles have natural advantages in solving the problem of energy shortage and pollution that governments around the world are paying attention to.However,the interconnection between distributed generation and electric vehicles poses new chal enges to the security,efficiency and reliability of distribution network.The impact of distributed generation on power grid mainly depends on location and capacity.Considering the uncertaint y of distributed generation and electric vehicles,it aims to make distributed generation run intelligently and safely in a good society and a good energy system.This is very important.This paper presents a probabilistic power flow analysis method which combines the semi invariant method,Copula theory and Gaussian mixture approximation method.This method overcomes the shortcoming that the existing series expansion method can not approximate the multimodal probability distribution.The mixture of Gaussian,non Gaussian and discret e probability distributions of input bus power is considered.Without using any series expansion method,the probability distributions of multimode bus voltage and line power flow associated with these inputs are obtained accurately.At the same time,mult i input correlation is considered.The performance of this method is verified in IEEE14 bus system and IEEE57 bus system.This method accurately establishes the multi-modal distribution of the expected random variables,and the result reduces the running time by 76% compared with the gram Charlier method.In this way,the annual investment cost,power purchase cost and the most effective power loss cost can be determined.In the model solving algorithm,the application of differential algorit hm based on the successful history will introduce the center mutation and mutation object to improve the acquisition rate.In addition,the uncertainty of distributed generation and electric vehicle is ful y considered in practical application.Based on the history of global success,the probability of power flow calculation is integrated into the adaptive differential algorithm to obtain the final optimal al ocation scheme.Finally,the improved semi variable is verified by IEEE14 and IEEE57 bus systems.According to the successful history of cec2010 and cec213,the convergence characteristics of the improved adaptive differential development algorit hm are analyzed.Taking ieee33 bus system as an example,the optimal algorithm configura t io n scheme in decentralized production of electric vehicles is given,and the convergence and voltage distribution characteristics of the optimized nodes are compared.The simulation results show that the algorithm has the advantages of high convergence accuracy,short calculat io n time and high efficiency,which provides a new solution for the decentralized planning of electric vehicle production.