Research On Electric Vehicle Charge And Discharge Electric Energy Measurement Based On Multi-objective Optimization

When an electric vehicle(EV)is connected to a charge-discharge network as a single-phase load,due to the randomness and flexibility of its access point selection,it will inevitably cause a series of problems,such as three-phase unbalance,high line loss of active power and abnormal power electric energy measurement in the charge-discharge network,etc,which cannot be accurately measured by traditional power metering methods.In order to improve the accuracy of electric energy measurement,aimed at the different selection of access points of electric vehicles will lead to different three-phase unbalance degree and line loss of charge-discharge network,thereby affecting the accuracy of electric energy measurement.This thesis uses multi-objective optimization method to optimize it to improve the accuracy of electric energy measurement in electric vehicles charge-discharge process.Firstly,this thesis introduces the characteristics of EV access to charge-discharge network,and the impact of its unique charge-discharge behavior on power metering and charge-discharge network.The root causes of three-phase unbalance in EV charge-discharge network are analyzed,and the calculation process of electric energy metering error in three-phase unbalance is given.The existing three-phase unbalanced control measures and control algorithms are summarized,which lays a foundation for three-phase unbalance governance thereby achieving accurate measurement of electric energy.Secondly,the development history of multi-objective optimization algorithm and the related concepts of multi-objective optimization problem are summarized.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm for the subsequent optimization of electric energy metering is introduced in detail.According to the typical layout of EV charging stations,a three-phase current unbalance model and a line loss model for electric energy metering optimization are established,which prepared for subsequent multi-objective optimization.Finally,the multi-objective particle swarm optimization algorithm(MOPSO)is applied to solve the established electric energy metering optimization model,and the optimal solution set is used to guide the orderly charge-discharge of electric vehicles.The three-phase unbalance degree of the charge-discharge network is reduced to the allowable range specified in the national standard.In this way,the electric energy of electric vehicles can be accurately measured.At the same time,the line loss of the charge-discharge network can be maximum reduced,and a win-win situation can be achieved between the power supply enterprises and EV users.