Research On Orderly Charging Strategy Of Electric Vehicle Based On Power Distribution

As a new type of clean and energy-saving transportation,new energy electric vehicles have gradually replaced the traditional fuel vehicles.The continuous popularity of electric vehicles leads to the increase of charging demand,and the uncertainty of users’ charging behavior and charging time and space makes the charging load very random and increases the difficulty of power grid regulation.In this thesis,an in-depth analysis of the impact of unordered charging of electric vehicles in residential areas on residential electricity consumption is conducted,and an intelligent charging pile control system with an embedded orderly charging control strategy is designed to assist charging stations in the operation of charging facilities.The system can control the charging load by distributing different charging power to the charging terminal.The main contents of this thesis are as follows:(1)Study the regulation strategy of orderly charging of electric vehicles.The thesis introduces and analyzes the common ordered charging strategy,the Monte Carlo method used in the charging load prediction and the Multi-objective Particle Swarm Optimization used in the power distribution strategy.The principle,model and evaluation index of MOPSO algorithm are briefly introduced to provide theoretical basis for subsequent research.(2)In terms of charging load prediction,a charging load prediction model based on users’ charging demand and dynamic changes of battery charging power is proposed.The charging load prediction model was optimized by studying the dynamic change rule of charging power of vehicle battery and combining with users’ charging demands.The model optimized in this thesis simulates that the peak value of the total load curve of the charging load decreases and the curve is relatively gentle.Meanwhile,the predicted total charge quantity of the whole day is close to the actual total charge quantity,which provides the basis for subsequent orderly charging control strategy.(3)In terms of orderly charging regulation strategy,adaptive Angle region division method and dual external archive method were used to improve MOPSO algorithm,and it was applied in the orderly charging regulation strategy based on power distribution.In order to solve the problems of the traditional MOPSO algorithm,such as lack of population diversity and poor convergence effect,an external file was added to preserve as many particles as possible,so that the Pareto Front(PF)obtained by the optimized MOPSO algorithm could be more uniform,and the dynamic variable inertia factor was adopted to further improve the convergence of the algorithm.The optimized MOPSO algorithm is used to calculate the optimal power distribution,and the charging peak of the charging facilities is regulated and the residential peak power consumption is staggered,so that the combined load of the two is lower than the restricted load of the residential area,so as to ensure the power safety of the residential area.(4)Design and develop an intelligent charging pile control system embedded with orderly charging control strategy.The intelligent charging pile control system is divided into intelligent charging pile control software and intelligent charging service platform.The bottom algorithm of the intelligent charging service platform is embedded with orderly charging control strategy to monitor the total load of residential areas and calculate the optimal charging scheme.The charging terminal’s charging power is regulated by intelligent control software of charging pile to meet the actual requirements of orderly charging control strategy in residential areas.The intelligent charging pile control system constructed in this thesis effectively regulates the charging load of charging piles through power distribution,assists the power grid in orderly charging regulation,reduces the load fluctuation of the power grid,and improves the charging efficiency.