Research On Cloud Edge Collaborative Ordered Charging Method For Electric Vehicles Based On Dynamic Time Of Use Tariff

Due to the randomness of the charging behavior of electric vehicle users,large-scale electric vehicles will be randomly connected to the power grid for charging,which will lead to issues such as decreased stability of power grid operation,increased peak valley load differences,and intensified harmonic pollution.At the same time,with the explosion of the number of electric vehicles,the data that needs to be collected,transmitted and processed will rapidly increase when the electric vehicles are charged in an orderly manner.The processing of a large amount of data will bring about problems such as time delay and curse of dimensionality,so effective methods must be adopted to solve them.With the rapid development of the Internet of Things and communication technology,cloud edge collaborative architecture can be applied in many fields.This architecture not only has huge computing resources in the cloud center,but also has the advantage of being close to the user side on the edge,improving the real-time performance of data processing.Based on this,this article proposes a cloud edge collaborative orderly charging method for electric vehicles under dynamic time of use pricing.The specific research content is as follows:Firstly,the disordered charging characteristics of electric vehicles are studied.Analyze the charging characteristics of electric vehicle batteries,the charging mode of electric vehicles,and the travel habits of electric vehicles.Using Monte Carlo method to simulate the electricity demand of electric vehicles,the disordered charging load curve of electric vehicles is obtained;Secondly,it analyzes the cloud side collaborative architecture and its application in electric vehicle charging.Then,considering the charging demand and grid load of electric vehicles,a dynamic time-of-use pricing method for orderly charging of electric vehicles is proposed.According to the load margin of the power transformer in the area where the charging station is located after removing the conventional load,the method determines the time-of-use charging tariff period for electric vehicle users,and realizes dynamic pricing by changing the charging tariff period for electric vehicle users.The time-of-use electricity price period for electric vehicle charging is calculated using the ergodic method,and the charging demand of electric vehicle is simulated using the Monte Carlo method.The effectiveness of the proposed method is verified through simulation calculations.Finally,a sequential charging method is proposed that considers both electric vehicles in motion and electric vehicles that have arrived at the charging station.The driving electric vehicle uses the Dijkstra algorithm to find the optimal path for the electric vehicle,while considering the time delay generated when the electric vehicle transmits information to the edge and center clouds,a cloud edge collaborative task offloading strategy is established.The goal function is to minimize the variance of central cloud load and maximize the operating revenue of edge cloud,achieving a win-win situation among the power grid,edge cloud operators,and electric vehicle users.