Research On Charging Strategy Of Electric Vehicle Based On Internet Of Things

Vehicle emissions are one of the major causes of global warming.In the context of a low-carbon economy and the continuous growth of vehicle ownership,reducing carbon emissions from motor vehicles is the key to achieving the goal of carbon neutrality and carbon peaking.Electric vehicles(EV)powered by electricity can significantly reduce greenhouse gas emissions compared to traditional fuel vehicles and can optimize energy allocation accordingly,and have an important role in the energy consumption revolution.However,as the number of electric vehicles continues to rise,it will bring new challenges to the planning,regulation and operation of the power network.Firstly,the charging behavior of EVs is disorderly,and the large-scale access is more likely to lead to significant peak-to-valley differences,power losses and more harmonics in the electricity load;secondly,since the energy storage function of EVs has not been fully utilized,it is beneficial to improve the stability of the power system by enhancing the intelligence of charging and discharging and making scientific interaction with the power grid.This paper is dedicated to solving the problem of high peak-to-valley load difference caused by disorderly charging of EVs,which affects the stability of the power grid,by using convex optimization method and Io T technology to research on EV charging strategy and EV management system,and the main work is as follows:(1)A real-time optimal scheduling scheme for electric vehicle charging and discharging is proposed.Taking into account the interests of both the grid and the users,the charging and discharging strategy is obtained by establishing an EV dispatching model,analyzing the grid node acceptance capacity with the grid loss sensitivity as the index,and solving the real-time tariff based on the grid load by a dynamic time window planning second-order convex optimization calculation method in real time.The effectiveness and feasibility of the proposed method are verified in the improved IEEE 33-node distribution network.The proposed method can effectively reduce charging cost and network loss cost,and has better real-time flexible scheduling performance.(2)An electric vehicle charging management system has been developed.The system uses RFID(Radio Frequency Identification)technology to identify electric vehicles so as to obtain information on the charging capacity of charging stations.Based on Socket communication technology and TCP/IP(Transmission Control Protocol/Internet Protocol)communication protocol,the acquired vehicle information is transferred to a SQL Server 2008 r2 database after data parsing,which enables the monitoring and collection of charging power data from the charging stations.