Research On V2G Scheduling Strategy Of Electric Vehicles Based On Trip Chain And Cumulative Prospect Theory

In the context of global environmental pollution and energy crisis,many countries have increased the promotion of advanced automotive technology,such as electric vehicles(EVs).The penetration of electric vehicles is steady increasing,gradually replacing the role of conventional vehicles(CVs).Due to the driving characteristics of electric vehicles,the random and intermittent nature of the charging load will have a significant impact on the stability of the power grid.Meanwhile,the energy storage characteristics of electric vehicles also bring new opportunities for the schedule and operation of the power grid.In response to the above problems,scholars have modeled the driving characteristics of electric vehicles,analyzed the charging behavior of electric vehicles,and study the corresponding electric vehicle schedule strategy.However,due to the current scarcity of actual EV travel data,most studies have adopted stochastic modeling for EV travel behaviors,which may not be consistent with the actual situation;when studying the charging behavior of EVs,only a few factors to be consider,and the model is simplified,without considering the multiple factors that influence charging behavior and detailed analysis of users’ own intentions;when studying EV Schedule strategies,EV users’ acceptance and participation of the schedule plan have not been considered.In view of the above situation,this article has conducted the following three aspects of research:To tackle the lack of data of EV trip,the study analyzed the 2017 National Household Travel Survey(NHTS)database.By pre-processing and filtering the missing values and outliers in the database the unique and real trip chains of CVs and EVs in different regions of the United States were obtained.Consider NHTS 2017 data and future charging facility development conditions,use the Market Acceptance of Advanced Automotive Technologies(MA3T)model to analysis drivers’ purchase desires for EVs in future scenarios,and obtain the actual population of EVs.Future EV measure market scenario is established.Finally,the corresponding EV trip chain will be obtained,which provides a solid data foundation for subsequent research on charging behavior and grid scheduling.In most current research,the models of EV charging behavior are simple,which does not take the multiple factors that influence EV charging behavior into account.Based on the rich data provided by the trip chain in future mature EV market scenarios,considering the actual charging process of EV users,use cumulative prospect theory to analyze the user’s charging decision,which consider multiple factors and risk preferences of the EV users.The charging decisions of EV users at home,at work and other public places will be obtained.The temporal and spatial distribution of the charging load of EVs have been showed in the simulation results.The key parameters that influence charging decisions have been analyzed.The risk appetite of EV users led to a peak EV charging load increase of about 4.2%.In response to the scheduling strategy not to adequately consider the willingness and risk attitudes of EV users,the proposed cumulative prospect SOC(state of charge)constraint based on the cumulative prospect theory fully consider the EV users’ own willingness to participate in dispatching as well as risk attitudes.Based on trip chain data,the charging and discharging model for EVs in the future mature EV market scenario is built.The objective functions are optimized to solve the maximum schedulable capacity of the EV cluster and the optimal dispatch result for each EV,respectively.Simulation results show that under the proposed strategy,the maximum schedulable capacity and effect of participating in peak load shifting are slightly reduced.