Assessment Of Dispatchable Potential Considering The Credibility And Limited Rationality Of Electric Vehicle Users

With the advancement of the "Dual Carbon" energy strategy,the Electric Vehicle(EV)industry has been vigorously supported from the policy,supply and demand sides,resulting in its continuous expansion.Electric vehicles possess remarkable mobility and Vehicle to Grid(V2G)capabilities,which enable them to participate in emergency auxiliary services such as temporary flow adjustment of the power grid,thus enhancing system reliability and stability.However,whether its dispatch capacity can meet the requirements of the power grid often determines the quality of auxiliary services.Therefore,it is crucial to accurately determine the dispatch potential of electric vehicles when formulating dispatching measures.Taking into account the limited rationality of users,this paper studies the influence of user complex social relationships and mutual learning on charging and discharging selection,in order to provide useful references for the power grid to determine the EV dispatch potential.To begin with,in order to investigate the impact of EV uncoordinated charging on the power grid,the characteristics of electric vehicles under different types and charging modes were analyzed,the main factors affecting the charging and discharging load were studied,and various distribution functions reflecting the user’s travel rules were obtained by fitting the statistical data.Furthermore,empirical analysis was conducted by taking peak-valley ratio as the index to explore the great influence of EV uncoordinated charging on the load characteristics of the power grid under different penetration rates.In addition,in order to realize the orderly participation of EV load in power grid auxiliary services,the corresponding main framework and communication framework were established.Subsequently,in order to accurately calculate the schedulable capacity,a demand response evolution game model considering user credibility in social networks was proposed to address the factors affecting user-side demand response.It simulates the information exchange and strategy learning characteristics of rational users in the real world in response to electricity prices or incentives,especially the abnormal behavior characteristics of node users,and verifies the actual operability and effectiveness of the model through case simulation.The influence of user social network factors and incentive electricity prices on user demand response decisions were analyzed.Lastly,based on the research of demand response evolution game process within the EV user group,the strategic interactive behavior between the charging station agent and the EV user group was analyzed in order to obtain the maximum schedule potential.Combined with the Wardrop first equilibrium principle and considering time delay,an EV shortest travel time model was established.Taking into account the limited rationality of users,the time delay of EVs arriving at the charging station,and the user activity type,the charging and discharging scheduling potential was evaluated to provide the upper limit for the scheduling capability of the charging station at different times.