The current rapid development and large number of applications of electric vehicles make it necessary to further strengthen the research on the state sensing and demand responsiveness of electric vehicles for residential measurements. Based on the non-intrusive load monitoring technology and deep learning algorithm, the user's power data is processed to obtain the start and end time of possible EV charging behavior, and then the user's power magnitude data and the obtained possible charging start and end time data are used as the input data of BP neural network to identify the EV charging behavior and establish the load sensing model of residential EVs. Based on the load perception model of electric vehicles, we identify the electric load data of residential measurement, obtain the charging status information such as power and electricity of electric vehicles of users, and use the status information as the data base to evaluate the demand response capability of residential measurement electric vehicles. Finally, the simulation modeling of the above method shows that the method can identify the charging status of electric vehicles more accurately and effectively evaluate the ability of residential measurement electric vehicles to participate in demand response.

Electric Vehicles;Non-intrusive Load Monitoring;Demand response (DR)