Estimation of electric vehicle schedulable capacity considering vehicle-road-station interaction and users' bounded rationality

Electric vehicles (EVs) can rapidly respond, and accurate estimation of EVs’ schedulable capacity is a premise for exerting EVs’ value of flexibility. A schedulable capacity estimation model considering users’ bounded rationality was proposed based on the practical background of information interaction among EVs, charging stations, and traffic networks. Firstly, the temporal-spatial distribution and charging demand of EVs in traffic networks was forecasted according to trip chain simulation. Secondly, a vehicle-road-station interaction model, in which queuing factors were considered, was established to predict the time and position that the EV connects to the power grid. Then, the mutual adjustment relationship between the cost of energy consumption and charging or discharging time was analyzed to characterize users’ bounded rational energy consumption behaviors for estimating EVs’ schedulable capacity. Finally, the schedulable capacity of EVs was estimated by simulation based on a certain traffic network, the result shows the advantages of the proposed method in accurately estimatin g EVs’ schedulable capacity.