Electric vehicle (EV) is a kind of operation resource with great potential value. In order to describing the reserve capacity of EV clusters, it is necessary to accurately predict its reserve capacity so as to participate in the ancillary service market more effectively. In this paper, Firstly, the machine learning method of long short-term memory (LSTM) recursive neural network is used to predict the EV behavior information in the future period with historical data. Secondly, the fuzzy neural network is used to predict the willingness of EVs to participate in centralized regulation by aggregators (AGG). Finally, the prediction results of the reserve capacity of EV clusters are analyzed through a simulation example, and compared with the real data, the basic error is controlled within 2%. This paper provides a useful reference for EVs to participate in the ancillary service market to provide reserve capacity.
 

electric vehicle; user willingness; operation reserve; demand side response; long short-term memory network