Abstract: In the background of the new power system，the importance of demand-side dispatchable resources

of the grid for system stability is increasing. As an important dispatchable load resource，an accurate assessment of

electric vehicle（EV）dispatchable potential can effectively improve the safety and stability of the grid. Existing

research has rarely considered the impact of EV user behavior preferences on grid load regulation. Therefore，a

method for evaluating the adjustable potential of EV centralized power stations considering user charging

preferences was proposed. The user charging behavior model based on the membership function（MF）was

established considering external conditions and their own behavioral preferences when charging EVs. And the long

short-term memory（LSTM）neural network algorithm was combined with MF to evaluate the adjustable potential

of charging stations. Finally，the coupling relationship between EV users and load dispatchable potential was

analyzed through actual charging station calculations，which verifies the effectiveness of the proposed method for

load dispatchable capacity assessment and provides theoretical support for EV adjustable load participation in

demand response services such as peak shaving and valley filling.

      Key words: electric vehicle（EV）；scheduling potential；user behavior；membership function（MF）；long shortterm memory（LSTM）neural network