Electric vehicles have the advantages of zero carbon emission, high energy efficiency and simple structure, and have been widely used today. Under the control of V2G, electric vehicles can be used as loads and distributed power sources, and become participants in the operation of auxiliary systems. Aiming at how to evaluate the load states and controllability of electric vehicle charging stations, this paper proposes a load sensing method for electric vehicle charging stations based on unsupervised learning and non-intrusive load monitoring. The proposed load sensing method firstly establishes the load sensing model according to the unsupervised learning strategy, and then uses the neural network model of the supervised learning strategy to evaluate the available regulation capability, so as to realize the load perception and regulation capability evaluation of the electric vehicle charging station. Through the verification of simulation data, it is proved that the proposed method has good performance in the evaluation of load perception and controllability of electric vehicle charging stations, which provides a feasible solution to the practical implementation of auxiliary responses by electric vehicles.

NILM, Deep learning, auxiliary service, electric vehicles, flexibility evaluation