Agent–cellular automata model for the dynamic fluctuation of EV traffic and charging demands based on machine learning algorithm

Electric vehicles (EV) comprise one of the foremost components of the smart grid and tightly link the power system with the road network. Spatial and temporal randomness in electric charging distribution will exert negative impacts on power grid dispatch. Existing research focuses mainly on mathematical inferences from statistical data, and the dynamic movement of an individual vehicle traveling in a traffic system is rarely taken into account. Machine learning algorithm can take the EV dynamic condition into consideration. Based on machine learning algorithm, this paper proposes a charging demand simulation method based on the Agent–cellular automata model to describe the changes in location and the state of charge of a moving EV. CRUISE software is used to analyze power consumption in different scenarios. Then, the Monte Carlo algorithm models the dynamic fluctuation of EV traffic and charging demands. Case studies are conducted on a typical composite system consisting of a 54-node distribution system and a 25-node traffic network, and the simulation results demonstrate the effectiveness of the proposed method.