Flexible regulation and cooperative optimization method of electric vehicle charging load for active distribution networks

Driven by the dual-carbon goals, the number of electric vehicles (EVs) in China has maintained a rapid growth. Large-scale uncoordinated charging of EVs poses severe challenges to the safe and stable operation of active distribution networks (ADNs). To realize the cooperative optimization between EV charging loads and ADNs, this paper proposes a flexible regulation method for EV charging loads based on demand response. First, the characteristics of EV charging behaviors are analyzed, and an EV charging load model covering both random and coordinated charging modes is established. The Monte Carlo method is adopted to perform sampling simulation of charging loads. Second, aiming at the defects of traditional NSGA-II algorithm, such as easy trapping in local convergence and uneven solution distribution, an improved NSGA-II algorithm based on chaotic initialization and elite selection is proposed. On this basis, a source-load cooperative optimization model is constructed, which takes the maximum comprehensive user satisfaction and the minimum net load variance of ADNs as optimization objectives, with constraints including battery state of charge and the lower limit of user comfort. Finally, an ADN in a residential area of China is taken as a numerical example to verify the regulation performance of EV charging on working days and rest days respectively. The results show that the proposed method reduces the peak-valley difference of ADN load from 1002.0 kW to 329.8 kW on working days, with a reduction rate of 63.5%. The charging cost for users is cut by 29.98%, and the comprehensive user satisfaction increases by 5.59%. The proposed method effectively improves the accommodation capacity of clean energy and the operational stability of power grids.