With the large-scale integration of Electric Vehicles (EVs), Great challenges to power system stability have been posed by the stochastic nature of EV charging behavior. Consequently, effective regulation of EV charging behavior is urgently required. However, existing research fails to comprehensively consider the subjective decision-making attributes of EV users. Therefore, an electric vehicle demand response charging scheduling strategy based on the multidimensional semi-cloud model is proposed to account for the impact of subjective decision making. Firstly, clustering method is applied to analyze the behavioral characteristics of EV users. The subjective factors of users are quantified using the Decision-Making Trial and Evaluation Laboratory- Weber-Fechner algorithm. Secondly, the user response willingness model is built through the multidimensional semi-cloud model. Finally, a two-dimensional mapping between user response willingness levels and user cluster density is established. Based on this mapping, a time-phased charging schedule strategy for EV users is proposed. Simultaneously, the objective functions of minimizing charging costs and grid-side load fluctuations are established. The collaborative optimization between grid and user sides is achieved. Simulation results demonstrate that the proposed method can effectively reduce the peak-to-valley load difference, decrease user charging costs, and improve the load curve stability of the demand side.
Yao et al. (Thu,) studied this question.