Research on intelligent charging and discharging management methods for electric vehicles based on user response modelling

To enhance the controllability of electric vehicle demand response, this paper proposes a dual-path approach for forecasting adjustment potential based on ‘quantifying price elasticity coupled with incentive strategy constraints. First, a time-of-use pricing response model incorporating an electricity price elasticity matrix is constructed to forecast users' self-regulation potential during peak, off-peak, and low-peak periods. Subsequently, integrating Vehicle-to-Grid (V2G) and direct load control, an incentive-based response model is established to minimize net load variance and peak-to-off-peak difference, thereby defining the regulatory boundaries for charging shift and discharge support. Subsequently, knowledge graphs and K-means clustering were integrated to extract user behavioral characteristics and analyze the impact of heterogeneity on available capacity. Finally, the methodology's validity was validated using actual data from Tianjin charging stations through cluster analysis, correlation testing, and capacity assessment. Results indicate that evaluations accounting for users' non-perfectly rational behavior yield more realistic outcomes, providing precise data support for coordinated dispatch of generation, grid, load, and storage within microgrids.