Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology

With the rapid increase in electric vehicle (EV) ownership, the uncertainty of EV charging demand has become a significant concern, especially in distributed photovoltaic (PV) power distribution networks (DNs) with high penetration rates. This growing demand presents challenges in meeting the needs of EV owners and grid charging/discharging stations (GCDSs), jeopardizing the stability, efficiency, reliability, and sustainability of the DNs. To address these challenges, this study introduces innovative models, the anchoring effect, and regret theory for EV demand response (DR) decision-making, focusing on dual-sided demand management for GCDSs and EVs. The proposed model leverages the light spectrum optimizer–convolutional neural network to predict PV output and utilizes Monte Carlo simulation to estimate EV charging load, ensuring precise PV output prediction and effective EV distribution. To optimize DR decisions for EVs, this study employs time-of-use guidance optimization through a logistic–sine hybrid chaotic–hippopotamus optimizer (LSC-HO). By integrating the anchoring effect and regret theory model with LSC-HO, this approach enhances satisfaction levels for GCDSs by balancing DR, enhancing voltage quality within the DNs. Simulations on a modified IEEE-33 system confirm the efficacy of the proposed approach, validating the efficiency of the optimal scheduling methods and enhancing the stable operation, efficiency, reliability, and sustainability of the DNs.