An Optimization Model for Orderly Charging and Discharging of Electric Vehicles Based on the Grey Wolf Optimization Algorithm

To achieve “carbon peaking” and “carbon neutrality”, transitioning to electric vehicles (EVs) is crucial. However, unregulated EV charging significantly impacts the power grid's peak-valley load difference. This paper proposes a multi-objective optimization model based on Vehicle-to-Grid (V2G) interaction to address this issue. The model aims to meet EV users' travel needs while optimizing charging and discharging times, planning these activities during peak and valley periods to enhance grid stability. Key innovations include: 1) A new optimization model for EV charging and discharging. 2) Using the Grey Wolf Optimization (GWO) algorithm to find the optimal number of EV s for charging and discharging at any time. 3) Excellent performance in stabilizing grid fluctuations and maximizing user benefits. Research shows the model reduces the grid's peak-valley difference and user charging costs by 23.22% and 30.33%, respectively, compared to unregulated charging. Thus, it effectively mitigates grid fluctuations and optimizes the impact of EV charging on grid load while maximizing economic benefits for V2G participants.