Multi-Objective Optimized Charging Strategy for Electric Vehicles Based on Demand Response

This paper proposes a two-stage electric vehicle charging optimization scheduling model based on the spotted hyena algorithm. Compared with random charging, this strategy reduces the user's charging fee and smoothes the grid load curve without affecting the user's charging demand. This strategy can effectively alleviate the negative consequences of increased load peak-valley difference and reduced power quality. Through the comparison of simulation results, the load peak-to-valley difference of 300 electric vehicles at 20% penetration rate is reduced by 622.7kW, a drop of 23.47%, which greatly improves the safety and economy of grid operation. The user's total charging cost has also dropped by 116.28 yuan compared with the disorderly charging, a drop of 11.26%, which provides users with a reasonable charging schedule.