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.

I. Introduction (Heading 1)

China's development is booming, and the total number of cars is also increasing day by day. The fuel consumption of traditional fuel vehicles has become a major part of oil consumption. With the increase in the total number of fuel vehicles, the demand for oil has gradually increased, which has exacerbated the depletion of fossil energy. At the same time, the burning of oil exhaust has also increased global air pollution and the greenhouse effect. Therefore, since 2012, the scale of electric vehicle sales has continued to grow. By 2020, China's pure electric vehicle sales have reached 1.115 million from 11,000 in 2012. From 2015 to 2020, the total number of charging piles in China is showing an increasing trend year by year. By 2020, there will be 498,000 charging piles using AC power in public charging piles in China, and there will be 30.9 charging piles using DC power. Ten thousand sets; the total number of personal charging piles is 749,000 sets, and the private pile accessories rate is 68.4%.