Optimal Charging and Discharging of Electric Vehicles within Campus Microgrids

Abstract

Electric vehicles (EVs) are being increasingly integrated into the electric grid as a result of their popularity and people's travel patterns. Especially in Universities, the campuses have a large number of people in a short period of time, which increases the electric vehicles' charging load. In this paper, an optimized charging and discharging method of EVs within a campus microgrid is proposed, which would significantly improve grid operation stability. This article used the disorderly charging of EVs using probability density functions and the Monte Carlo (MC) method. By analyzing the EVs owners' willingness to charge in the campus area, the distribution of start-of-charge (SOC) of the vehicles, and the EVs' stay time on campus, the amount of charge is estimated. To achieve a suitable charging scenario for a campus microgrid, the analysis parameters are coded and optimized using the Genetic Algorithm (GA). According to the characteristics of university class-times, it is expected that the charging of vehicles entering campus will cause peak-load periods. During the time period from 6:00 to 24:00, 1,000 EVs that follow these plans will exist. The GA algorithm is used to allocate the best charging time for the EVs, thereby adjusting the peak-to-valley gap of the campus power grid. Further, four types of GA are compared and it is realized that the input parameters have a significant impact on the outcomes. The results demonstrate the impact of optimized charging on improving microgrid stability and reducing charging costs.