A multi-energy scheduling strategy for orderly charging and discharging of electric vehicles based on multi-objective particle swarm optimization. (April 2021) - Wang, Ning; Li, Bo; Duan, Yan; Jia, Shengling
Highlights: Electric vehicle is introduced as both load and source to power grid. The photovoltaic system is introduced into the multi-energy scheduling system. Total load variance and operation cost minimum are taken as optimization objectives. The urban residential area is taken as the application scenario of the model. Stability and profitability of the system is improved with MPSO algorithm. Abstract: In this paper, a multi-energy scheduling model based on the ordered charging and discharging of EVs in typical urban residential areas is established, and the photovoltaic system is introduced into the model. To improve models with single objective in previous research, the minimum variance of total power load and the minimum scheduling cost are taken as optimization objectives of the model, and the multi-objective particle swarm optimization algorithm is used to solve the problem. To reduce the gap between theoretical results and practical application, the actual power load data of a distribution area in Shanghai in spring and summer is taken, and in the case of different number of EVs and weather conditions, the multi-energy scheduling system is simulated. The results show that: 1) The total power load of the area adopting the scheduling algorithm is significantly less than that of the area with disordered charging, where the growth rate of peak electricity consumption can be reduced by up to 127.2%; 2) With the increase of the number of EVs participating in the scheduling, the profitability of the system increases significantly. When 70 EVs participate in dispatching, the profit is up to 150% higher than that of 10 EVs.