Battery charging and discharging scheduling with demand response for an electric bus public transportation system

Abstract

This paper presents a case study of the Penghu bus transportation system wherein all the diesel buses were considered for replacement with battery-swapping e-buses. A genetic algorithm was employed to optimize the battery charging and discharging capacity at different time points during the timeframe, thereby minimizing the total single-day cost of the bus system. Demand response was used to adjust the main transformer load by using the residual capacity of the batteries. However, the resale of electricity increased the total cost because more batteries were required, battery life was shortened due to more charging cycles being performed, and energy was lost. Two cases were used to elucidate this problem. In case 1, the battery charging and discharging schedule was optimized for different maximum residual battery capacities. In case 2, we additionally considered wind and solar power generation, feeder load, and demand response. The results revealed that the total single-day cost in case 2 was 13%–17% higher than that of case 1. Finally, revenue from the resale of electricity was found to be higher than the added cost of conducting demand response when the resale price of electricity was twice that of the electricity rate offered by the power company.