Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Abstract

Across the globe, the adoption of electric vehicles (EVs), particularly in mass transit systems such as electric buses (E-bus), is on the rise in modern cities. This surge is attributed to their environmentally friendly nature, zero carbon emissions, and absence of engine noise. However, the charging of E-bus batteries could impact the peak demand on the main grid and its overall serviceability, especially when numerous batteries are charged simultaneously. This scenario may also lead to increased energy costs. To address the previously mentioned issue, battery swapping is employed at the charging station in lieu of conventional battery charging. In this paper, the battery swapping approach is utilized to establish the optimal battery charging schedule for E-buses, taking into account both energy costs and the peak-to-average ratio (PAR). The E-bus battery swapping stations incorporate photovoltaic (PV) power generation as their energy source. Three metaheuristic algorithms-namely, the binary bat algorithm (BBA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)-are employed to identify the optimal conditions. The simulation results demonstrate that integrating the optimal battery charging schedule with a PV power generation system in an E-bus battery swapping station can effectively lower energy costs and the PAR when compared to traditional battery charging methods at charging stations. The optimal charging schedule derived through the GWO technique outperforms those obtained from the WOA and BBA techniques. This resulted in a notable reduction in peak demand from 758.41 to 580.73 kW, corresponding to a 23.43% decrease in peak demand. The integration of the GWO with battery charging scheduling and PV installation resulted in a significant 27.63% reduction in energy costs. As per the simulation results, an optimized battery swapping schedule has the potential to lower energy costs and enhance serviceability for the E-bus battery swapping station.INDEX TERMS Battery charging scheduling, battery swapping stations, electric buses, metaheuristic algorithm, peak-to-average ratio. I. INTRODUCTIONAdvancements in electric vehicle (EV) and battery technology have spurred increased usage, supported by governmental advocacy for clean energy, prompting a shift from internal combustion engine cars to electric vehicles. The development of smart cities includes the expansion of electric bus mass transit systems. Major cities are witnessing a growing preference for electric vehicles over traditional internal combustion engine vehicles, particularly in the form of electric buses (Ebus), offering reductions in diesel fuel usage and air pollution

Optimal Scheduling of Microgrids Considering Offshore Wind Power and Carbon Trading

“…Modern heuristic optimization methods are powerful and effective tools for solving non-benign optimization problems and have proven their effectiveness in many fields [20,21]. Some modern heuristic algorithms are also frequently applied in the field of power system optimization, such as Particle Swarm Optimization (PSO), the genetic algorithm (GA), and the Salp Swarm Algorithm (SSA).…”