Joint charging scheduling of electric vehicles with battery to grid technology in battery swapping station

Abstract

With the proposal of carbon peak and carbon neutrality policy, electric vehicle as a clean new energy transportation tool has attracted more and more attention. Battery swapping station (BSS) is an alternative method to charge electric vehicles (EVs). This paper summarizes the development and present situation of battery swapping station and analyzes the distribution probability model of EVs’ arrival based on the historical data of electrical swapping station, which approximately obeys Poisson distribution. The convergence accuracy of traditional particle swarm optimization algorithm is improved by combining the particle swarm optimization algorithm with the immune algorithm, which can find the global optimal solution more easily. Then an economic scheduling method for battery swapping station based on monte carlo simulation was proposed, and the function of BSS as an energy storage device to power grid (B2G) is analyzed. Next a mathematical model of multi-objective joint optimization of battery swapping station based on B2G technology is established, considering the revenue of operator, RMS of grid load and peak–valley difference as functions. Finally, the simulation analysis of this model is carried out. The optimization results of ordered and disordered charging scheduling with B2G and without B2G are compared to verify the effectiveness and superiority of this model. It can be seen that B2G root mean square and peak–valley difference decreased by 15.72% and 14.63% respectively, and the revenue of battery swapping station increased by 1.4%. Therefore, the application of B2G technology in orderly charging scheduling is better than that without B2G technology.

Keywords

• New energy vehicles
• B2G
• Joint optimization scheduling
• Battery swapping station
• Adaptive immune particle swarm optimization