Optimization and Self-Adaptive Dispatching Strategy for Multiple Shared Battery Stations of Electric Vehicles
Jie Yang, Weiqiang Wang, Kai Ma, Bo Yang, Chunxia Dou
Abstract
The fast-growing demand of refueling electric vehicles (EVs) blocks the application and popularization of EVs. Battery swapping provides the EV users with a quick and convenient refueling way. In this article, an aggregative shared battery station (SBS) model is proposed, which is composed of a control center and a group of SBSs. With the SBS, the customers can rent the battery and pay a corresponding fee based on the swapped energy and satisfaction level. In order to enhance the SBS system responsiveness and reconfiguration to meet the changeable customers' battery demand and peak shaving and valley filling task, a two-stage framework for the multi-SBS is designed based on a self-adaptive dispatching strategy. On behalf of the SBS operator, an optimization objective function is established to maximize the operating revenue by optimizing the charging, discharging, and sleeping process of the batteries. Using the genetic algorithm, we perform extensive simulations to validate the optimization model and demonstrate the efficiency of the self-adaptive dispatching strategy. The results suggest that the proposed dispatching strategy is effective for scheduling SBSs to satisfy the EV refueling demand, provide peak shaving and valley filling service, and achieve the revenue maximization.