492 / 2024-04-25 16:21:56

Citywide Orchestration of Electrical Vehicle Battery Swapping

battery swapping station; adaptive distributionally robust optimization; location and capacity planning; orderly swapping

To efficiently integrate the operation of electric vehicles (EVs) and battery swapping stations, we propose an orderly swapping strategy in which EVs carry out battery swaps at designated swapping stations in response to swapping demands, with real-time scheduling managed by the service platform. Based on this strategy, we characterize the battery swapping station location and capacity planning problem as a two-stage distributionally robust optimization model. This model presents challenges with bilinear terms involving decision variables and uncertain variables, and sums of piecewise linear functions in the second-stage problem. Notably, we demonstrate that the second-stage problem is convex concerning the uncertain variable and reveals a distinctive structure in the optimal solution of its dual problem. These model properties guide us in designing an efficient column-and-constraint generation algorithm for solving it. We also propose two alternative strategies designed for specific scenarios, reformulating them as mixed-integer second-order cone programmings. In a numerical study, we validate our algorithm's efficacy using classic transportation data and compare the performance of the three strategies with real transportation data from Beijing. Our findings indicate that the Orderly strategy surpasses the other two strategies regarding flexibility and demonstrates economies of scale as the battery swapping scale increases. This flexibility becomes more pronounced as the service rate decreases and the grid capacity increases. Furthermore, the enhanced grid capacity results in an imbalanced battery distribution and the significant technological advancement eliminates the possibility of battery shortages.