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This paper proposes an innovative approach to optimizing the operations of a central battery swapping station (BSS) and its affiliated mobile battery swapping stations (MBSS) in the urban environment. The principal aim is to maximize the profit of the BSS, focusing on enhancing revenue through energy sales to the grid and effective battery swapping operations, facilitated by MBSS that are strategically deployed across Berlin, Germany. The decision-making paradigm integrates spatial considerations based on the population distribution of different regions, thus predicting their battery swapping demand. To accommodate this demand, MBSS are loaded with a determined number of batteries. As these batteries are depleted, MBSS return to the BSS to deposit empty units and procure fully-charged ones. The BSS, in its operational dynamics, holds these depleted batteries, strategically charging them based on dynamic energy prices to optimize grid sales and increase revenue. A mixed integer programming model has been formulated and developed to offer a systematic solution to this multifaceted problem. Results show that Case Study I prioritizes battery swap operations, while Case Study II allocates more capacity to energy sales. Ultimately, Case Study I achieves higher profitability than Case Study II, despite the latter’s increased energy sales revenue. The findings presented here not only pave the way for a more efficient electric vehicle (EV) adoption infrastructure but also underscore the potential financial benefits of such a system when interfaced with the power grid. • Strategic deployment of MBSS across the city, orchestrated by the central BSS. • Optimizing the positioning of MBSS and the charging cycles of central BSS. • The optimization model for contributions of both BSS and MBSS to ancillary services. • The holistic approach for the important role of BSS and MBSS in the energy ecosystem.
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Kocer et al. (2025) studied this question.
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