Combined real-time and scheduling methodology for operation of battery swapping stations considering energy flexibility and grid support

Battery Swapping Stations (BSSs) offer a viable alternative to Electric Vehicle Charging Stations (EVCSs) in electric mobility. However, due to their higher investment costs, primarily associated with battery inventory costs, their economic and technical feasibility still lacks improvements for a wider adoption. In contrast, the electric micro-mobility sector, with smaller EVs, simpler battery requirements and charging complexity, emerges as a promising field for BSS studies and applications. Therefore, this paper presents a methodology, termed MH-RB-ARW, for optimizing BSS operations within electric micro-mobility while supporting grid services during Flexible Response to Demand (FRD) events. The approach integrates rule-based (RB) algorithms and a meta-heuristic (MH) optimizer within an adaptive rolling window (ARW) approach. This enables real-time coordination of BSS operations for scheduled and opportunistic users, aligning preparation (short-term) and operation phases. FRD events are classified into power absorption (PA), where the BSS absorbs excess grid energy (valley filling service), and power injection (PI), where the BSS injects energy into the grid (peak shaving service), both adhering to predefined demand contracts. While supporting the grid, the BSS simultaneously manages battery swapping operations. RB algorithms address real-time and scheduled requests, while the MH optimizer minimizes recharging costs for depleted batteries (DBs). Case study results demonstrate that the proposed methodology allows the BSS to provide demand response services without compromising its primary operations. Furthermore, the MH optimizer significantly reduces energy purchase costs for recharging DBs, enhancing economic benefits during both PA and PI events.