Aggregation Control Strategy for Battery Swapping Station Clusters in Response to Battery Swapping and Grid Regulation Needs

The participation of battery swapping station (BSS) clusters in grid regulation is significantly constrained by the spatio-temporal uncertainty and climate sensitivity of electric vehicle (EV) demand. To address these issues, this paper proposes an aggregated scheduling method that integrates demand forecasting and rolling optimization. First, a demand forecasting model is established by considering seasonal climate and users’ range anxiety. On this basis, a “day-ahead bidding and intra-day tracking” two-stage scheduling framework is constructed. In the day-ahead stage, the optimal bidding power of the cluster is determined for minimizing the overall cluster cost. In the intra-day stage, taking the bidding power as the tracking index, the demand distribution scheme and station charging/discharging strategies are synergistically optimized to minimize the operational costs. Furthermore, for real-time EV swapping requests, suitable BSS nodes are recommended based on the distribution scheme. To address the stochasticity of user rejection, rolling optimization is applied for real-time adjustments, ensuring reliable grid response and service quality. Finally, a case study using real operational data verifies the effectiveness of the proposed model.