A Bilevel DLMP-Based Framework for the Optimal Operation of Battery Swapping Stations Supporting Congestion Management and Flexibility Provision

Battery swapping stations (BSSs) are considered a promising solution to decouple the impacts of electric vehicle (EV) owner behavior from grid operations by offering fast battery swapping services and introducing new flexibility resources to support distribution system (DS) operation. This study proposes a bilevel optimization framework that optimizes a BSS aggregator’s decisions through a distribution locational marginal price-based pricing mechanism to actively contribute to congestion management. The upper-level problem maximizes the aggregator’s profit from energy services, whereas, in the lower level, the DS operator minimizes energy procurement and load shedding costs employing a second-order cone programming-based optimal power flow formulation. Congestion management is addressed in detail by jointly considering both voltage and line limits. To ensure realistic and user-aware operation, the model accounts for battery degradation, individual battery tracking, and EV user comfort through quality of experience constraints. Empirical EV demand data is generated using kernel density estimation based on real-world charging transactions. The proposed approach removes congestion-induced load shedding on the IEEE 33-Node and 69-Node Distribution Test Systems, reducing benchmark curtailment from 1.13 MWh and 0.35 MWh, respectively, while keeping net BSS revenues positive. A comprehensive sensitivity analysis across varying EV fleet sizes and battery inventories also demonstrates the robustness and practical value of the proposed framework.