Optimal integration of battery swapping stations for voltage management under uncontrolled electric vehicle charging scenarios

Abstract

Battery swapping stations (BSSs) offer a promising solution for electric vehicle (EV) integration by addressing both user-level and grid-level challenges. For users, BSSs reduce range anxiety through rapid battery replacement. On the grid side, aggregated batteries in BSSs form a sizable energy storage resource that can be utilized to support distribution system (DS) operations. This study proposes a novel optimization framework that leverages BSSs as flexible grid-interactive assets to mitigate the adverse impacts of uncontrolled EV charging, including both arrival-driven and price-driven behaviors, without requiring direct control over charging stations (CSs). A mixed-integer quadratically constrained programming (MIQCP) model is developed based on an AC optimal power flow formulation to minimize voltage deviations, while capturing nonlinear power flow behavior and meeting operational and user-related constraints. Real-world EV-based data is used to represent temporal variations in charging demand. The analysis is performed on the IEEE 33-Node Distribution Test System with 15-min resolution over a 30-hour horizon, representing overnight charging dynamics. Simulation results show that the BSS-based strategy effectively mitigates undervoltage issues and improves voltage uniformity under high and clustered demand. While direct control of CSs through unidirectional smart charging (V1G) and vehicle-to-grid (V2G) operations enhances voltage levels, it introduces greater variability and requires extensive coordination. In contrast, the BSS-centric approach delivers more stable and consistent voltage regulation while requiring significantly lower coordination complexity. Furthermore, sensitivity analyses conducted under varying photovoltaic generation and on the larger IEEE 69-Node Distribution Test System demonstrate the effectiveness and scalability of the proposed model.