Adaptive energy management for battery swapping stations using HMDE-PSO: optimizing charge-discharge control against cyber-physical attacks

Abstract

Battery Swapping Stations (BSSs) are emerging as critical components in smart power systems, offering rapid energy refueling, grid load balancing, and improved battery lifecycle management for electric vehicles (EVs). However, the economic operation and cyber-physical security of BSSs remain underexplored, particularly in microgrids that integrate distributed generation (DG) and face increasing vulnerability to cyber-attacks. This paper presents a novel, adaptive energy management framework that optimally schedules the charge and discharge cycles of BSSs under uncertain EV user behavior and potential cyber-physical disruptions. A key innovation lies in modeling two types of cyber-attacks—power disruption and control hijacking—and embedding their technical and economic impacts directly into the optimization process. To solve this multi-objective problem, a Hybrid multi-objective Differential Evolution–Particle Swarm Optimization (HMDE-PSO) algorithm is proposed, which efficiently balances cost minimization, system reliability, and resilience. The framework is validated using the IEEE 69-bus distribution system, demonstrating substantial improvements: over 40% reduction in power losses, enhanced voltage stability, and lower operational costs compared to conventional methods. This work distinguishes itself by integrating cyber-defense considerations with real-time energy scheduling, providing a comprehensive and resilient solution for future BSS-integrated microgrids.