Optimized Scheduling for Microgrid-Connected Battery Swapping Stations Using Modified Particle Swarm Optimization

The rapid adoption of Electric Vehicles (EVs) is creating unprecedented demand on power grids, necessitating innovative strategies for energy management. Battery Swapping Stations (BSS) present an efficient alternative to traditional EV charging, enabling quick battery replacement while also functioning as energy storage assets within Distributed Energy Resource (DER) based microgrids. This study explores the integration of BSS into microgrid systems, focusing on optimizing charging and discharging schedules to maximize profitability for microgrid operators. A Modified Particle Swarm Optimization (MPSO) algorithm is proposed, featuring dynamic decision boundary adjustments to enhance power flow management. The study compares the performance of the MPSO algorithm with the Artificial Bee Colony (ABC) algorithm in both Battery-to-Grid (B2G) and non-B2G scenarios. Results reveal that the MPSO algorithm significantly improves power flow optimization, leading to a $27 increase in daily profits for microgrid operators. These findings underscore the potential of advanced BSS scheduling techniques to enhance microgrid efficiency, support renewable energy integration, and improve overall economic outcomes for operators.