Collaborative Optimal Scheduling of Microgrids Incorporating Electric Heavy-Duty Truck Battery Swap Stations
The integration of electric heavy-duty truck battery swap stations (BSSs) into microgrids introduces a bidirectional coupling between renewable generation volatility and swap demand that existing dispatch frameworks fail to capture. This study formulates a tri-layer collaborative optimal scheduling model spanning the microgrid, the battery swap station, and the heavy-duty truck fleet. The microgrid layer maximizes daily revenue by co-optimizing microgas turbine, battery storage, wind, and photovoltaic outputs, with CPLEX resolving the mixed-integer linear program. The BSS layer adjusts service fees to influence truck arrival rates, thereby reshaping the station load profile to track renewable generation. The truck layer responds to fee signals by autonomously selecting swap times, with a 15-minute discretization interval. An antelope optimization algorithm solves the BSS-truck subproblem, and the two layers iterate until convergence. The framework is validated against a case study, demonstrating that fee-mediated demand response reduces curtailment and improves supply-demand balance. The model addresses a critical gap: prior BSS scheduling treated arrival rates as exogenous and ignored renewable fluctuations, while truck swap decisions ignored station service capacity. By internalizing both signals, the proposed architecture achieves coordinated optimization without centralized control over vehicle behavior.