As premium regulation resources in power systems

electric vehicle (EV) battery swapping stations can generate ancillary service revenues through participation in electricity spot markets while fulfilling battery replacement demands. Nevertheless

the coupling effect between the temporal stochasticity of battery swap demands and multi-timescale pricing mechanisms in electricity spot markets poses dual challenges to the optimal operation of swap stations. To address this

this paper establishes a characterization model of the electric energy regulation capability of EV swapping stations based on battery swap demand forecasting. Building upon this model and incorporating multi-timescale pricing signals from electricity markets

we formulate a two-stage optimization model encompassing day-ahead scheduling and intraday real-time energy management. To address the temporal coupling effects

a hybrid MPC-PSO optimization framework is proposed

integrating model predictive control (MPC) with particle swarm optimization (PSO). Simulation experiments verify that the proposed battery energy management model for EV swap stations can effectively participate in electricity spot markets

significantly enhancing operational economic efficiency while ensuring user swap demand satisfaction.