Day-ahead demand response decision and charging planning for multiple battery swapping stations

This study focuses on optimizing peak shaving auxiliary services and charging planning in a system with multiple Battery Swapping Stations (BSSs). We develop a Mixed Integer Linear Programming (MILP) model to maximize the total revenue of the BSSs system. Given the scalability challenges of managing multiple BSSs, we propose a heuristic approach that breaks down the problem into smaller sub-models for each individual BSS. An innovative concept introduced is the Unit Cost for Attending Peak Shaving Service, which serves as a criterion for adjusting power consumption reduction across different BSSs. We evaluate the effectiveness of this method through a case study, highlighting the benefits of the Cooperated Planning Charging Policy derived from our model. Additionally, a sensitivity analysis is performed to examine the impact of various factors on the multi-BSS system.