Abstract: Currently, as the interface between electric vehicles (EVs) and the power grid, to improve the revenue of the battery swapping/charging stations (BSCSs), the BSCS participates in the market in the form of an aggregator and at the same time, utilizes the complementary characteristics of the adjustable capacity (AC) of the BSCS, and puts forward a multi-purpose market operation strategy of the BSCS aggregator that takes into account the seasonal differences of AC. First, considering the influence of environmental factors on EVs travel behavior, this paper constructs a road network energy consumption model based on "virtual nodes" and simulates the switching and charging demand of EVs with the lowest energy consumption as the travel goal to analyze the seasonal differences in the uncertainty of EVs' behavior. Second, to cope with the differences in the switching and charging/discharging behaviors in the BSCS, a switching model is introduced to solve the uncertainty of user response and establish a carving method to satisfy the AC under the continuous operation constraints of the switched-charging service. Finally, based on the reinforcement learning algorithm to solve the operational strategy for BSCS aggregator to participate in the market, the arithmetic example demonstrates the utilization of the complementary characteristics of the AC of BSCS, which smooths out the volatility of the AC due to seasonal rotation and improves the revenue of BSCS aggregators.