With the influx of electric vehicles, rise of renewable energy sources, and increased deregulation in the electricity market, aggregators such as battery swapping stations with sustainable operational strategies and business models are needed to resolve these challenges to benefit the relevant stakeholders: electric vehicle drivers, battery swapping station owners and distributing company operators. Attempts were already made to create models and strategies for swapping stations, but unfortunately, no comprehensive model addresses the uncertainties inherent in the design and the decisions of relevant stakeholders. This research aims to present practical yet comprehensive stochastic approaches for swapping station business models and operational strategies. Depending on the available information and computing power, the developed models and strategies explored the long-term feasibility and stakeholder reviews using the time-sequence Monte Carlo approach, two-stage optimization techniques and distributed optimization. Two-stage techniques used are optimization with recourse, bilevel programming with Karush-Kuhn-Tucker optimality condition reformulations and multiobjective optimization. Aggregated EV batteries in the station act as a form of distributed energy storage supporting the intermittent renewable energy sources in managing peak loads. For the practical implementation of the business models, complementary electric vehicle station visit forecasting strategies were also proposed ranging from peak-based, conventional arrival, and previous visit dependency techniques. This research has shown how policies for swapping station business models and operational strategies can be developed considering the uncertainties in the swapping design and the decisions of relevant stakeholders.