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Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution.

Business models for battery swapping stations (BSS) have been emerging as influenced by the increased attention to electric vehicles (EVs) and the deregulation of the electricity market. BSS may also provide support mechanisms for a sustainable EV ecosystem, but swapping stations are still at an early stage and viewed as being risky without a widely accepted prediction of financial return. Although different BSS operational strategies have been proposed, an integrated model that considers battery life, lifecycle cost, EV consumer behaviour, and supplementary grid services is still missing. A two-level hierarchical model is proposed where the unit model follows a transition-based battery allocation technique and the station model provides a system-view platform. Based on the designed hierarchical model, the strict grid scheduling strategy and grid scheduling with battery reservation strategy are evaluated in terms of profit and average battery life using New South Wales and South Australia electricity demand profiles. Results suggest that trading short-term grid services profitability in the grid scheduling with battery reservation strategy led to overall increased profit and also longer service life for batteries.

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