• Sharing private charging piles to boost EVs charging and citizen collaboration. • Setting up renewable energy and vehicle-to-grid systems to boost sustainability. • Using two-stage stochastic programming and sample average approximation method. • Studying a real case in Canada to verify the efficiency of the smart city framework. The increasing use of electric vehicles (EVs) has led to challenges in determining the most effective methods for charging their batteries. A potential solution to address this issue is the expansion of smart homes equipped with renewable energy sources, such as wind turbines and solar panels, to meet the growing demand for EV charging. By installing private charging piles (PCPs) in homes and enabling their sharing, both homes and EVs can benefit economically. Moreover, these PCPs can provide vehicle-to-grid services that can enhance the stability of the power system. This study develops a two-stage stochastic model to optimize the energy system of homes equipped with various types of distributed generation and PCPs. To address the role of uncertainty in parameters such as demands and renewable power generation, a sample average approximation (SAA) method is used to optimize the model. The SAA method is developed in a way that an autoregressive moving average (ARMA) model is employed to generate scenarios, and a fuzzy c-means (FCM) clustering algorithm is utilized to reduce the number of scenarios. This study utilizes data from the city of Calgary, Canada, to examine the application of the proposed model in a real-life setting. The results demonstrate that sharing PCPs benefits households by optimizing energy use, supports EV owners by making charging more convenient, and helps governments by reducing the need to build additional public charging stations.
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Khodoomi et al. (2025) studied this question.
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