Revenue Estimation for Orderly Charging of Electric Vehicles Based on Smart Meters

With the rapid development of electric vehicles (EVs), uncoordinated charging of large numbers of EVs in residential areas leads to transformer overloading. Given a number of orderly charging approaches in existing literature, their business model and corresponding revenue estimation is still unclear, especially for residential areas. Therefore, a business model of smart meter-based ordered charging is proposed to alleviate the charging problems of users in residential areas and quantify the revenue of aggregators. First, a bidirectional” cloud-meter-pile-vehicle” communication scheme enabled by smart meters is established to facilitate data collection and implement controlled, organized charging. Then, A Gaussian mixture model (GMM) is employed to characterize users' historical behaviors, enabling the formulation of an orderly charging strategy designed to minimize charging costs for user groups. Finally, taking the actual data of a region in Hangzhou as an example, GMM is constructed to analyze charging start times, charging durations, and charging energy. This allows for revenue estimation of EV orderly charging within this business model and the determination of the investor's return period.