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Integrating massive electric vehicles (EVs) into the power grid requires charging to be coordinated to reduce energy costs and the peak-to-average ratio (PAR) of the system. The coordination becomes more challenging when the highly fluctuant renewable energies constitute a significant portion of the power resources. To tackle this problem, a novel two-stage EV charging mechanism is designed in this paper, which mainly includes three parts. At the first stage, based on the prediction of future energy requests and considering the elastic charging property of EVs, an offline optimal energy generation scheduling problem is formulated and solved in a day-ahead manner to determine the energy generation in each time slot the next day. Then, at the second stage, based on the planned energy generation day-ahead, an adaptive real-time charging strategy is developed to determine the charging rate of each vehicle in a dynamic manner. Finally, we develop a charging rate compression (CRC) algorithm, which tremendously reduces the complexity of the problem solving. The fast algorithm supports real-time operations and enables the large-scale small-step scheduling more efficiently. Simulation results indicate that the proposed scheme can help effectively save energy costs and reduce the system PAR. Detailed evaluations on the impact of renewable energy uncertainties show that our proposed approach performs well in enhancing the system fault tolerance against uncertainties and the noises of real-time data. We further extend the mechanism to track a given load profile and handle the scenario where EVs only have several discrete charging rates. As a universal methodology, the proposed scheme is not restricted to any specific data traces and can be easily applied to many other cases as well.
Wang et al. (Thu,) studied this question.