The matching between dynamic supply of renewable power generation and flexible charging demand of the Electric Vehicles (EVs) can not only increase the penetration of renewables but also reduce the load to the state electric power grid. The challenges herein are the curse of dimensionality, the multiple decision making stages involved, and the uncertainty of both the supply and demand sides. Event-Based Optimization (EBO) provides a new way to solve large-scale Markov decision process. Considering different spatial scales, we develop a bi-level EBO model in this paper which can both catch the changes on the macro and micro levels. By proper definition, the size of event space stays fixed with the scale of the problem, which shows good scalability in online optimization. Then a bi-level Q-learning method is developed to solve the problem iteratively. We demonstrate the performance of the method by numerical examples. Our method outperforms other methods both in performance and scalability.