Abstract
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.
Highlights
The growing population of Electric Vehicles (EVs) in recent years has made controlled EV charging a popular research topic[1]
Different from the existing papers, this paper focuses on scheduling the charging schedule of EVs managed by the commercial company with on-site wind power to minimize the operation cost
The EV charging scheduling can be divided into two steps, how many EVs will be charged in each EV Aggregators (EVAs)
Summary
The growing population of Electric Vehicles (EVs) in recent years has made controlled EV charging a popular research topic[1]. Yang et al.[4] showed the potential of using on-site wind energy generations of skyscrapers to supply EVs charging in the smart grid. With the increase of the number of EVs, both the state and action space grow exponentially It contains a multiple decision-making stages since the charging action will affect the future states. Compared with the existing works on the similar topics, this paper considers the charging scheduling problem and makes the following main contributions.
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