A Q-Learning Method for Scheduling Shared EVs under Uncertain User Demand and Wind Power Supply

The last few years have witnessed the fast rise of sharing economy around the world. Thanks to the rapid development of electric vehicle industry and its higher market share, the business of shared electric vehicles (EVs) gains the opportunity to expand. With the improvements in charging facilities, wind power generation of high-rise buildings is expected to be a major technology to utilize renewable energy in cities. While the intermittence of wind power makes it hard to be used. Shared EVs are the perfect users of wind power for their flexibilities in using and charging. However, the scheduling of shared EVs is highly challenging because of the randomness both in wind power supply and the user demand. We address this important problem in this paper. We formulate the scheduling of shared EVs in the framework of Markov decision process. An agent-based state is defined, based on which a distributed optimization algorithm can be applied. We propose a Q-learning algorithm to solve the problem of scheduling shared EVs to maximize the global daily income. Both the users' uncertain demand and stochastic wind power supply are considered. The performance of the proposed algorithm is illustrated by numerical experiments.