On optimal charging scheduling for electric vehicles with wind power generation

We consider the scheduling of battery charging of electric vehicles (EVs) integrated with renewable power generation. The increasing adoption of EVs and the development of renewable energies contribute importance to this research. The optimization of charging scheduling is challenging because of the large action space, the multi-stage decision making, and the high uncertainty. To solve this problem is time-consuming when the scale of the system is large. It is urgent to develop a practical and efficient method to properly schedule the charging of EVs. The contribution of this work is threefold. First, we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation. An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds. Second, the scenario when the supply of the renewable power generation is deficient is investigated. We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first (mLLLP) rule. Third, we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations. We test the proposed algorithm by numerical experiments. The results show that it performs better than the other existing rule-based methods.