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This paper addresses the problem of defining a day-ahead consumption plan for charging a fleet of electric vehicles (EVs), and following this plan during operation. A challenge herein is the beforehand unknown charging flexibility of EVs, which depends on numerous details about each EV (e.g., plug-in times, power limitations, battery size, power curve, etc.). To cope with this challenge, EV charging is controlled during opertion by a heuristic scheme, and the resulting charging behavior of the EV fleet is learned by using batch mode reinforcement learning. Based on this learned behavior, a cost-effective day-ahead consumption plan can be defined. In simulation experiments, our approach is benchmarked against a multistage stochastic programming solution, which uses an exact model of each EVs charging flexibility. Results show that our approach is able to find a day-ahead consumption plan with comparable quality to the benchmark solution, without requiring an exact day-ahead model of each EVs charging flexibility.
Vandael et al. (Mon,) studied this question.