Abstract
To achieve coordinated electric vehicle (EV) charging with demand response (DR), a model-free approach using reinforcement learning (RL) is an attractive proposition. Using RL, the DR algorithm is defined as a Markov decision process (MDP). Initial work in this area comprises algorithms to control just one EV at a time, because of scalability challenges when taking coupling between EVs into account. In this paper, we propose a novel MDP definition for charging an EV fleet. More specifically, we propose (1) a relatively compact aggregate state and action space representation, and (2) a batch RL algorithm (i.e., an instance of fitted Q-iteration, FQI) to learn the optimal EV charging policy.
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