  Initial DR studies mainly adopt model predictive control and thus require
accurate models of the control problem (e.g., a customer behavior model), which
are to a large extent uncertain for the EV scenario. Hence, model-free
approaches, especially based on reinforcement learning (RL) are an attractive
alternative. In this paper, we propose a new Markov decision process (MDP)
formulation in the RL framework, to jointly coordinate a set of EV charging
stations. State-of-the-art algorithms either focus on a single EV, or perform
the control of an aggregate of EVs in multiple steps (e.g., aggregate load
decisions in one step, then a step translating the aggregate decision to
individual connected EVs). On the contrary, we propose an RL approach to
jointly control the whole set of EVs at once. We contribute a new MDP
formulation, with a scalable state representation that is independent of the
number of EV charging stations. Further, we use a batch reinforcement learning
algorithm, i.e., an instance of fitted Q-iteration, to learn the optimal
charging policy. We analyze its performance using simulation experiments based
on a real-world EV charging data. More specifically, we (i) explore the various
settings in training the RL policy (e.g., duration of the period with training
data), (ii) compare its performance to an oracle all-knowing benchmark (which
provides an upper bound for performance, relying on information that is not
available or at least imperfect in practice), (iii) analyze performance over
time, over the course of a full year to evaluate possible performance
fluctuations (e.g, across different seasons), and (iv) demonstrate the
generalization capacity of a learned control policy to larger sets of charging
stations.
