  Effectively operating electrical vehicle charging station (EVCS) is crucial
for enabling the rapid transition of electrified transportation. To solve this
problem using reinforcement learning (RL), the dimension of state/action spaces
scales with the number of EVs and is thus very large and time-varying. This
dimensionality issue affects the efficiency and convergence properties of
generic RL algorithms. We develop aggregation schemes that are based on the
emergency of EV charging, namely the laxity value. A least-laxity first (LLF)
rule is adopted to consider only the total charging power of the EVCS which
ensures the feasibility of individual EV schedules. In addition, we propose an
equivalent state aggregation that can guarantee to attain the same optimal
policy. Based on the proposed representation, policy gradient method is used to
find the best parameters for the linear Gaussian policy . Numerical results
have validated the performance improvement of the proposed representation
approaches in attaining higher rewards and more effective policies as compared
to existing approximation based approach.
