  With the increasing number of fast-electric vehicle charging stations
(fast-EVCSs) and the popularization of information technology, electricity
price competition between fast-EVCSs is highly expected, in which the
utilization of public and/or privacy-preserved information will play a crucial
role. Self-interest electric vehicle (EV) users, on the other hand, try to
select a fast-EVCS for charging in a way to maximize their utilities based on
electricity price, estimated waiting time, and their state of charge. While
existing studies have largely focused on finding equilibrium prices, this study
proposes a personalized dynamic pricing policy (PeDP) for a fast-EVCS to
maximize revenue using a reinforcement learning (RL) approach. We first propose
a multiple fast-EVCSs competing simulation environment to model the selfish
behavior of EV users using a game-based charging station selection model with a
monetary utility function. In the environment, we propose a Q-learning-based
PeDP to maximize fast-EVCS' revenue. Through numerical simulations based on the
environment: (1) we identify the importance of waiting time in the EV charging
market by comparing the classic Bertrand competition model with the proposed
PeDP for fast-EVCSs (from the system perspective); (2) we evaluate the
performance of the proposed PeDP and analyze the effects of the information on
the policy (from the service provider perspective); and (3) it can be seen that
privacy-preserved information sharing can be misused by artificial
intelligence-based PeDP in a certain situation in the EV charging market (from
the customer perspective).
