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).

翻译：随着快速电动汽车充电站（fast-EVCSs）数量的增加以及信息技术的普及，快速电动汽车充电站之间的电价竞争备受期待，其中公共信息和/或隐私保护信息的利用将发挥关键作用。另一方面，自私的电动汽车（EV）用户会基于电价、预估等待时间和自身荷电状态，以最大化自身效用为目标选择快速电动汽车充电站进行充电。现有研究主要集中于寻找均衡电价，而本研究则提出了一种面向快速电动汽车充电站的个性化动态定价策略（PeDP），采用强化学习（RL）方法以最大化收益。我们首先构建了一个多快速电动汽车充电站竞争仿真环境，通过引入基于博弈的充电站选择模型（包含货币效用函数）来描述电动汽车用户的自私行为。在此环境中，我们提出了一种基于Q学习的个性化动态定价策略，以最大化快速电动汽车充电站的收益。基于该环境的数值仿真表明：（1）从系统视角出发，通过将经典伯特兰德竞争模型与所提出的快速电动汽车充电站个性化动态定价策略进行比较，我们识别出等待时间在电动汽车充电市场中的重要性；（2）从服务提供商视角出发，我们评估了所提出的个性化动态定价策略的性能，并分析了信息对该策略的影响；（3）从顾客视角出发，可见在电动汽车充电市场的特定情境下，基于人工智能的个性化动态定价策略可能滥用隐私保护信息共享。