As electric vehicles (EVs) steadily gain popularity on public roads, commercial EV charging has become a routine business service. As this trend continues, charging station operators are actively exploring strategies to boost the profitability of their services or to distribute limited resources more effectively. Dynamic pricing has proven effective for increasing revenue in markets with varying customer demand. This paper introduces a Markov Decision Process (MDP)-based framework for dynamic pricing that maximizes either revenue or utilization for charging station operators. The proposed method is implemented through a Monte Carlo Tree Search (MCTS) algorithm. It is tested in simulation across multiple scenarios derived from a real-world dataset of EV charging sessions. Results demonstrate that the approach delivers near-optimal pricing decisions within milliseconds, even for large-scale instances, delivering substantial improvements in revenue or utilization compared to a conventional flat-rate baseline across various parameter settings.
Institute of Smart Energy Systems Press