The transition to electric vehicles (EVs), coupled with the rise of renewable energy sources, will significantly impact the electric grid. Unlike conventional fuel sources, electricity for EVs is constrained by grid capacity, price fluctuations, and long EV charging times, requiring new pricing solutions to manage demand and supply. This paper proposes a model for online dynamic pricing of reserved EV charging services, including reservation, parking, and charging as a bundled service priced as a whole. Our approach focuses on the individual charging station operator, employing a stochastic demand model and online dynamic pricing based on expected demand. The proposed model uses a Markov Decision Process (MDP) formulation to optimize sequential pricing decisions for charging session requests. A key contribution is the novel definition and quantification of discretization error introduced by the discretization of the Poisson process for use in the MDP. The model's viability is demonstrated with a heuristic solution method based on Monte-Carlo tree search, offering a viable path for real-world application.

Inventory control using discrete-time models is a wellstudied problem, where orders of items to hold in stock must anticipate future demand [1, 2]. By defining the costs of insufficient stocks, it is possible to find cost-minimizing policies using dynamic programming [3, 4, 5]. In practice, however, maintaining a certain service level of an inventory control system is a greater priority than cost minimization [6, 7]. Under certain restrictive assumptions on the demand process - such as memoryless and identically distributed demand - there are explicit formulations of the duality between service levels and costs [8].