为快充桩设计动态定价模型，整合预约与充电服务。
Online Dynamic Pricing for Electric Vehicle Charging Stations with Reservations
- 用泊松过程建模预约请求，结合马尔可夫决策过程优化定价。
- 提出离散化误差分析，提升连续时间模型在离散框架中的精度。
- 基于蒙特卡洛树搜索的启发式算法，适合实际部署应用。
本文提出一种新型在线动态定价模型，将电动汽车充电服务的预约、停车与充电整合为统一套餐进行定价。针对高需求快充站点，采用泊松过程建模充电预约到达，构建基于马尔可夫决策过程（MDP）的在线动态定价策略。关键贡献在于分析了将连续时间泊松过程引入离散MDP框架时产生的离散化误差。通过基于蒙特卡洛树搜索的启发式动态定价方法，验证了该模型在实际应用中的可行性。
原文摘要 · Abstract (English)
This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model's feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications.
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