论文标题

平衡乘车电动舰队充电的等级定价游戏

Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

论文作者

Maljkovic, Marko, Nilsson, Gustav, Geroliminis, Nikolas

论文摘要

由于乘车服务的普及以及向替代燃料汽车的无可争议的转变,乘车市场和智能电动移动性的交汇处为实现社会最佳的交易提供了机会。在这项工作中,我们提出了一种基于层次的基于游戏的控制机制,用于平衡多个乘车舰队的同时充电。该机制考虑了乘车司机的利益有时相互矛盾的利益,乘车公司管理公司以及提供权力的公司或城市政府等外部代理商将来将在将来发挥重要作用。高层控制将收费价格激励措施,并将外部代理商与乘车公司之间的交互作用建模为具有单个领导者和多个关注者的反向Stackelberg游戏。较低级别的控制激发了收入最大化的驱动因素,以遵循公司运营商的要求,并通过涌现的定价并将互动建模为单个领导者,多个追随者stackelberg游戏。我们提供了一种定价机制,可确保在上层游戏中存在独特的NASH均衡,从而同时最大程度地降低了外部代理的目标。我们提供了高级控制的理论和实验鲁棒性分析,这些参数取决于敏感信息,而敏感信息可能无法完全访问外部试剂。对于低级算法,我们将高级游戏的NASH平衡与二次混合整数优化问题结合在一起,以找到最佳的激增价格。最后,我们根据中国深圳市的实际出租车数据进行了案例研究中的控制机制的性能。

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balancing the simultaneous charging of multiple ride-hailing fleets. The mechanism takes into account sometimes conflicting interests of the ride-hailing drivers, the ride-hailing company management, and the external agents such as power-providing companies or city governments that will play a significant role in charging management in the future. The upper-level control considers charging price incentives and models the interactions between the external agents and ride-hailing companies as a Reverse Stackelberg game with a single leader and multiple followers. The lower-level control motivates the revenue-maximizing drivers to follow the company operator's requests through surge pricing and models the interactions as a single leader, multiple followers Stackelberg game. We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time. We provide theoretical and experimental robustness analysis of the upper-level control with respect to parameters whose values depend on sensitive information that might not be entirely accessible to the external agent. For the lower-level algorithm, we combine the Nash equilibrium of the upper-level game with a quadratic mixed integer optimization problem to find the optimal surge prices. Finally, we illustrate the performance of the control mechanism in a case study based on real taxi data from the city of Shenzhen in China.

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