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

Abstract

Due to the ever-increasing popularity of ridehailing 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 ridehailing 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 lowerlevel 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 Shenzhen in China.

A Pricing Mechanism for Balancing the Charging of Ride-Hailing Electric Vehicle Fleets

“…Therefore, even the Stackelberg games that exhibit favorable mathematical properties such as the one in Definition 2 pose significant difficulties in computing the leader's strategy. As previously mentioned in the introduction, if the nature of the application allows centralized computation, one can formulate an MPCC that can be recast into an instance of mixed-integer linear or quadratic problems [22] using the big-M reformulation [13] as demonstrated in [12,18,26]. Unfortunately, such computation could breach the privacy of the lowerlevel agents in many real-world applications, particularly in terms of sharing information about personal constraint sets X i .…”