Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Charging Pricing for Autonomous Mobility-on-demand Fleets Based on Game Theory
Author:
Affiliation:

1.Department of Electrical Engineering, Tsinghua University, Beijing100084, China;2.Shanxi Energy Internet Research Institute, Taiyuan030032, China

Fund Project:

This work was supported by Shanxi Energy Internet Research Institute (No. SXEI 2023 A 003).

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    Abstract:

    Considering the enormous potential application of autonomous mobility-on-demand (AMoD) systems in future urban transportation, the charging behavior of AMoD fleets, as a key link connecting the power system and the transportation system, needs to be guided by a reasonable charging demand management method. This paper uses game theory to investigate charging pricing methods for the AMoD fleets. Firstly, an AMoD fleet scheduling model with appropriate scale and mathematical complexity is established to describe the spatio-temporal action patterns of the AMoD fleet. Subsequently, using Stackelberg game and Nash bargaining, two game frameworks, i.e., non-cooperative and cooperative, are designed for the charging station operator (CSO) and the AMoD fleet. Then, the interaction trends between the two entities and the mechanism of charging price formation are discussed, along with an analysis of the game implications for breaking the non-cooperative dilemma and moving towards cooperation. Finally, numerical experiments based on real-world city-scale data are provided to validate the designed game frameworks. The results show that the spatio-temporal distribution of charging prices can be captured and utilized by the AMoD fleet. The CSO can then use this action pattern to determine charging prices to optimize the profit. Based on this, negotiated bargaining improves the overall benefits for stakeholders in urban transportation.

    图1 Schematic diagram of an augmented time-space-energy network. (a) Augmented time-space-energy network. (b) Traffic network topology.Fig.1
    图2 Illustration of Stackelberg game between CSO and AMoD fleet.Fig.2
    图3 Illustration of Nash bargaining between CSO and AMoD fleet.Fig.3
    图4 Heat map distribution of origin locations for passenger travel demands.Fig.4
    图5 Distribution of time for passenger orders.Fig.5
    图6 Electricity price data in each grid.Fig.6
    图7 Pricing results and charging power of each charging station (Stackelberg game).Fig.7
    图8 Distribution of vehicle locations at different time (Stackelberg game). (a) 00:00. (b) 03:00. (c) 06:00. (d) 09:00. (e) 12:00. (f) 15:00. (g) 18:00. (h) 21:00. (i) 24:00.Fig.8
    图9 Number of vehicles in different states at each time slot (Stackelberg game).Fig.9
    图10 Pricing results and charging power of each charging station (Nash bargaining).Fig.10
    表 1 Table 1
    表 2 Table 2
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History
  • Received:February 05,2024
  • Revised:March 23,2024
  • Adopted:
  • Online: December 20,2024
  • Published: