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Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

Author

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  • Shijun Chen

    (School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246133, China)

  • Huwei Chen

    (Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China)

  • Shanhe Jiang

    (School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246133, China)

Abstract

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.

Suggested Citation

  • Shijun Chen & Huwei Chen & Shanhe Jiang, 2019. "Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach," Energies, MDPI, vol. 12(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:325-:d:199436
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    References listed on IDEAS

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    1. Ainur Rakhymbay & Anvar Khamitov & Mehdi Bagheri & Batyrbek Alimkhanuly & Maxim Lu & Toan Phung, 2018. "Precise Analysis on Mutual Inductance Variation in Dynamic Wireless Charging of Electric Vehicle," Energies, MDPI, vol. 11(3), pages 1-21, March.
    2. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    3. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
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    1. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

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