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Energy Use > 2026 Vol. 2 > No. 2 > 100050
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Pricing-Driven Cooperative Mechanisms for Bidirectional Vehicle-to-Building Charging: An Evolutionary Game Approach

  • 1.

    Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China

  • 2.

    Network Management Product Department, China Mobile R & D Center of Network Planning and Optimization, China Mobile Group Design Institute Co., Ltd., Beijing 100080, China

  • 3.

    Persagy Technology CO., Ltd., Beijing 100084, China

  • 4.

    Beijing Uni-Construction Group Co., Ltd., Beijing 100085, China

  • 5.

    These authors contributed equally to this work

    • Fund Project: This work was supported by the Beijing Natural Science Foundation Undergraduate Research Program (Grant No. QY24105) and the Beijing Uni-Construction Group Co., Ltd. through the project "Applicability of Energy Microgrids for the Retrofit of Existing Commercial Buildings".
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  • Corresponding author: zhangwr@bjut.edu.cn 
  • Received Date:  April 17, 2026
    Accepted Date:  June 11, 2026
    Published Online:  June 25, 2026
Energy Use  2(2),  https://doi.org/10.59717/ipj.energy-use.2026.100050  |  Cite this article
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    • PUBLIC SUMMARY

    1. V2B can boost urban energy system flexibility, but prior research ignores strategic decisions of EV users and building managers under price-based coordination.

      This study integrates evolutionary game theory and system dynamics to explore pricing-driven V2B interactions and cooperative strategy evolution.

      Price incentives and discharging profits drive cooperation; larger discharge volume and lower non-cooperation losses speed up stable equilibrium.

      The case gives valid charging (1.3626–1.8333) and discharging (0.483–0.9591) price ranges (CNY/(kW∙h)) for stable V2B operation.

      The results support V2B mechanism design and urban low-carbon energy management.

    • ABSTRACT
  • With rapid growth of electric vehicles (EVs), vehicle-to-building (V2B) interactions have emerged as a promising approach to enhance flexibility and sustainability in urban energy systems. However, existing studies often overlook the strategic decision-making processes of EV users and office building managers under pricing-based coordination schemes, which may limit the stability and effectiveness of cooperative V2B operation. To address this gap, this study develops an integrated framework combining evolutionary game theory and system dynamics to investigate pricing-driven interactions between EV users and office building managers within urban energy systems. A cost-benefit model is formulated for both parties to analyse the evolution of cooperative strategies, and a case study is conducted to identify EV charging and discharging price ranges conducive to stable cooperation. The results indicate that electricity price incentives and EV discharging profitability significantly promote cooperative behaviour, while higher EV discharging volumes and lower unilateral non-cooperation losses accelerate convergence to a stable cooperative equilibrium. A case study demonstrates that stable cooperation can be achieved when EV charging and discharging prices range from 1.3626-1.8333 and 0.483-0.9591 CNY/(kW∙h), respectively, in office building scenarios. These findings provide a theoretical basis for facilitating effective interactions between EVs and buildings in urban energy systems and offer practical insights for designing pricing mechanisms that support sustainable urban energy management and low-carbon transitions.
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  • Cite this article:

    Gao S., Bai Y., Zhang W., et al. (2026). Pricing-Driven Cooperative Mechanisms for Bidirectional Vehicle-to-Building Charging: An Evolutionary Game Approach. Energy Use 2:100050. https://doi.org/10.59717/ipj.energy-use.2026.100050
    Gao S., Bai Y., Zhang W., et al. (2026). Pricing-Driven Cooperative Mechanisms for Bidirectional Vehicle-to-Building Charging: An Evolutionary Game Approach. Energy Use 2:100050. https://doi.org/10.59717/ipj.energy-use.2026.100050

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