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Incentive-willingness-decision framework: Unit discharge triangle-based maximum stable V2G capability evaluation

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  • Liu, Ke
  • Liu, Yanli

Abstract

Popularizing electric vehicles (EVs) brings substantial vehicle-to-grid (V2G) potential. Meanwhile, uncertainties in EV states and user decisions pose significant challenges for V2G capability evaluation. However, existing methods fail to determine the stable and practical V2G capabilities integrating user states and decisions. To this end, this paper proposes a stable V2G capability evaluation method based on maximum discharge potential assessment and incorporating user decisions under multi-attribute influencing factors. Firstly, the unit discharge triangle (UDT) model within the maximum discharge feasible region (MDFR) for EVs is proposed to cover stable discharge states of all dischargeable EV objects for maximum stable discharge potential evaluation. Subsequently, the incentive-willingness-decision (IWD) framework, integrating the user's entire decision process with accumulated multi-attribute influencing factors, is presented to determine V2G participation willingness/decisions of stably dischargeable users for practical V2G capability evaluation. Specifically, travel demand, dwell time, tariff compensation, and battery degradation are introduced as direct incentive factors with influence throughout the process. Individual preferences and uncertainties are captured during the willingness and decision stages. Finally, considering the spatiotemporal characteristics of EVs, the test was conducted on a realistic 29-node traffic network, validating the generality and superiority of the proposed method.

Suggested Citation

  • Liu, Ke & Liu, Yanli, 2024. "Incentive-willingness-decision framework: Unit discharge triangle-based maximum stable V2G capability evaluation," Applied Energy, Elsevier, vol. 374(C).
    • Handle: RePEc:eee:appene:v:374:y:2024:i:c:s0306261924012339
    DOI: 10.1016/j.apenergy.2024.123850
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    References listed on IDEAS

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