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A canonical coalitional game model incorporating motivational psychology analysis for incentivizing stable direct energy trading in smart grid

Author

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  • Wang, Yifeng
  • Jiang, Aihua
  • Wang, Rui
  • Tian, Junyang

Abstract

While maximizing distributed energy value, direct electricity trading introduces strong interactive features, leading to conflicting interests among users. A key challenge is balancing user benefits and interests to encourage broad and sustainable prosumer participation. Motivation plays a vital role in driving human emotions and behaviors. This study incorporates motivational psychology to design an incentive scheme based on a canonical coalition game (CCG). By analyzing prosumers' psychological changes before energy trading, we develop corresponding incentive models for different psychological stages. The trading scheme must align with these model attributes to attract prosumer participation. Building upon canonical coalition game theory, we assess the feasibility of cooperation among prosumers and propose an edge-end coalitional trading scheme to reconcile conflicting interests. Numerical results demonstrate that the proposed scheme satisfies motivational psychology model attributes, motivating active and sustainable prosumer engagement. We further investigate the coalition game model properties, rigorously proving superadditivity and non-empty core, ensuring prosumers consistently receive stable and fair profits. Case analysis reveals the grand coalition effectively enhances global payoffs. Furthermore, the proposed scheme facilitates peak shaving, valley filling, and on-site integration of renewable energy, promoting grid stability.

Suggested Citation

  • Wang, Yifeng & Jiang, Aihua & Wang, Rui & Tian, Junyang, 2024. "A canonical coalitional game model incorporating motivational psychology analysis for incentivizing stable direct energy trading in smart grid," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223032024
    DOI: 10.1016/j.energy.2023.129808
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    References listed on IDEAS

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    Cited by:

    1. Bilardo, M., 2025. "A fair dynamic incentive allocation method for virtual energy sharing in renewable energy communities that rewards members’ virtuosity and engagement," Renewable Energy, Elsevier, vol. 255(C).
    2. Jiang, Qian & Jia, Hongjie & Mu, Yunfei & Yu, Xiaodan & Wang, Zibo, 2024. "Bilateral planning and operation for integrated energy service provider and prosumers - A Nash bargaining-based method," Applied Energy, Elsevier, vol. 368(C).
    3. Xia, Yuanxing & Wang, Ke & Huang, Yu & Lin, Tinjun & Shi, Linjun & Wu, Feng, 2026. "Bounded rational decision-making modeling and analysis in local energy markets: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
    4. Gao, Jin & Shao, Zhenguo & Chen, Feixiong & Lak, Mohammadreza, 2024. "Robust optimization for integrated energy systems based on multi-energy trading," Energy, Elsevier, vol. 308(C).
    5. Li, Ping & Chen, Jiajia & Yang, Hui & Lin, Zhenjia, 2026. "Peer-to-peer power trading and pricing for rental energy storage shared community microgrid: A coordinated Stackelberg and cooperative game," Renewable Energy, Elsevier, vol. 256(PB).
    6. Wang, Jie & Jin, Xiaolong & Jia, Hongjie & Xu, Da & Mu, Yunfei & Yu, Xiaodan & Hou, Kai, 2025. "Electricity-hydrogen-heat coupled sharing for multi-energy buildings: A game theoretic approach," Applied Energy, Elsevier, vol. 398(C).

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