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Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game

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  • Li, Songrui
  • Zhang, Lihui
  • Nie, Lei
  • Wang, Jianing

Abstract

With the rise of integrated energy systems, the load aggregator's role in forming a connection between the demand and supply sides has been gaining increasing importance. Traditional demand response has gradually developed into integrated demand response. Considering consumer psychology, a bilateral integrated demand response model between the integrated energy production base, load aggregator, and users is established; the model is a hierarchical Stackelberg game. Simultaneously, a mixed integer quadratic programming-multi-verse optimizer distributed algorithm is proposed to solve the game model. During the continuous interactive gaming process, the utilities of all parties can be maximized while achieving a balance of benefits. Considering whether price incentives should be implemented and including consumers' psychological characteristics, three scenarios were established for case analysis. The results show that the proposed trading strategy can increase the benefits of the integrated energy production base, load aggregator, and users by 5%, 4%, and 23%, respectively. In addition, the penalty cost of abandoning wind and solar power and of power reserve capacity are reduced by $34.4 and $9, respectively. The introduction of the hierarchical game model ensures the sustainability of the mechanism and provides innovative ideas for the development of integrated energy systems.

Suggested Citation

  • Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005813
    DOI: 10.1016/j.energy.2022.123678
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    6. Mota, Bruno & Faria, Pedro & Vale, Zita, 2022. "Residential load shifting in demand response events for bill reduction using a genetic algorithm," Energy, Elsevier, vol. 260(C).
    7. Lin, Xueshan & Huang, Tao & Bompard, Ettore & Wang, Beibei & Zheng, Yaxian, 2023. "Ex-ante market power evaluation and mitigation in day-ahead electricity market considering market maturity levels," Energy, Elsevier, vol. 278(C).
    8. Lei, Zhenxing & Liu, Mingbo & Shen, Zhijun & Lu, Wentian & Lu, Zhilin, 2023. "A data-driven Stackelberg game approach applied to analysis of strategic bidding for distributed energy resource aggregator in electricity markets," Renewable Energy, Elsevier, vol. 215(C).
    9. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    10. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(C).
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    13. Wang, Yudong & Hu, Junjie, 2023. "Two-stage energy management method of integrated energy system considering pre-transaction behavior of energy service provider and users," Energy, Elsevier, vol. 271(C).

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