IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i9p2396-d1650915.html
   My bibliography  Save this article

Research on Resource Utilization of Bi-Level Non-Cooperative Game Systems Based on Unit Resource Return

Author

Listed:
  • Bo Fu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Peiwen Li

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Yi Quan

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

Abstract

In a competitive market, due to differences in the nature of various power generation entities, there is a decline in resource utilization and difficulties in ensuring a return on investment for generating units within the system. A bi-level non-cooperative game model based on the Unit Resource Return (URR) is proposed to safeguard the interests and demands of each power generation unit while improving the overall resource utilization rate of the system. Firstly, we construct a comprehensive energy-trading framework for the overall system and analyze the relationship between the Independent System Operator (ISO) and the generation units. Secondly, we propose the Unit Resource Return (URR), inspired by the concept of input-output efficiency in economics. URR evaluates the return on unit resource input by taking the maximum generation potential of each unit as the benchmark. Finally, a bi-level non-cooperative game model is established. In the lower-level non-cooperative game, the generating units safeguard their own interests, while in the upper-level, the ISO adjusts the output allocation and engages in a master–slave game between generating units to ensure the overall operational efficiency of the system. URR is adopted as the ISO’s price-clearing equilibrium criterion, enabling the optimization of both resource profitability and allocation. Ultimately, both the upper and lower-level decision variables reach a Nash equilibrium. The experimental results show that the bi-level non-cooperative game model based on the Unit Resource Return improves the overall resource utilization of the system and enhances the long-term operational motivation of the generating units.

Suggested Citation

  • Bo Fu & Peiwen Li & Yi Quan, 2025. "Research on Resource Utilization of Bi-Level Non-Cooperative Game Systems Based on Unit Resource Return," Energies, MDPI, vol. 18(9), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2396-:d:1650915
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/9/2396/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/9/2396/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seyyed Ali Sadat & Kashish Mittal & Joshua M. Pearce, 2025. "Using Investments in Solar Photovoltaics as Inflation Hedges," Energies, MDPI, vol. 18(4), pages 1-27, February.
    2. Zhao, Bingxu & Duan, Pengfei & Fen, Mengdan & Xue, Qingwen & Hua, Jing & Yang, Zhuoqiang, 2023. "Optimal operation of distribution networks and multiple community energy prosumers based on mixed game theory," Energy, Elsevier, vol. 278(PB).
    3. Hong, Qiuyi & Meng, Fanlin & Liu, Jian & Bo, Rui, 2023. "A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets," Applied Energy, Elsevier, vol. 330(PA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Félix González & Paul Arévalo & Luis Ramirez, 2025. "Game Theory and Robust Predictive Control for Peer-to-Peer Energy Management: A Pathway to a Low-Carbon Economy," Sustainability, MDPI, vol. 17(5), pages 1-23, February.
    2. Wu, Chenyu & Gu, Wei & Yi, Zhongkai & Chen, Xi & Shi, Zhengkun & Luo, Enbo, 2023. "A multi-rate hybrid model for real-time iterative bidding coupled with power system dynamics," Applied Energy, Elsevier, vol. 337(C).
    3. Suryakiran, B.V. & Nizami, Sohrab & Verma, Ashu & Saha, Tapan Kumar & Mishra, Sukumar, 2023. "A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network," Energy, Elsevier, vol. 282(C).
    4. Li, Kang & Duan, Pengfei & Cao, Xiaodong & Cheng, Yuanda & Zhao, Bingxu & Xue, Qingwen & Feng, Mengdan, 2024. "A multi-energy load forecasting method based on complementary ensemble empirical model decomposition and composite evaluation factor reconstruction," Applied Energy, Elsevier, vol. 365(C).
    5. Li, Xinyan & Wu, Nan & Lei, Lin, 2025. "Nash-Stackelberg-Nash three-layer mixed game optimal control strategy for multi-integrated energy systems considering multiple uncertainties," Energy, Elsevier, vol. 320(C).
    6. Wang, Y.X. & Chen, J.J. & Zhao, Y.L. & Xu, B.Y., 2024. "Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park," Energy, Elsevier, vol. 301(C).
    7. Volpato, Gabriele & Carraro, Gianluca & De Giovanni, Luigi & Dal Cin, Enrico & Danieli, Piero & Bregolin, Edoardo & Lazzaretto, Andrea, 2024. "A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community," Renewable Energy, Elsevier, vol. 237(PA).
    8. Dong, Lei & Zhang, Shiming & Zhang, Tao & Wang, Zibo & Qiao, Ji & Pu, Tianjiao, 2024. "DSO-prosumers dual-layer game optimization based on risk price guidance in a P2P energy market environment," Applied Energy, Elsevier, vol. 361(C).
    9. Yu, Jie & Chen, Lu & Wang, Qiong & Zhang, Xi & Sun, Qinghe, 2024. "Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning," Energy, Elsevier, vol. 305(C).
    10. Agnieszka Bus & Michał Hasny & Edyta Hewelke & Anna Szelągowska, 2025. "The Power of Sun—A Comparative Cost–Benefit Analysis of Residential PV Systems in Poland," Sustainability, MDPI, vol. 17(12), pages 1-20, June.
    11. Cheng, Xiaoyuan & Yao, Ruiqiu & Postnikov, Andrey & Hu, Yukun & Varga, Liz, 2024. "Decentralized intelligent multi-party competitive aggregation framework for electricity prosumers," Applied Energy, Elsevier, vol. 373(C).
    12. Hosseini Dolatabadi, Sayed Hamid & Bhuiyan, Tanveer Hossain & Chen, Yang & Morales, Jose Luis, 2024. "A stochastic game-theoretic optimization approach for managing local electricity markets with electric vehicles and renewable sources," Applied Energy, Elsevier, vol. 368(C).
    13. Yayun Yang & Lingying Pan, 2024. "An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies," Energies, MDPI, vol. 17(17), pages 1-20, September.
    14. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    15. Yang, Yuyan & Xu, Xiao & Pan, Li & Liu, Junyong & Liu, Jichun & Hu, Weihao, 2024. "Distributed prosumer trading in the electricity and carbon markets considering user utility," Renewable Energy, Elsevier, vol. 228(C).
    16. Leila Bagherzadeh & Innocent Kamwa, 2023. "Joint Multi-Objective Allocation of Parking Lots and DERs in Active Distribution Network Considering Demand Response Programs," Energies, MDPI, vol. 16(23), pages 1-37, November.
    17. Zhao, Wenna & Ma, Kai & Yang, Jie & Guo, Shiliang, 2024. "A multi-time scale demand response scheme based on noncooperative game for economic operation of industrial park," Energy, Elsevier, vol. 302(C).
    18. Meng, Fanlin & Ma, Qian & Liu, Zixu & Zeng, Xiao-Jun, 2023. "Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids," Applied Energy, Elsevier, vol. 333(C).
    19. Gianluca Carraro & Enrico Dal Cin & Sergio Rech, 2024. "Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities," Energies, MDPI, vol. 17(24), pages 1-20, December.
    20. Zhao, Bingxu & Cao, Xiaodong & Duan, Pengfei, 2024. "Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties," Energy, Elsevier, vol. 297(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2396-:d:1650915. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.