IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5884-d1527741.html
   My bibliography  Save this article

Optimal Operation of Generation Company’s Participating in Multiple Markets with Allocation and Exchange of Energy-Consuming Rights and Carbon Credits

Author

Listed:
  • Hanyu Yang

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Mengru Ding

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Muyao Li

    (China Electric Power Research Institute (Nanjing), Nanjing 210003, China)

  • Shilei Wu

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Ye Zhang

    (Inner Mongolia Power Electric Operations Control Company, Inner Mongolia Electric Power (Group) Co., Ltd., Hohhot 010010, China)

  • Xun Dou

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

Abstract

The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the carbon emission reduction accurately for the GenCos that participate in electricity, carbon, and ECR markets simultaneously, this paper proposes a market framework where a flexible exchange mechanism between the ECR and carbon markets is specially considered. To investigate the influence of the allocation and exchange of ECR and carbon credits on the behavior of GenCos that participate in multi-type markets, a bi-level model based on the leader–follower game theory is proposed. In the upper level of the proposed model, a decision problem for maximizing the profit of GenCos is developed, which is especially constrained to the primary allocation of ECR and carbon credits. While the multi-type market clearing model and an exchange mechanism between the ECR and carbon credits are proposed in the lower level of the model. The bi-level problem is converted into the mathematical program with equilibrium constraints (MPECs) through the Karush–Kuhn–Tucker (KKT) condition to solve. The results illustrate that the interaction between the ECR market and the carbon market can improve the energy efficiency and reduce the carbon emissions of GenCos.

Suggested Citation

  • Hanyu Yang & Mengru Ding & Muyao Li & Shilei Wu & Ye Zhang & Xun Dou, 2024. "Optimal Operation of Generation Company’s Participating in Multiple Markets with Allocation and Exchange of Energy-Consuming Rights and Carbon Credits," Energies, MDPI, vol. 17(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5884-:d:1527741
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5884/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/5884/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Yibing & Wang, Can & Sun, Yuwei & Liu, Xianbing, 2018. "Factors influencing companies' willingness to pay for carbon emissions: Emission trading schemes in China," Energy Economics, Elsevier, vol. 75(C), pages 357-367.
    2. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    3. Menegaki, Angeliki N. & Ahmad, Nisar & Aghdam, Reza FathollahZadeh & Naz, Amber, 2021. "The convergence in various dimensions of energy-economy-environment linkages: A comprehensive citation-based systematic literature review," Energy Economics, Elsevier, vol. 104(C).
    4. Wang, Kaike & Su, Xuewei & Wang, Shuhong, 2023. "How does the energy-consuming rights trading policy affect China's carbon emission intensity?," Energy, Elsevier, vol. 276(C).
    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. Du, Minzhe & Wu, Fenger & Luo, Lichun & Wang, Qiya & Liao, Liping, 2025. "Spatial effects of the market-based energy allocation on energy efficiency: A quasi-natural experiment of energy quota trading," Energy, Elsevier, vol. 318(C).
    2. Göteman, Malin & Panteli, Mathaios & Rutgersson, Anna & Hayez, Léa & Virtanen, Mikko J. & Anvari, Mehrnaz & Johansson, Jonas, 2025. "Resilience of offshore renewable energy systems to extreme metocean conditions: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    3. Qiu, Zhaoxuan & Li, Jincheng & Liu, Bei & Jin, Meilin & Wang, Jinmin, 2025. "How does energy quota trading affect the corporate pollution gap? Evidence from China," Economic Modelling, Elsevier, vol. 146(C).
    4. Wang, Weilong & Wang, Jianlong & Wu, Haitao, 2024. "The impact of energy-consuming rights trading on green total factor productivity in the context of digital economy: Evidence from listed firms in China," Energy Economics, Elsevier, vol. 131(C).
    5. He, Yan & Xiao, Jiang-Wen & Wang, Yan-Wu & Liu, Zhi-Wei & He, Shi-Yuan, 2025. "Subjective-uncertainty-oriented dynamic renting framework for energy storage sharing," Applied Energy, Elsevier, vol. 378(PA).
    6. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Assessing the policy synergy among power, carbon emissions trading and tradable green certificate market mechanisms on strategic GENCOs in China," Energy, Elsevier, vol. 278(PB).
    7. Li, Zheng-Zheng & Li, Yameng & Huang, Chia-Yun & Peculea, Adelina Dumitrescu, 2023. "Volatility spillover across Chinese carbon markets: Evidence from quantile connectedness method," Energy Economics, Elsevier, vol. 119(C).
    8. Wang, Wei & Zhang, Yue-Jun, 2022. "Does China's carbon emissions trading scheme affect the market power of high-carbon enterprises?," Energy Economics, Elsevier, vol. 108(C).
    9. Guo, Bingnan & Hu, Peiji & Lin, Ji, 2024. "The effect of digital infrastructure development on enterprise green transformation," International Review of Financial Analysis, Elsevier, vol. 92(C).
    10. Hua Pan & Xin Song & Jianchao Hou & Siyi Tan, 2024. "Evolutionary Game Analysis Between Large Power Consumers and Power Sellers in the Context of Big-Data-Driven Value-Added Services," Sustainability, MDPI, vol. 16(20), pages 1-17, October.
    11. Zheng, Qingying & Hu, Haoyang & Li, Jianglong, 2024. "Enterprise decision-making in energy use rights trading market: A theoretical and simulation study," Energy Policy, Elsevier, vol. 193(C).
    12. Wang, Meiling & Liu, Zichen & Zhou, Bingxuan, 2025. "Towards carbon neutrality: The impact of energy right trading policy on carbon performance of manufacturing enterprises," Energy, Elsevier, vol. 323(C).
    13. Li, Xingwei & Yang, Jie & Yao, Yuhong & Ding, Zuoyi & Hao, Jiekuo & Yin, Wenjun & Shen, Qiong, 2024. "Boosting green technology innovation in China's construction industry: The power of energy-consuming rights trading policy," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 410-423.
    14. Qiuyan Yan & Kai Wan, 2024. "Energy-Consuming Right Trading Policy and Corporate ESG Performance: Quasi-Natural Experimental Evidence from China," Energies, MDPI, vol. 17(13), pages 1-20, July.
    15. Lan, Liuhan & Zhang, Xingping & Ng, Tsan Sheng & Wang, Teng & Tan, Qinliang, 2025. "Bidding behaviors of coal-fired units in China's electricity market considering previous capacity revenues based on prospect theory," Energy, Elsevier, vol. 322(C).
    16. Jiemin Huang & Jiaoju Ge & Kai Chang & Yixiang Tian, 2020. "Dynamic hedging analysis of carbon emission trading yield in Shenzhen," Energy & Environment, , vol. 31(5), pages 870-885, August.
    17. Zhang, He & Gong, Zhenting & Yang, Yunglieh & Chen, Fan, 2023. "Dynamic connectedness between China green bond, carbon market and traditional financial markets: Evidence from quantile connectedness approach," Finance Research Letters, Elsevier, vol. 58(PC).
    18. Zhang, Yue & Wu, Qiong & Ren, Hongbo & Li, Qifen & Zhou, Weisheng, 2025. "A multi-agent distributed electricity-carbon shared trading strategy considering regional carbon inclusion," Renewable Energy, Elsevier, vol. 239(C).
    19. Yongliang Yang & Yiyang Guo & Suqing Luo, 2020. "Consumers’ Intention and Cognition for Low-Carbon Behavior: A Case Study of Hangzhou in China," Energies, MDPI, vol. 13(21), pages 1-19, November.
    20. Zhang, Yue & Zhao, Xin-gang & Zhou, Ying & Wang, Hui, 2025. "Impact of combined environmental policies on carbon emission reduction: A system dynamics analysis," Utilities Policy, Elsevier, vol. 94(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:17:y:2024:i:23:p:5884-:d:1527741. 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.