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Power Generation Mix Optimization under Auction Mechanism for Carbon Emission Rights

Author

Listed:
  • Erdong Zhao

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Jianmin Chen

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Junmei Lan

    (International Business School, University of International Business and Economics, Beijing 100029, China)

  • Liwei Liu

    (International Business School, University of International Business and Economics, Beijing 100029, China)

Abstract

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China’s future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.

Suggested Citation

  • Erdong Zhao & Jianmin Chen & Junmei Lan & Liwei Liu, 2024. "Power Generation Mix Optimization under Auction Mechanism for Carbon Emission Rights," Energies, MDPI, vol. 17(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:617-:d:1327701
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    References listed on IDEAS

    as
    1. Chen, Hao & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2016. "A multi-period power generation planning model incorporating the non-carbon external costs: A case study of China," Applied Energy, Elsevier, vol. 183(C), pages 1333-1345.
    2. Stringer, Thomas & Joanis, Marcelin & Abdoli, Shiva, 2024. "Power generation mix and electricity price," Renewable Energy, Elsevier, vol. 221(C).
    3. Cai, Jinyang & Zheng, Huanyu & Vardanyan, Michael & Shen, Zhiyang, 2023. "Achieving carbon neutrality through green technological progress: evidence from China," Energy Policy, Elsevier, vol. 173(C).
    4. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
    5. Zhang, Yiyi & Wang, Jiaqi & Zhang, Linmei & Liu, Jiefeng & Zheng, Hanbo & Fang, Jiake & Hou, Shengren & Chen, Shaoqing, 2020. "Optimization of China’s electric power sector targeting water stress and carbon emissions," Applied Energy, Elsevier, vol. 271(C).
    6. Zhou, X. & Fan, L.W. & Zhou, P., 2015. "Marginal CO2 abatement costs: Findings from alternative shadow price estimates for Shanghai industrial sectors," Energy Policy, Elsevier, vol. 77(C), pages 109-117.
    7. Liu, Liwei & Sun, Xiaoru & Chen, Chuxiang & Zhao, Erdong, 2016. "How will auctioning impact on the carbon emission abatement cost of electric power generation sector in China?," Applied Energy, Elsevier, vol. 168(C), pages 594-609.
    8. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    9. Zhang, Shuang & Zhao, Tao & Xie, Bai-Chen, 2018. "What is the optimal power generation mix of China? An empirical analysis using portfolio theory," Applied Energy, Elsevier, vol. 229(C), pages 522-536.
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