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Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling

Author

Listed:
  • Yixin Li

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, China)

  • Weijie Wu

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, China)

  • Haotian Yang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100190, China)

  • Guoxian Gong

    (Department of Electrical Engineering, Tsinghua University, Beijing 100190, China)

  • Yining Zhang

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, China)

  • Shuxin Luo

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, China)

  • Shucan Zhou

    (Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, China)

  • Peng Wang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100190, China)

Abstract

In light of global climate change, China has set strategic goals for carbon peaking by 2030 and carbon neutrality by 2060, emphasizing the necessity of constructing a new power system with a high proportion of renewable energy sources. As coal-fired power plants are the main carbon emissions source in the power system, their low-carbon transition and morphology structure optimization is crucial. This paper explores the critical role of dynamic carbon emission factors within source–network–storage power system planning and proposes an innovative inverse dynamic carbon emission factor that effectively captures the nonlinear relationship between load rates and emissions. Comparative analyses using the HRP-38 test case demonstrate that the inverse model enhances computational efficiency, reduces solution times, and more accurately reflects the emissions characteristics of coal-fired units across varying operational conditions. Furthermore, the inverse model offers improved economic performance and broader flexibility in unit selection, highlighting its potential to balance carbon emissions control and economic optimization in future power system planning.

Suggested Citation

  • Yixin Li & Weijie Wu & Haotian Yang & Guoxian Gong & Yining Zhang & Shuxin Luo & Shucan Zhou & Peng Wang, 2024. "Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling," Energies, MDPI, vol. 17(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6346-:d:1545481
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    References listed on IDEAS

    as
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    2. Wang, Rutian & Wen, Xiangyun & Wang, Xiuyun & Fu, Yanbo & Zhang, Yu, 2022. "Low carbon optimal operation of integrated energy system based on carbon capture technology, LCA carbon emissions and ladder-type carbon trading," Applied Energy, Elsevier, vol. 311(C).
    3. Zhenyu Zhuo & Ershun Du & Ning Zhang & Chris P. Nielsen & Xi Lu & Jinyu Xiao & Jiawei Wu & Chongqing Kang, 2022. "Cost increase in the electricity supply to achieve carbon neutrality in China," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian, 2015. "A multi-region optimization planning model for China’s power sector," Applied Energy, Elsevier, vol. 137(C), pages 413-426.
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