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

Yixin Li, Weijie Wu, Haotian Yang, Guoxian Gong, Yining Zhang, Shuxin Luo, Shucan Zhou and Peng Wang ()
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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

Energies, 2024, vol. 17, issue 24, 1-16

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.

Keywords: source–network–storage; power system planning; dynamic carbon emission factors; inverse modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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