Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6346 |
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| author | Yixin Li Weijie Wu Haotian Yang Guoxian Gong Yining Zhang Shuxin Luo Shucan Zhou Peng Wang |
| author_facet | Yixin Li Weijie Wu Haotian Yang Guoxian Gong Yining Zhang Shuxin Luo Shucan Zhou Peng Wang |
| author_sort | Yixin Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-675b00ba0e6b4cc0a84bc1f8699fd5d9 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-675b00ba0e6b4cc0a84bc1f8699fd5d92025-08-20T02:53:29ZengMDPI AGEnergies1996-10732024-12-011724634610.3390/en17246346Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse ModellingYixin Li0Weijie Wu1Haotian Yang2Guoxian Gong3Yining Zhang4Shuxin Luo5Shucan Zhou6Peng Wang7Grid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, ChinaGrid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100190, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100190, ChinaGrid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, ChinaGrid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, ChinaGrid Planning and Research Center, Guangdong Power Grid Corporation, Guangzhou 510220, ChinaDepartment of Electrical Engineering, Tsinghua University, Beijing 100190, ChinaIn 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.https://www.mdpi.com/1996-1073/17/24/6346source–network–storagepower system planningdynamic carbon emission factorsinverse modeling |
| spellingShingle | Yixin Li Weijie Wu Haotian Yang Guoxian Gong Yining Zhang Shuxin Luo Shucan Zhou Peng Wang Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling Energies source–network–storage power system planning dynamic carbon emission factors inverse modeling |
| title | Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling |
| title_full | Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling |
| title_fullStr | Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling |
| title_full_unstemmed | Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling |
| title_short | Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling |
| title_sort | dynamic carbon emission factors in source network storage power system planning a focus on inverse modelling |
| topic | source–network–storage power system planning dynamic carbon emission factors inverse modeling |
| url | https://www.mdpi.com/1996-1073/17/24/6346 |
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