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Carbon responsibility allocation method based on complex structure carbon emission flow theory
A carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution char...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883471/ https://www.ncbi.nlm.nih.gov/pubmed/36707638 http://dx.doi.org/10.1038/s41598-023-28518-y |
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author | Wang, Wenyong Huo, Qunhai Deng, Huawei Yin, Jingyuan Wei, Tongzhen |
author_facet | Wang, Wenyong Huo, Qunhai Deng, Huawei Yin, Jingyuan Wei, Tongzhen |
author_sort | Wang, Wenyong |
collection | PubMed |
description | A carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution characteristics, and mechanism of carbon emission flow were analyzed. The “carbon potential of complex structure” concept was introduced to track “carbon trajectory” and “green trajectory” by harnessing the ability of complex structures to retain two-dimensional information. Subsequently, the carbon responsibility allocation methods for network loss and users’ electricity consumption behavior were developed to realize the accurate carbon responsibility allocation of each system link. Finally, the effectiveness and advancement of the proposed carbon responsibility allocation method were verified using the improved IEEE 6-bus and 30-bus test systems. The application of the proposed complex structure carbon potential in the carbon emission flow theory expands the research dimension of electric power carbon emission for low-carbon development from the “carbon perspective,” provides a novel optimization space for the operation of the distribution network and realizes the carbon emission flow theory, which serves as a bridge from calculation evaluation to optimization decision. |
format | Online Article Text |
id | pubmed-9883471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98834712023-01-29 Carbon responsibility allocation method based on complex structure carbon emission flow theory Wang, Wenyong Huo, Qunhai Deng, Huawei Yin, Jingyuan Wei, Tongzhen Sci Rep Article A carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution characteristics, and mechanism of carbon emission flow were analyzed. The “carbon potential of complex structure” concept was introduced to track “carbon trajectory” and “green trajectory” by harnessing the ability of complex structures to retain two-dimensional information. Subsequently, the carbon responsibility allocation methods for network loss and users’ electricity consumption behavior were developed to realize the accurate carbon responsibility allocation of each system link. Finally, the effectiveness and advancement of the proposed carbon responsibility allocation method were verified using the improved IEEE 6-bus and 30-bus test systems. The application of the proposed complex structure carbon potential in the carbon emission flow theory expands the research dimension of electric power carbon emission for low-carbon development from the “carbon perspective,” provides a novel optimization space for the operation of the distribution network and realizes the carbon emission flow theory, which serves as a bridge from calculation evaluation to optimization decision. Nature Publishing Group UK 2023-01-27 /pmc/articles/PMC9883471/ /pubmed/36707638 http://dx.doi.org/10.1038/s41598-023-28518-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Wenyong Huo, Qunhai Deng, Huawei Yin, Jingyuan Wei, Tongzhen Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title | Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title_full | Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title_fullStr | Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title_full_unstemmed | Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title_short | Carbon responsibility allocation method based on complex structure carbon emission flow theory |
title_sort | carbon responsibility allocation method based on complex structure carbon emission flow theory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883471/ https://www.ncbi.nlm.nih.gov/pubmed/36707638 http://dx.doi.org/10.1038/s41598-023-28518-y |
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