A spatio-temporal carbon tracing and allocation method considering indirect carbon emission responsibility of renewable energy

• A carbon tracing method considering spatio-temporal coupling relationship is built. • A carbon obligation allocation strategy based on discrete analysis theory is proposed. • The carbon responsibility for renewable energy is quantified. Clarifying the rights and responsibilities of carbon emissions is the foundation for the low-carbon transition. However, conventional allocation concentrates either on direct generator-side emissions or shifts to consumers while failing to account for the support from other generation units due to renewable energy volatility and making it inequitable to place the entire carbon emission responsibility solely on the load or other thermal power units. This paper proposes a carbon tracing method considering the spatial coupling of different units and the temporal variation of carbon emission factor, which uses the directed graph to represent the spatial coupling between generation units and loads and builds a carbon emission factor model considering the working principle of coal-fired generating units based on the Informer network. Then, a carbon obligation allocation strategy considering the indirect carbon allocation mechanism for renewable energy based on discrete analysis theory is built, enabling the quantitative assessment of the carbon responsibility of renewable energy. Finally, the effectiveness of the proposed method is demonstrated based on the IEEE 14-bus system.

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Publication Details

Journal
Electric Power Systems Research
Published
2026-02-13
DOI
https://doi.org/10.1016/j.epsr.2026.112845
Primary Topic
Integrated Energy Systems Optimization
Type
article
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article

A spatio-temporal carbon tracing and allocation method considering indirect carbon emission responsibility of renewable energy

Yufan Hong, Jingxian Yang, Junyong Liu, Shuai Zhang et al.
Electric Power Systems Research
Integrated Energy Systems Optimization
article

A spatio-temporal carbon tracing and allocation method considering indirect carbon emission responsibility of renewable energy

Yufan Hong, Jingxian Yang, Junyong Liu, Shuai Zhang, Jing Yan, Kai Wang
article en

Abstract

• A carbon tracing method considering spatio-temporal coupling relationship is built. • A carbon obligation allocation strategy based on discrete analysis theory is proposed. • The carbon responsibility for renewable energy is quantified. Clarifying the rights and responsibilities of carbon emissions is the foundation for the low-carbon transition. However, conventional allocation concentrates either on direct generator-side emissions or shifts to consumers while failing to account for the support from other generation units due to renewable energy volatility and making it inequitable to place the entire carbon emission responsibility solely on the load or other thermal power units. This paper proposes a carbon tracing method considering the spatial coupling of different units and the temporal variation of carbon emission factor, which uses the directed graph to represent the spatial coupling between generation units and loads and builds a carbon emission factor model considering the working principle of coal-fired generating units based on the Informer network. Then, a carbon obligation allocation strategy considering the indirect carbon allocation mechanism for renewable energy based on discrete analysis theory is built, enabling the quantitative assessment of the carbon responsibility of renewable energy. Finally, the effectiveness of the proposed method is demonstrated based on the IEEE 14-bus system.

Electric Power Systems ResearchVol. 255
Minzu University of China (CN), State Grid Corporation of China (China) (CN), Sichuan University (CN), Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 57%
Integrated Energy Systems Optimization
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A spatio-temporal carbon tracing and allocation method considering indirect carbon emission responsibility of renewable energy — Yufan Hong, Jingxian Yang, et al. · Electric Power Systems Research (2026) | TGRS Research Map | TGRS