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A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems

Author

Listed:
  • Yang, Chao
  • Liang, Gaoqi
  • Liu, Jinjie
  • Liu, Guolong
  • Yang, Hongming
  • Zhao, Junhua
  • Dong, Zhaoyang

Abstract

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers “prosumers” arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs.

Suggested Citation

  • Yang, Chao & Liang, Gaoqi & Liu, Jinjie & Liu, Guolong & Yang, Hongming & Zhao, Junhua & Dong, Zhaoyang, 2023. "A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010760
    DOI: 10.1016/j.apenergy.2023.121712
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    References listed on IDEAS

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    Cited by:

    1. Li, Liang & Zhao, Jian & Deng, Kai, 2026. "Edge-based real-time tracking of carbon emission flow in power systems with dynamic network pruning," Applied Energy, Elsevier, vol. 402(PB).
    2. Hu, Guoqing & You, Fengqi, 2025. "Exploring sustainable solutions in PV-integrated indoor farming: Energy, economic, and environmental insights from major U.S. cities," Applied Energy, Elsevier, vol. 399(C).
    3. Luo, Qingquan & Yu, Tao & Liang, Minhang & Pan, Zhenning & Guo, Wenlong & Hu, Xiaolei, 2025. "Review of advances in scaling non-intrusive load monitoring for real-world applications," Applied Energy, Elsevier, vol. 398(C).
    4. Xiao, Guolin & Lang, Qi & Gao, Xiaori & Lu, Wei & Liu, Xiaodong, 2025. "Enhancing parameter prediction in gas-fired boiler systems through node similarity-based feature aggregation," Energy, Elsevier, vol. 328(C).
    5. Zhao, Huirong & Yuan, Shuying & Peng, Daogang & Li, Tianchen, 2026. "A two-stage optimization operation method for low-carbon demand response in molten salt manufacturing process considering dynamic carbon potential and price signals of grid electricity," Renewable Energy, Elsevier, vol. 256(PA).

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