IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v402y2026ipbs0306261925017003.html

Edge-based real-time tracking of carbon emission flow in power systems with dynamic network pruning

Author

Listed:
  • Li, Liang
  • Zhao, Jian
  • Deng, Kai

Abstract

Carbon emission flow is a virtual network flow attached to the physical power flow. Accurate tracking of carbon emission flow distribution within power systems constitutes a critical prerequisite for enhancing carbon emission accounting frameworks and refining responsibility allocation mechanisms. However, existing carbon emission flow models lack dynamic tracking capability for renewable-induced carbon emission flow variations. In this context, this paper proposes a lightweight edge computing method based on dynamic network pruning for real-time tracking of carbon emission flows in power systems. First, a coupled spatiotemporal graph convolutional network-transformer model is developed to accurately capture dynamic variations in grid carbon emission flow distribution induced by PV output and load fluctuations. Second, an adaptive mechanism is established, correlating core model structural redundancy with real-time measured PV output volatility to determine model simplification feasibility. This mechanism specifically identifies suitable periods for simplification when substantial carbon emission flow shifts are unlikely. Then, a dynamic pruning method is developed on edge devices to adaptively lighten the tracking model guided by this feasibility determination at resource-constrained edge devices. Finally, the effectiveness of proposed algorithms is verified in power systems with a complex network.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925017003
    DOI: 10.1016/j.apenergy.2025.126970
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925017003
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.126970?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. de Chalendar, Jacques A. & Benson, Sally M., 2021. "A physics-informed data reconciliation framework for real-time electricity and emissions tracking," Applied Energy, Elsevier, vol. 304(C).
    2. Xia, Yingqi & Sun, Gengchen & Wang, Yanfeng & Yang, Qing & Wang, Qingrui & Ba, Shusong, 2024. "A novel carbon emission estimation method based on electricity‑carbon nexus and non-intrusive load monitoring," Applied Energy, Elsevier, vol. 360(C).
    3. Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).
    4. Zhang, Anshan & Wang, Feiliang & Li, Huanyu & Pang, Bo & Yang, Jian, 2024. "Carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas based on digital twin," Applied Energy, Elsevier, vol. 376(PB).
    5. Hu, Junjie & Wang, Yudong & Dong, Lei, 2024. "Low carbon-oriented planning of shared energy storage station for multiple integrated energy systems considering energy-carbon flow and carbon emission reduction," Energy, Elsevier, vol. 290(C).
    6. 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).
    7. Lee, Sangyoon & Prabawa, Panggah & Choi, Dae-Hyun, 2025. "Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning," Applied Energy, Elsevier, vol. 379(C).
    8. Dong, Wei & Chen, Chaofan & Fang, Xiaolun & Zhang, Fan & Yang, Qiang, 2024. "Enhanced integrated energy system planning through unified model coupling multiple energy and carbon emission flows," Energy, Elsevier, vol. 307(C).
    9. Zhong, Weiyi & Zhai, Dengshuai & Xu, Wenran & Gong, Wenwen & Yan, Chao & Zhang, Yang & Qi, Lianyong, 2024. "Accurate and efficient daily carbon emission forecasting based on improved ARIMA," Applied Energy, Elsevier, vol. 376(PA).
    10. Kohút, Roman & Klaučo, Martin & Kvasnica, Michal, 2025. "Unified carbon emissions and market prices forecasts of the power grid," Applied Energy, Elsevier, vol. 377(PC).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Di Wang & Sha Li & Xiaojin Fu, 2024. "Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention," Energies, MDPI, vol. 17(16), pages 1-23, August.
    2. Li, Songrui & Zhu, Chunxu, 2026. "Towards intra-provincial and inter-provincial low-carbon balanced development: a two-level carbon compensation mechanism for rural energy based on carbon deficit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
    3. Josivan Leite Alves & Rachel Perez Palha & Adiel Teixeira de Almeida Filho, 2025. "BIM-Based Framework for Photovoltaic Systems: Advancing Technologies, Overcoming Challenges, and Enhancing Sustainable Building Performance," Sustainability, MDPI, vol. 17(8), pages 1-41, April.
    4. Jiaolong Gou & Xudong Niu & Xi Chen & Shuxin Dong & Jing Xin, 2025. "Identification of Abnormal Electricity Consumption Behavior of Low-Voltage Users in New Power Systems Based on a Combined Method," Energies, MDPI, vol. 18(10), pages 1-17, May.
    5. Ma, Changwei & Dong, Wei & Shi, Yongtao & Zhang, Fan & Yang, Qiang, 2025. "A fuzzy logic dispatching method based on model predictive control for adaptively addressing uncertain operation scenarios in multi-energy systems," Energy, Elsevier, vol. 322(C).
    6. Han, Hui & Lin, Shuifa & Li, Weijiao & Li, Jiaming & Lin, Jianyi & Jing, Rui, 2025. "Integrating hourly quasi-input-output into multi-city power system planning considering spatio-temporal emission variations," Applied Energy, Elsevier, vol. 391(C).
    7. Li, Yanbin & Hu, Weikun & Zhang, Feng & Li, Yun, 2025. "Multi-objective collaborative operation optimization of park-level integrated energy system clusters considering green power forecasting and trading," Energy, Elsevier, vol. 319(C).
    8. Duaa Ashtar & Seyed Sahand Mohammadi Ziabari & Ali Mohammed Mansoor Alsahag, 2025. "Hybrid Forecasting for Sustainable Electricity Demand in The Netherlands Using SARIMAX, SARIMAX-LSTM, and Sequence-to-Sequence Deep Learning Models," Sustainability, MDPI, vol. 17(16), pages 1-22, August.
    9. Wang, Pei & Xu, Shijie & Hu, Bangjie & Tai, Nengling & Vafai, Kambiz, 2025. "Modeling on carbon emission flow in micro energy grid optimal dispatching considering source-load coordination," Energy, Elsevier, vol. 333(C).
    10. Wu, Biao & Zhang, Shaohua & Yuan, Chenxin & Wang, Xian & Wang, Fei & Zhang, Shengqi, 2024. "Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory," Energy, Elsevier, vol. 313(C).
    11. Lv, You & Tian, Helu & Liao, Conglin & Fang, Fang & Liu, Jizhen, 2026. "Multi-time scale optimal scheduling of green energy data centers considering Carnot batteries," Renewable Energy, Elsevier, vol. 257(C).
    12. Ma, Runzhuo & Bu, Siqi, 2025. "Evaluation and mitigation of carbon emissions in energy industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    13. Wu, Dongge & Chang, Xinyue & Xue, Yixun & Huang, Yuxi & Su, Jia & Sun, Hongbin, 2024. "Bilevel low-carbon coordinated operation of integrated energy systems considering dynamic tiered carbon pricing methodology," Energy, Elsevier, vol. 310(C).
    14. Wenjie Guo & Jie Liu & Jun Ma & Zheng Lan, 2025. "Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine," Energies, MDPI, vol. 18(10), pages 1-17, May.
    15. Yang, Zhongsen & Wang, Yong & Fan, Neng & Wen, Shixiong & Kuang, Wenyu & Yang, Mou & Sapnken, Flavian Emmanuel & Narayanan, Govindasami & Li, Hong-Li, 2025. "A novel fractional order grey Euler model and its application in clean energy prediction," Energy, Elsevier, vol. 322(C).
    16. 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).
    17. Tao, Jiaxin & Duan, Jiandong & Tuo, Lihao & Gao, Qi & Tian, Qinxing & Lu, Wenchao, 2025. "Exergy efficiency based multi-objective configuration optimization of energy hubs in the multi-energy distribution system," Energy, Elsevier, vol. 329(C).
    18. Fang, Guochang & Meng, Aoxiang & Wang, Qingling & Zhou, Huixin & Tian, Lixin, 2024. "Analysis of the evolution path of new energy system under polymorphic uncertainty—A case study of China," Energy, Elsevier, vol. 300(C).
    19. Siting Hong & Ting Fu & Ming Dai, 2025. "Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces," Sustainability, MDPI, vol. 17(5), pages 1-29, February.
    20. Wang, Pei & Xue, Weixian & Li, Zhi, 2025. "How does peer firms' ESG performance affect carbon emission reductions ?," Finance Research Letters, Elsevier, vol. 83(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925017003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.