IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1646-d1060295.html
   My bibliography  Save this article

A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Author

Listed:
  • Shangli Zhou

    (Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Hengjing He

    (Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Leping Zhang

    (Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Wei Zhao

    (Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Fei Wang

    (School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Reducing CO 2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO 2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO 2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO 2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO 2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement.

Suggested Citation

  • Shangli Zhou & Hengjing He & Leping Zhang & Wei Zhao & Fei Wang, 2023. "A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants," Energies, MDPI, vol. 16(4), pages 1-27, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1646-:d:1060295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1646/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/4/1646/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huiru Zhao & Guo Huang & Ning Yan, 2018. "Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China," Energies, MDPI, vol. 11(4), pages 1-21, March.
    2. Akpan, P.U. & Fuls, W.F., 2021. "Cycling of coal fired power plants: A generic CO2 emissions factor model for predicting CO2 emissions," Energy, Elsevier, vol. 214(C).
    3. Jeon, Eui-Chan & Myeong, Soojeong & Sa, Jae-Whan & Kim, Jinsu & Jeong, Jae-Hak, 2010. "Greenhouse gas emission factor development for coal-fired power plants in Korea," Applied Energy, Elsevier, vol. 87(1), pages 205-210, January.
    4. Yu, Shiwei & Wei, Yi-Ming & Guo, Haixiang & Ding, Liping, 2014. "Carbon emission coefficient measurement of the coal-to-power energy chain in China," Applied Energy, Elsevier, vol. 114(C), pages 290-300.
    5. Wierzbowski, Michal & Lyzwa, Wojciech & Musial, Izabela, 2016. "MILP model for long-term energy mix planning with consideration of power system reserves," Applied Energy, Elsevier, vol. 169(C), pages 93-111.
    6. Zheqi Yang & Xuming Dou & Yuqing Jiang & Pengfei Luo & Yu Ding & Baosheng Zhang & Xu Tang, 2022. "Tracking the CO 2 Emissions of China’s Coal Production via Global Supply Chains," Energies, MDPI, vol. 15(16), pages 1-10, August.
    7. Wang, Ning & Ren, Yixin & Zhu, Tao & Meng, Fanxin & Wen, Zongguo & Liu, Gengyuan, 2018. "Life cycle carbon emission modelling of coal-fired power: Chinese case," Energy, Elsevier, vol. 162(C), pages 841-852.
    8. Gutiérrez-Martín, F. & Da Silva-Álvarez, R.A. & Montoro-Pintado, P., 2013. "Effects of wind intermittency on reduction of CO2 emissions: The case of the Spanish power system," Energy, Elsevier, vol. 61(C), pages 108-117.
    9. Zhu, Zhi-Shuang & Liao, Hua & Cao, Huai-Shu & Wang, Lu & Wei, Yi-Ming & Yan, Jinyue, 2014. "The differences of carbon intensity reduction rate across 89 countries in recent three decades," Applied Energy, Elsevier, vol. 113(C), pages 808-815.
    10. Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2021. "The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning," Land, MDPI, vol. 10(12), pages 1-23, December.
    11. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    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. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
    2. Zhi-Fu Mi & Yi-Ming Wei & Chen-Qi He & Hua-Nan Li & Xiao-Chen Yuan & Hua Liao, 2017. "Regional efforts to mitigate climate change in China: a multi-criteria assessment approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(1), pages 45-66, January.
    3. Eising, Jan Willem & van Onna, Tom & Alkemade, Floortje, 2014. "Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains," Applied Energy, Elsevier, vol. 123(C), pages 448-455.
    4. Yang, Ranran & Long, Ruyin & Yue, Ting & Shi, Haihong, 2014. "Calculation of embodied energy in Sino-USA trade: 1997–2011," Energy Policy, Elsevier, vol. 72(C), pages 110-119.
    5. Shu-Hong Wang & Ma-Lin Song & Tao Yu, 2019. "Hidden Carbon Emissions, Industrial Clusters, and Structure Optimization in China," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1319-1342, December.
    6. Xiao, Hao & Sun, Ke-Juan & Bi, Hui-Min & Xue, Jin-Jun, 2019. "Changes in carbon intensity globally and in countries: Attribution and decomposition analysis," Applied Energy, Elsevier, vol. 235(C), pages 1492-1504.
    7. Zhao, Xueting & Wesley Burnett, J. & Lacombe, Donald J., 2015. "Province-level convergence of China’s carbon dioxide emissions," Applied Energy, Elsevier, vol. 150(C), pages 286-295.
    8. Li, Jin & Wang, Rui & Li, Haoran & Nie, Yaoyu & Song, Xinke & Li, Mingyu & Shi, Mai & Zheng, Xinzhu & Cai, Wenjia & Wang, Can, 2021. "Unit-level cost-benefit analysis for coal power plants retrofitted with biomass co-firing at a national level by combined GIS and life cycle assessment," Applied Energy, Elsevier, vol. 285(C).
    9. Qianyu Zhao & Boyu Xie & Mengyao Han, 2023. "Unpacking the Sub-Regional Spatial Network of Land-Use Carbon Emissions: The Case of Sichuan Province in China," Land, MDPI, vol. 12(10), pages 1-22, October.
    10. Guangfang Luo & Jianjun Zhang & Yongheng Rao & Xiaolei Zhu & Yiqiang Guo, 2017. "Coal Supply Chains: A Whole-Process-Based Measurement of Carbon Emissions in a Mining City of China," Energies, MDPI, vol. 10(11), pages 1-18, November.
    11. Wang, Ke & Zhang, Jianjun & Cai, Bofeng & Yu, Shengmin, 2019. "Emission factors of fugitive methane from underground coal mines in China: Estimation and uncertainty," Applied Energy, Elsevier, vol. 250(C), pages 273-282.
    12. Shaikh, Mohammad A. & Kucukvar, Murat & Onat, Nuri Cihat & Kirkil, Gokhan, 2017. "A framework for water and carbon footprint analysis of national electricity production scenarios," Energy, Elsevier, vol. 139(C), pages 406-421.
    13. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    14. Akpan, P.U. & Fuls, W.F., 2021. "Cycling of coal fired power plants: A generic CO2 emissions factor model for predicting CO2 emissions," Energy, Elsevier, vol. 214(C).
    15. Chen, Jiandong & Cheng, Shulei & Song, Malin & Wu, Yinyin, 2016. "A carbon emissions reduction index: Integrating the volume and allocation of regional emissions," Applied Energy, Elsevier, vol. 184(C), pages 1154-1164.
    16. Yang, Qing & Zhang, Lei & Zou, Shaohui & Zhang, Jinsuo, 2020. "Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security," Energy Policy, Elsevier, vol. 139(C).
    17. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    18. Gong, Chengzhu & Yu, Shiwei & Zhu, Kejun & Hailu, Atakelty, 2016. "Evaluating the influence of increasing block tariffs in residential gas sector using agent-based computational economics," Energy Policy, Elsevier, vol. 92(C), pages 334-347.
    19. Chen, Jiandong & Gao, Ming & Mangla, Sachin Kumar & Song, Malin & Wen, Jie, 2020. "Effects of technological changes on China's carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    20. Hanak, Dawid P. & Kolios, Athanasios J. & Manovic, Vasilije, 2016. "Comparison of probabilistic performance of calcium looping and chemical solvent scrubbing retrofits for CO2 capture from coal-fired power plant," Applied Energy, Elsevier, vol. 172(C), pages 323-336.

    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:gam:jeners:v:16:y:2023:i:4:p:1646-:d:1060295. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.