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. 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.
    10. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vislavath Suresh & Siddhartha Agarwal & Yoginder P. Chugh & Priyanshu Jha & Renzhong Wang, 2025. "Advancing Sustainability in Surface Coal Mines Through Real-Time Air Quality Monitoring: Low-Cost IoT Solutions and the Role of Meteorological Factors in PM and GHG Emissions," Sustainability, MDPI, vol. 17(3), pages 1-35, February.

    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. 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).
    2. 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.
    3. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    4. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    5. Fangyi Li & Zhaoyang Ye & Xilin Xiao & Dawei Ma, 2019. "Environmental Benefits of Stock Evolution of Coal-Fired Power Generators in China," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    6. 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.
    7. Guerra, K. & Haro, P. & Gutiérrez, R.E. & Gómez-Barea, A., 2022. "Facing the high share of variable renewable energy in the power system: Flexibility and stability requirements," Applied Energy, Elsevier, vol. 310(C).
    8. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    9. Gennadiy Stroykov & Alexey Y. Cherepovitsyn & Elizaveta A. Iamshchikova, 2020. "Powering Multiple Gas Condensate Wells in Russia’s Arctic: Power Supply Systems Based on Renewable Energy Sources," Resources, MDPI, vol. 9(11), pages 1-15, November.
    10. Yang, Lin & Lv, Haodong & Jiang, Dalin & Fan, Jingli & Zhang, Xian & He, Weijun & Zhou, Jinsheng & Wu, Wenjing, 2020. "Whether CCS technologies will exacerbate the water crisis in China? —A full life-cycle analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    11. Nitsch, Felix & Deissenroth-Uhrig, Marc & Schimeczek, Christoph & Bertsch, Valentin, 2021. "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets," Applied Energy, Elsevier, vol. 298(C).
    12. 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.
    13. 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.
    14. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    15. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    16. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    17. Cui, Qi & He, Ling & Han, Guoyi & Chen, Hao & Cao, Juanjuan, 2020. "Review on climate and water resource implications of reducing renewable power curtailment in China: A nexus perspective," Applied Energy, Elsevier, vol. 267(C).
    18. Xiaonan Wang & Licheng Wang & Jianping Chen & Shouting Zhang & Paolo Tarolli, 2020. "Assessment of the External Costs of Life Cycle of Coal: The Case Study of Southwestern China," Energies, MDPI, vol. 13(15), pages 1-26, August.
    19. 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.
    20. Gustavo G. Koch & Caio R. D. Osório & Ricardo C. L. F. Oliveira & Vinícius F. Montagner, 2023. "Robust Control Based on Observed States Designed by Means of Linear Matrix Inequalities for Grid-Connected Converters," Energies, MDPI, vol. 16(4), pages 1-24, February.

    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.