IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v227y2021ics0360544221006320.html
   My bibliography  Save this article

Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants

Author

Listed:
  • Nakamura, Kotaro
  • Muramatsu, Takehiko
  • Ogawa, Takashi
  • Nakagaki, Takao

Abstract

As variable renewable energy expands, natural gas-fired combined cycle (NGCC) power plants are expected to provide important functions for grid stabilization such as quick start-up, shutdown, and load following. However, transient operation of NGCC significantly increases the NO2 content of NOX in the exhaust gas and reduces de-NOx performance of selective catalytic reduction (SCR) with ammonia injection. The denitrification performance of SCR depends on transient mechanisms such as adsorption of ammonia in the catalyst and redox reactions on the catalyst surface. This study evaluates whether de-NOX performance can be maintained during expected operational fluctuations for grids with a high penetration of variable renewable energy. Simulations involving a modified de-NOX reaction scheme have been developed and validated for various NGCC exhaust gas compositions expected in both steady state and transient operation using a commercial, monolithic SCR catalyst. Results show that sudden output load changes cause a decrease in SCR catalyst performance due to changes in gas composition and temperature outpacing the adsorption/desorption of ammonia in the catalyst. It was found that adjusting the injection amount of ammonia several minutes prior to the output load change was effective in maintaining de-NOX performance.

Suggested Citation

  • Nakamura, Kotaro & Muramatsu, Takehiko & Ogawa, Takashi & Nakagaki, Takao, 2021. "Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221006320
    DOI: 10.1016/j.energy.2021.120383
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221006320
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120383?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    2. Shin‐ichi Inage, 2015. "The role of large‐scale energy storage under high shares of renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(1), pages 115-132, January.
    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. Zhang, Xin & Chen, Zhichao & Hou, Jian & Liu, Zheng & Zeng, Lingyan & Li, Zhengqi, 2022. "Evaluation of wide-range coal combustion performance of a novel down-fired combustion technology based on gas–solid two-phase flow characteristics," Energy, Elsevier, vol. 248(C).
    2. Yan, Peiliang & Fan, Weijun & Zhang, Rongchun, 2023. "Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization," Energy, Elsevier, vol. 273(C).
    3. Ouyang, Tiancheng & Tan, Jiaqi & Wu, Wencong & Xie, Shutao & Li, Difan, 2022. "Energy, exergy and economic benefits deriving from LNG-fired power plant: Cold energy power generation combined with carbon dioxide capture," Renewable Energy, Elsevier, vol. 195(C), pages 214-229.

    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. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    2. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    3. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    5. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    6. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    7. Heymann, Fabian & Miranda, Vladimiro & Soares, Filipe Joel & Duenas, Pablo & Perez Arriaga, Ignacio & Prata, Ricardo, 2019. "Orchestrating incentive designs to reduce adverse system-level effects of large-scale EV/PV adoption – The case of Portugal," Applied Energy, Elsevier, vol. 256(C).
    8. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    9. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
    10. Shi, Jiaqi & Liu, Nian & Huang, Yujing & Ma, Liya, 2022. "An Edge Computing-oriented Net Power Forecasting for PV-assisted Charging Station: Model Complexity and Forecasting Accuracy Trade-off," Applied Energy, Elsevier, vol. 310(C).
    11. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    12. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    13. Eppinger, Bernd & Zigan, Lars & Karl, Jürgen & Will, Stefan, 2020. "Pumped thermal energy storage with heat pump-ORC-systems: Comparison of latent and sensible thermal storages for various fluids," Applied Energy, Elsevier, vol. 280(C).
    14. Steinmann, Wolf-Dieter, 2017. "Thermo-mechanical concepts for bulk energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 205-219.
    15. Rubasinghe, Osaka & Zhang, Tingze & Zhang, Xinan & Choi, San Shing & Chau, Tat Kei & Chow, Yau & Fernando, Tyrone & Iu, Herbert Ho-Ching, 2023. "Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration," Applied Energy, Elsevier, vol. 333(C).
    16. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    17. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    18. Li, Tianxiao & Li, Zheng & Li, Weiqi, 2020. "Scenarios analysis on the cross-region integrating of renewable power based on a long-period cost-optimization power planning model," Renewable Energy, Elsevier, vol. 156(C), pages 851-863.
    19. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    20. Chi Hua & Erxi Zhu & Liang Kuang & Dechang Pi, 2019. "Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.

    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:energy:v:227:y:2021:i:c:s0360544221006320. 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.journals.elsevier.com/energy .

    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.