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Modeling and Control of Supercritical and Ultra-Supercritical Power Plants: A Review

Author

Listed:
  • Omar Mohamed

    (King Abdullah II School of Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan)

  • Ashraf Khalil

    (Electrical and Electronic Engineering Department, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam)

  • Jihong Wang

    (School of Engineering, University of Warwick, Coventry CV4 7AL, UK)

Abstract

This paper presents a critical review of the research conducted for modeling and controlling supercritical power plants. Thermal power plants are classified according to the boiler pressure to supercritical and subcritical. The modeling concepts and control strategies of supercritical generation units are far more complex than those of subcritical. On the other hand, supercritical generation technologies are more efficient and much cleaner than subcritical generation units. From a deep technical analysis of the literature, there is no review that is dedicated to models-based control of supercritical power plants and most previous reviews are found to be too general to modeling-based control of fossil fuelled energy sources. This review reports the advancements on modeling and control of supercritical and ultra-supercritical plants as cleaner generation technologies. The various published achievements for modeling supercritical and ultra-supercritical units have been reviewed. The control strategies that fulfill the practical load demand requirements while keeping optimum efficiencies are also reviewed. Finally, expected future directions are reported as recommendations to overcome future challenges. The paper can be used as a brief educational directory to the postgraduate students or future researchers in the field.

Suggested Citation

  • Omar Mohamed & Ashraf Khalil & Jihong Wang, 2020. "Modeling and Control of Supercritical and Ultra-Supercritical Power Plants: A Review," Energies, MDPI, vol. 13(11), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2935-:d:368609
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    References listed on IDEAS

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    Cited by:

    1. Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    2. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    3. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    4. Mohammad Qasem & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    5. Wu, Chunying & Sun, Lingfang & Piao, Heng & Yao, Lijia, 2024. "Adaptive fuzzy finite time integral sliding mode control of the coordinated system for 350 MW supercritical once-through boiler unit to enhance flexibility," Energy, Elsevier, vol. 302(C).
    6. Andrés Meana-Fernández & Juan M. González-Caballín & Roberto Martínez-Pérez & Francisco J. Rubio-Serrano & Antonio J. Gutiérrez-Trashorras, 2022. "Power Plant Cycles: Evolution towards More Sustainable and Environmentally Friendly Technologies," Energies, MDPI, vol. 15(23), pages 1-27, November.
    7. Esmaeili, Mohammad & Moradi, Hamed, 2023. "Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem," Energy, Elsevier, vol. 282(C).
    8. Tayma Afaneh & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Load Frequency Model Predictive Control of a Large-Scale Multi-Source Power System," Energies, MDPI, vol. 15(23), pages 1-21, December.

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