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A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units

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  • Fan, He
  • Su, Zhi-gang
  • Wang, Pei-hong
  • Lee, Kwang Y.

Abstract

As the penetration of renewable energy generation increases in power grid, ultra-supercritical (USC) once-through boiler-turbine (OTBT) units are required to operate in a wide load range to maintain the grid stability. Coordinated control system (CCS) coordinates boiler and turbine systems to track load demand and ensure safe and economical operation simultaneously. However, few studies focus on modeling the USC-OTBT units for CCS design under the current stringent requirement. This study develops a dynamic nonlinear model of a USC-OTBT unit operating in a wide load range, which is a first step toward designing an effective CCS. This work first analyzes and captures the nonlinear characteristics of the USC-OTBT unit in low load and extends to model the dynamic process of the unit in a wider load range, from low load to full load. Besides, two energy functions are presented by analyzing open-loop responses to improve the model structure and dynamic modeling accuracy. Model structure is derived from mass and energy conservation laws, and model parameters and functions are identified based on running data. Validation results demonstrate the effectiveness of the model for a wide load range operation of the USC-OTBT unit, providing a basis for an effective CCS design.

Suggested Citation

  • Fan, He & Su, Zhi-gang & Wang, Pei-hong & Lee, Kwang Y., 2021. "A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006745
    DOI: 10.1016/j.energy.2021.120425
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Hengyi & Tan, Peng & He, Ziqian & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2022. "Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network," Energy, Elsevier, vol. 255(C).
    2. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    3. Liu, Kairui & Wang, Chao & Wang, Limin & Liu, Bin & Ye, Maojing & Guo, Yalong & Che, Defu, 2023. "Dynamic performance analysis and control strategy optimization for supercritical coal-fired boiler: A dynamic simulation," Energy, Elsevier, vol. 282(C).
    4. 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).
    5. 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).
    6. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).

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