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Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit

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  • Hou, Guolian
  • Gong, Linjuan
  • Hu, Bo
  • Su, Huilin
  • Huang, Ting
  • Huang, Congzhi
  • Fan, Wei
  • Zhao, Yuanzhu

Abstract

As main measure to stabilize fluctuation of power grid under renewable energy utilization, the flexible operation of thermal power unit reveals remarkable significant. To serve for peak shaving and fast load varying, a novel T-S fuzzy modeling approach based on fast adaptive moth-flame optimization (FAMFO) algorithm is proposed for dynamics investigation of supercritical unit. This modeling scheme is deemed as hierarchical process with data partition stage and parameter determination stage. Firstly, an automatic clustering strategy relying on FAMFO is fulfilled for more reasonable data partition result, which devotes to accuracy modeling without subjective interference. In this stage, the FAMFO is attained with assistance of four improvements and its merits in balancing search exploration and exploitation guarantee the width of covered operation range of supercritical unit for peak shaving. Secondly, inherent advantages of least square technics in parameter identification witness the successful implementation of a well-performed exponentially-weighted least squares algorithm in later stage of fuzzy modeling. In effectiveness verification of the proposed modeling approach, the superiorities of FAMFO in promoting search precision and speed are demonstrated via benchmark function test and Friedman test. Then, the flexible operation modeling performance is proved via extensive simulation experiments using on-site data of supercritical unit.

Suggested Citation

  • Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221020910
    DOI: 10.1016/j.energy.2021.121843
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    Cited by:

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    3. Hou, Guolian & Gong, Linjuan & Hu, Bo & Huang, Ting & Su, Huilin & Huang, Congzhi & Zhou, Guiping & Wang, Shunjiang, 2022. "Flexibility oriented adaptive modeling of combined heat and power plant under various heat-power coupling conditions," Energy, Elsevier, vol. 242(C).
    4. 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).
    5. Zima, Wiesław & Grądziel, Sławomir & Cebula, Artur & Rerak, Monika & Kozak-Jagieła, Ewa & Pilarczyk, Marcin, 2023. "Mathematical model of a power boiler operation under rapid thermal load changes," Energy, Elsevier, vol. 263(PC).
    6. Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    7. 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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