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Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit

Author

Listed:
  • Qingfeng Yang

    (Dongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, China)

  • Gang Chen

    (State Key Laboratory of Low-Carbon Smart Coal-Fired Power Generation and Ultra-Clean Emission, Nanjing 210023, China
    Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210000, China)

  • Mengmeng Guo

    (National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China)

  • Tingting Chen

    (Dongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, China)

  • Lei Luo

    (Dongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, China)

  • Li Sun

    (National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China)

Abstract

In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspace identification method. The model’s high precision is confirmed by its alignment with field data. Load change simulations demonstrate that the PID–MPC hybrid controller shows faster response times and more precise tracking capabilities compared to the feedforward-PID strategy. It achieves substantial improvements in the IAE index for three loops, with increases of 29.2%, 54.1%, and 57.3% over the feedforward-PID controller. An energy-saving performance analysis indicates that the proactive control actions of both the PID–MPC and MPC strategies lead to dynamic exergy efficiency and coal consumption rates with a broader range of dynamic process changes. The disturbance scenario simulation regarding the proposed controller achieves faster settling times and minimizes control deviation compared to the traditional controller.

Suggested Citation

  • Qingfeng Yang & Gang Chen & Mengmeng Guo & Tingting Chen & Lei Luo & Li Sun, 2024. "Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit," Energies, MDPI, vol. 17(24), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6356-:d:1545985
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    References listed on IDEAS

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    1. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    2. C. Sanama & X. Xia & M. Nguepnang & Sheng Du, 2022. "PID-MPC Implementation on a Chiller-Fan Coil Unit," Journal of Mathematics, Hindawi, vol. 2022, pages 1-13, September.
    3. Çetin, Gürcan & Özkaraca, Osman & Keçebaş, Ali, 2021. "Development of PID based control strategy in maximum exergy efficiency of a geothermal power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    4. Nikki Forrester, 2024. "Catching the rays: my part in Morocco’s renewable-energy revolution," Nature, Nature, vol. 626(8001), pages 1156-1156, February.
    5. Wu, Xiao & Wang, Meihong & Shen, Jiong & Li, Yiguo & Lawal, Adekola & Lee, Kwang Y., 2019. "Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls," Applied Energy, Elsevier, vol. 238(C), pages 495-515.
    6. Stanger, Lukas & Bartik, Alexander & Hammerschmid, Martin & Jankovic, Stefan & Benedikt, Florian & Müller, Stefan & Schirrer, Alexander & Jakubek, Stefan & Kozek, Martin, 2024. "Model predictive control of a dual fluidized bed gasification plant," Applied Energy, Elsevier, vol. 361(C).
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