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Robust model predictive control of condensate throttling in an ultra-supercritical plant for frequency supports of power systems with high renewable share

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  • Li, Xiaoming
  • Wu, Yingjie
  • Yu, Xinghuo

Abstract

To enhance the load response capability of coal-fired power plants in transition to 100% renewable, a neural network (NN) based robust model predictive control (MPC) with accurate demand tracking of condensate throttling is developed. The immeasurable megawatt (MW) from throttling condensate which is filtered out from the total output power, is used to train the NN. A neural state observer (NSO) is then built to predict the immeasurable MW, with a robust stabilization technique applied to address the uncertainties introduced by the filtering process. Based on the NSO, a state-variable MPC is developed for condensate throttling, enabling robust load demand tracking while improving dynamic performance. The control design can be formulated as a nonlinear model-based H2/H∞ suboptimization problem. A solver utilizing linear matrix inequality (LMI) technology, is designed to solve this nonlinear complex problem without the need for linearization. To maximize the utilization of condensate throttling and minimize the required capacity of a battery energy storage system (BESS), a multi-scale load dispatching filter is developed under the virtual power plant (VPP) concept. The effectiveness of the approach is demonstrated through real-time simulations on the StarSim Hardware-in-the-Loop (HIL) Simulator.

Suggested Citation

  • Li, Xiaoming & Wu, Yingjie & Yu, Xinghuo, 2025. "Robust model predictive control of condensate throttling in an ultra-supercritical plant for frequency supports of power systems with high renewable share," Energy, Elsevier, vol. 332(C).
    • Handle: RePEc:eee:energy:v:332:y:2025:i:c:s0360544225026313
    DOI: 10.1016/j.energy.2025.136989
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    References listed on IDEAS

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    1. Long, Dongteng & Wang, Wei & Yao, Chu & Liu, Jizhen, 2017. "An experiment-based model of condensate throttling and its utilization in load control of 1000 MW power units," Energy, Elsevier, vol. 133(C), pages 941-954.
    2. Yin, Guangzhi & Duan, Maosheng, 2022. "Pricing the deep peak regulation service of coal-fired power plants to promote renewable energy integration," Applied Energy, Elsevier, vol. 321(C).
    3. Zhang, Kezhen & Zhao, Yongliang & Liu, Ming & Gao, Lin & Fu, Yue & Yan, Junjie, 2021. "Flexibility enhancement versus thermal efficiency of coal-fired power units during the condensate throttling processes," Energy, Elsevier, vol. 218(C).
    4. Han, Zhonghe & Xiang, Peng, 2020. "Modeling condensate throttling to improve the load change performance of cogeneration units," Energy, Elsevier, vol. 192(C).
    5. Cui, Qi & He, Ling & Han, Guoyi & Chen, Hao & Cao, Juanjuan, 2020. "Review on climate and water resource implications of reducing renewable power curtailment in China: A nexus perspective," Applied Energy, Elsevier, vol. 267(C).
    6. Nguyen, Thu Ha & Nguyen, Van Chung & Bui, Dang Quang & Dao, Phuong Nam, 2024. "An efficient Min/Max Robust Model Predictive Control for nonlinear discrete-time systems with dynamic disturbance," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    7. Wu, Qingyang & Li, Gen & Liu, Ming & Zhang, Yufeng & Yan, Junjie & Deguchi, Yoshihiro, 2024. "The enhancement of primary frequency regulation ability of combined water and power plant based on nuclear energy: Dynamic modelling and control strategy optimization," Energy, Elsevier, vol. 313(C).
    8. Li, Xuehan & Wang, Wei & Ye, Lingling & Ren, Guorui & Fang, Fang & Liu, Jizhen & Chen, Zhe & Zhou, Qiang, 2024. "Improving frequency regulation ability for a wind-thermal power system by multi-objective optimized sliding mode control design," Energy, Elsevier, vol. 300(C).
    9. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    10. Liu, Ji-Zhen & Yan, Shu & Zeng, De-Liang & Hu, Yong & Lv, You, 2015. "A dynamic model used for controller design of a coal fired once-through boiler-turbine unit," Energy, Elsevier, vol. 93(P2), pages 2069-2078.
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