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An online parameter identification and real-time optimization platform for thermal systems and its application

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  • Chen, Xi
  • Zhao, Tian
  • Chen, Qun

Abstract

Real-time performance optimization of thermal systems is crucial for energy conservation but also challenging because of the high system complexity and time sensitivity. Herein, an online parameter identification and real-time optimization platform for thermal systems is developed, and a gas-steam combined cycle cogeneration system is used to demonstrate its capability. The platform integrates data collection, parameter identification, system simulation, and real-time optimization modules. The data preprocessing and parameter identification modules collects real-time operation parameters for optimization. The system simulation module applies the heat current method to model the cogeneration system precisely, and a high-efficiency simulation procedure is proposed using the hierarchical and categorized (H&C) algorithm. The system optimization module introduces the artificial neural network technology to ensure the response time of real-time optimization. Meanwhile, the H&C algorithm and genetic algorithm are combined to update the database to improve the optimization performance gradually. The platform is first validated on three typical conditions. It is further deployed at a power plant, where a field test is conducted for the practical verification. Field test results show that the standard coal consumption of the cogeneration system could be reduced by 0.415 g/kWh by using the platform, which proves its practicability.

Suggested Citation

  • Chen, Xi & Zhao, Tian & Chen, Qun, 2022. "An online parameter identification and real-time optimization platform for thermal systems and its application," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014677
    DOI: 10.1016/j.apenergy.2021.118199
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    Cited by:

    1. He, Ke-Lun & Zhao, Tian & Ma, Huan & Chen, Qun, 2023. "Optimal operation of integrated power and thermal systems for flexibility improvement based on evaluation and utilization of heat storage in district heating systems," Energy, Elsevier, vol. 274(C).
    2. Lin, Xiaojie & Mao, Yihui & Chen, Jiaying & Zhong, Wei, 2023. "Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access," Applied Energy, Elsevier, vol. 349(C).
    3. Xin, Yong-Lin & Sun, Qing-Han & Zhao, Tian & Li, Xia & Chen, Qun, 2023. "A categorized and decomposed algorithm for thermal system simulation based on generalized benders decomposition," Energy, Elsevier, vol. 282(C).

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