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Modeling of Large-Scale Thermal Power Plants for Performance Prediction in Deep Peak Shaving

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  • Sha Liu

    (School of Mechanical and Electrical Engineering, Jingling Institute of Technology, Nanjing 211169, China
    Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China)

  • Jiong Shen

    (Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China)

Abstract

To integrate more renewable energy into the power grid, large-scale thermal power plants have to extend their operating ranges and participating in deep peak shaving. In order to improve the thermal economy of large-scale thermal power plants participating in deep peak shaving, and to determine the performance of a thermal system under different conditions, a method of modeling for the performance prediction of large-scale thermal power plants in deep peak shaving is proposed. In the algorithm design of the model, a three-layer iterative cycle logic is constructed, and the coupling relationship between the parameters of the thermal system is analyzed from the mechanism level. All of the steam water parameters and the correction values of the flow rate at all levels of the system after the parameter disturbance are obtained. The algorithm can simulate the response of a thermal power plant under load variation and operation parameter variation. Compare the error between the data given by the prediction model and the test, the accuracy of the proposed prediction model is verified. When the unit participates in deep peak shaving, the prediction model is used to analyze the relative deviation of the unit thermal efficiency caused by cycle parameters and energy efficiency of equipment. It provides a date basis for the performance evaluation and multi-parameter coupling optimization. The research results can be used to determine the operation mode and equipment transformation scheme.

Suggested Citation

  • Sha Liu & Jiong Shen, 2022. "Modeling of Large-Scale Thermal Power Plants for Performance Prediction in Deep Peak Shaving," Energies, MDPI, vol. 15(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3171-:d:802943
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    References listed on IDEAS

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