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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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    as
    1. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa, 2016. "Impacts of intermittent renewable generation on electricity system costs," Energy Policy, Elsevier, vol. 94(C), pages 411-420.
    2. Wang, Zhu & Liu, Ming & Zhao, Yongliang & Wang, Chaoyang & Chong, Daotong & Yan, Junjie, 2020. "Flexibility and efficiency enhancement for double-reheat coal-fired power plants by control optimization considering boiler heat storage," Energy, Elsevier, vol. 201(C).
    3. Guzel Mingaleeva & Olga Afanaseva & Duc Toan Nguen & Dang Nayt Pham & Pietro Zunino, 2020. "The Integration of Hybrid Mini Thermal Power Plants into the Energy Complex of the Republic of Vietnam," Energies, MDPI, vol. 13(21), pages 1-17, November.
    4. Eser, Patrick & Singh, Antriksh & Chokani, Ndaona & Abhari, Reza S., 2016. "Effect of increased renewables generation on operation of thermal power plants," Applied Energy, Elsevier, vol. 164(C), pages 723-732.
    5. Rodríguez, Rolando A. & Becker, Sarah & Andresen, Gorm B. & Heide, Dominik & Greiner, Martin, 2014. "Transmission needs across a fully renewable European power system," Renewable Energy, Elsevier, vol. 63(C), pages 467-476.
    6. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    7. Kubik, M.L. & Coker, P.J. & Barlow, J.F., 2015. "Increasing thermal plant flexibility in a high renewables power system," Applied Energy, Elsevier, vol. 154(C), pages 102-111.
    8. Zhao, Zhigang & Su, Sheng & Si, Ningning & Hu, Song & Wang, Yi & Xu, Jun & Jiang, Long & Chen, Gang & Xiang, Jun, 2017. "Exergy analysis of the turbine system in a 1000 MW double reheat ultra-supercritical power plant," Energy, Elsevier, vol. 119(C), pages 540-548.
    9. Wu, Xiao & Xi, Han & Ren, Yuning & Lee, Kwang Y., 2021. "Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture," Energy, Elsevier, vol. 226(C).
    10. Wei, Huimin & Wu, Tao & Ge, Zhihua & Yang, Lijun & Du, Xiaoze, 2019. "Entransy analysis optimization of cooling water flow distribution in a dry cooling tower of power plant under summer crosswinds," Energy, Elsevier, vol. 166(C), pages 1229-1240.
    11. Ye, Liang-Cheng & Lin, Hai Xiang & Tukker, Arnold, 2019. "Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China," Energy, Elsevier, vol. 167(C), pages 708-714.
    12. Frew, Bethany A. & Becker, Sarah & Dvorak, Michael J. & Andresen, Gorm B. & Jacobson, Mark Z., 2016. "Flexibility mechanisms and pathways to a highly renewable US electricity future," Energy, Elsevier, vol. 101(C), pages 65-78.
    13. Wu, Xiao & Wang, Meihong & Liao, Peizhi & Shen, Jiong & Li, Yiguo, 2020. "Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation," Applied Energy, Elsevier, vol. 257(C).
    14. Wang, Weiliang & Zhang, Hai & Li, Zheng & Lv, Junfu & Ni, Weidou & Li, Yongsheng, 2016. "Adoption of enclosure and windbreaks to prevent the degradation of the cooling performance for a natural draft dry cooling tower under crosswind conditions," Energy, Elsevier, vol. 116(P2), pages 1360-1369.
    15. Wang, Weiliang & Zhang, Hai & Liu, Pei & Li, Zheng & Lv, Junfu & Ni, Weidou, 2017. "The cooling performance of a natural draft dry cooling tower under crosswind and an enclosure approach to cooling efficiency enhancement," Applied Energy, Elsevier, vol. 186(P3), pages 336-346.
    16. Kopiske, Jakob & Spieker, Sebastian & Tsatsaronis, George, 2017. "Value of power plant flexibility in power systems with high shares of variable renewables: A scenario outlook for Germany 2035," Energy, Elsevier, vol. 137(C), pages 823-833.
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