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Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant

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  • Lee, Jae Hong
  • Kim, Tong Seop
  • Kim, Eui-hwan

Abstract

A precise prediction of the power generation capacity of each power plant in an electric grid is important for managing the grid stably by preventing mismatch between the electric demand and power production. This prediction also helps power plant owners maintain their facilities because it allows effective performance monitoring. This paper proposes a unique analysis tool for predicting the near-term power generation capacity of cogeneration combined cycle power plants using correction curves and real operating data. The plant operation was simulated using the well-simulated design performance and an off-design calculation model consisting of performance correction curves and thermodynamic modeling. Real operation data of several seven-day periods were simulated using the developed tool. The data of the first five days was used as a tool set up leading to a calculation of the power correction factor. The obtained power correction factor reflected the performance degradation quite well. The data of the last two days was used for tool validation. The discrepancy between the predicted and measured power outputs were less than 2%. The proposed method can be used for both performance monitoring and a prediction of the power generation capacity of gas turbine-based power plants.

Suggested Citation

  • Lee, Jae Hong & Kim, Tong Seop & Kim, Eui-hwan, 2017. "Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant," Energy, Elsevier, vol. 124(C), pages 187-197.
  • Handle: RePEc:eee:energy:v:124:y:2017:i:c:p:187-197
    DOI: 10.1016/j.energy.2017.02.032
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    Cited by:

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    5. Yang, Yongping & Bai, Ziwei & Zhang, Guoqiang & Li, Yongyi & Wang, Ziyu & Yu, Guangying, 2019. "Design/off-design performance simulation and discussion for the gas turbine combined cycle with inlet air heating," Energy, Elsevier, vol. 178(C), pages 386-399.
    6. Hossein Moayedi & Amir Mosavi, 2021. "Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
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    8. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    9. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.

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