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Compressor washing economic analysis and optimization for power generation

Author

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  • Aretakis, N.
  • Roumeliotis, I.
  • Doumouras, G.
  • Mathioudakis, K.

Abstract

The deregulation of the energy market has created an additional incentive for gas turbine plants operators to minimize and control performance deterioration with respect to the economical aspects of the plant. The most prevalent deterioration problem is compressor fouling, which has a significant impact on the power plant profit. Off-line washing is able to recover the engine’s performance losses due to fouling, but has a variety of associated costs. A method to predict the impact of the compressor washing process on the power plant revenue is presented herein, allowing for the optimization of the process with regards to power plant specific data. For this reason, a detailed cost analysis module is formed and coupled with an engine model allowing for the study of both economic parameters and engine operation parameters like the increase of maintenance cost due to start-ups and the variation of the engine degradation rate. The method is applied for the case of an aeroderivative gas turbine of 42MW. The parameters associated with the off-line washing process and the engine performance that affects the plant’s revenue are examined and discussed, while recommendations on the optimal washing schedule are made.

Suggested Citation

  • Aretakis, N. & Roumeliotis, I. & Doumouras, G. & Mathioudakis, K., 2012. "Compressor washing economic analysis and optimization for power generation," Applied Energy, Elsevier, vol. 95(C), pages 77-86.
    • Handle: RePEc:eee:appene:v:95:y:2012:i:c:p:77-86
    DOI: 10.1016/j.apenergy.2012.02.016
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    References listed on IDEAS

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    1. Li, Y.G. & Nilkitsaranont, P., 2009. "Gas turbine performance prognostic for condition-based maintenance," Applied Energy, Elsevier, vol. 86(10), pages 2152-2161, October.
    2. Roumeliotis, I. & Mathioudakis, K., 2010. "Evaluation of water injection effect on compressor and engine performance and operability," Applied Energy, Elsevier, vol. 87(4), pages 1207-1216, April.
    3. Naeem, M. & Singh, R. & Probert, D., 1998. "Implications of engine deterioration for creep life," Applied Energy, Elsevier, vol. 60(4), pages 183-223, August.
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    Citations

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    Cited by:

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    2. Zagorowska, Marta & Schulze Spüntrup, Frederik & Ditlefsen, Arne-Marius & Imsland, Lars & Lunde, Erling & Thornhill, Nina F., 2020. "Adaptive detection and prediction of performance degradation in off-shore turbomachinery," Applied Energy, Elsevier, vol. 268(C).
    3. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    4. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    5. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    6. Milosavljevic, Predrag & Marchetti, Alejandro G. & Cortinovis, Andrea & Faulwasser, Timm & Mercangöz, Mehmet & Bonvin, Dominique, 2020. "Real-time optimization of load sharing for gas compressors in the presence of uncertainty," Applied Energy, Elsevier, vol. 272(C).
    7. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
    8. Kalathakis, Christos & Aretakis, Nikolaos & Roumeliotis, Ioannis & Alexiou, Alexios & Mathioudakis, Konstantinos, 2019. "Simulation models for supporting the solar thermal power plant operator," Energy, Elsevier, vol. 167(C), pages 1065-1073.

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