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Methodologies for predicting the part-load performance of aero-derivative gas turbines

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  • Haglind, F.
  • Elmegaard, B.

Abstract

Prediction of the part-load performance of gas turbines is advantageous in various applications. Sometimes reasonable part-load performance is sufficient, while in other cases complete agreement with the performance of an existing machine is desirable. This paper is aimed at providing some guidance on methodologies for predicting part-load performance of aero-derivative gas turbines. Two different design models – one simple and one more complex – are created. Subsequently, for each of these models, the part-load performance is predicted using component maps and turbine constants, respectively. Comparisons with manufacturer data are made. With respect to the design models, the simple model, featuring a compressor, combustor and turbines, results in equally good performance prediction in terms of thermal efficiency and exhaust temperature as does a more complex model. As for part-load predictions, the results suggest that the mass flow and pressure ratio characteristics can be well predicted with both methods. The thermal efficiency and exhaust temperature, however, are not well predicted below 60–70% load when using turbine constants and assuming constant efficiencies for turbomachinery.

Suggested Citation

  • Haglind, F. & Elmegaard, B., 2009. "Methodologies for predicting the part-load performance of aero-derivative gas turbines," Energy, Elsevier, vol. 34(10), pages 1484-1492.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1484-1492
    DOI: 10.1016/j.energy.2009.06.042
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    Cited by:

    1. Desai, Nishith B. & Pranov, Henrik & Haglind, Fredrik, 2021. "Techno-economic analysis of a foil-based solar collector driven electricity and fresh water generation system," Renewable Energy, Elsevier, vol. 165(P1), pages 642-656.
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    3. Ouyang, Tiancheng & Qin, Peijia & Xie, Shutao & Tan, Xianlin & Pan, Mingming, 2023. "Flexible dispatch strategy of purchasing-selling electricity for coal-fired power plant based on compressed air energy storage," Energy, Elsevier, vol. 267(C).
    4. Gürgen, Samet & Altın, İsmail, 2022. "Novel decision-making strategy for working fluid selection in Organic Rankine Cycle: A case study for waste heat recovery of a marine diesel engine," Energy, Elsevier, vol. 252(C).
    5. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    6. Pierobon, L. & Benato, A. & Scolari, E. & Haglind, F. & Stoppato, A., 2014. "Waste heat recovery technologies for offshore platforms," Applied Energy, Elsevier, vol. 136(C), pages 228-241.
    7. Song, Yin & Gu, Chun-wei & Ji, Xing-xing, 2015. "Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling," Energy, Elsevier, vol. 89(C), pages 545-557.
    8. Zhang, Yi & Xu, Yujie & Zhou, Xuezhi & Guo, Huan & Zhang, Xinjing & Chen, Haisheng, 2019. "Compressed air energy storage system with variable configuration for accommodating large-amplitude wind power fluctuation," Applied Energy, Elsevier, vol. 239(C), pages 957-968.
    9. Liya Ren & Huaixin Wang, 2020. "Optimization and Comparison of Two Combined Cycles Consisting of CO 2 and Organic Trans-Critical Cycle for Waste Heat Recovery," Energies, MDPI, vol. 13(3), pages 1-16, February.
    10. Zhang, Yi & Xu, Yujie & Guo, Huan & Zhang, Xinjing & Guo, Cong & Chen, Haisheng, 2018. "A hybrid energy storage system with optimized operating strategy for mitigating wind power fluctuations," Renewable Energy, Elsevier, vol. 125(C), pages 121-132.
    11. Zhang, Xinjing & Chen, Haisheng & Xu, Yujie & Li, Wen & He, Fengjuan & Guo, Huan & Huang, Ye, 2017. "Distributed generation with energy storage systems: A case study," Applied Energy, Elsevier, vol. 204(C), pages 1251-1263.
    12. Mehrpanahi, Abdollah & Payganeh, Gholamhasan & Arbabtafti, Mohammadreza, 2017. "Dynamic modeling of an industrial gas turbine in loading and unloading conditions using a gray box method," Energy, Elsevier, vol. 120(C), pages 1012-1024.
    13. Jiménez-Espadafor Aguilar, Francisco & Quintero, R. Rodríguez & Trujillo, E. Carvajal & García, Miguel Torres, 2014. "Analysis of regulation methods of a combined heat and power plant based on gas turbines," Energy, Elsevier, vol. 72(C), pages 574-589.
    14. Benato, Alberto & Stoppato, Anna & Mirandola, Alberto, 2015. "Dynamic behaviour analysis of a three pressure level heat recovery steam generator during transient operation," Energy, Elsevier, vol. 90(P2), pages 1595-1605.
    15. Desai, Nishith B. & Mondejar, Maria E. & Haglind, Fredrik, 2022. "Techno-economic analysis of two-tank and packed-bed rock thermal energy storages for foil-based concentrating solar collector driven cogeneration plants," Renewable Energy, Elsevier, vol. 186(C), pages 814-830.
    16. Pierobon, Leonardo & Casati, Emiliano & Casella, Francesco & Haglind, Fredrik & Colonna, Piero, 2014. "Design methodology for flexible energy conversion systems accounting for dynamic performance," Energy, Elsevier, vol. 68(C), pages 667-679.
    17. Orlandini, Valentina & Pierobon, Leonardo & Schløer, Signe & De Pascale, Andrea & Haglind, Fredrik, 2016. "Dynamic performance of a novel offshore power system integrated with a wind farm," Energy, Elsevier, vol. 109(C), pages 236-247.
    18. Leonardo Pierobon & Tuong-Van Nguyen & Andrea Mazzucco & Ulrik Larsen & Fredrik Haglind, 2014. "Part-Load Performance of aWet Indirectly Fired Gas Turbine Integrated with an Organic Rankine Cycle Turbogenerator," Energies, MDPI, vol. 7(12), pages 1-23, December.
    19. Jiang, Yuemao & Ma, Yue & Han, Fenghui & Ji, Yulong & Cai, Wenjian & Wang, Zhe, 2023. "Assessment and optimization of a novel waste heat stepped utilization system integrating partial heating sCO2 cycle and ejector refrigeration cycle using zeotropic mixtures for gas turbine," Energy, Elsevier, vol. 265(C).
    20. Haglind, F., 2010. "Variable geometry gas turbines for improving the part-load performance of marine combined cycles – Gas turbine performance," Energy, Elsevier, vol. 35(2), pages 562-570.
    21. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    22. Jesper Graa Andreasen & Andrea Meroni & Fredrik Haglind, 2017. "A Comparison of Organic and Steam Rankine Cycle Power Systems for Waste Heat Recovery on Large Ships," Energies, MDPI, vol. 10(4), pages 1-23, April.
    23. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    24. Benato, A. & Kærn, M.R. & Pierobon, L. & Stoppato, A. & Haglind, F., 2015. "Analysis of hot spots in boilers of organic Rankine cycle units during transient operation," Applied Energy, Elsevier, vol. 151(C), pages 119-131.

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