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Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis

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  • Tautz-Weinert, Jannis
  • Yürüşen, Nurseda Y.
  • Melero, Julio J.
  • Watson, Simon J.

Abstract

Commercial operation and maintenance of wind farms always involves trying to find the most cost-effective solution from various possible options. In this paper, a maintenance action within a Spanish wind farm was studied, whereby a blade replacement was required to prevent catastrophic failure. The conducted replacement was accompanied by an underperformance resolved in a later blade re-pitching. We analyse the decision taken in terms of the power performance and net present value from the cash flow resulting from the energy sales. The impact of the timing of the maintenance is discussed in various what-if scenarios. The sensitivity to environmental causes of underperformance is compared by varying the duration of blade icing and comparing the performance in different wind directions. Country dynamics and subsidy impacts are hypothetically evaluated for the prevailing electricity market conditions as if the turbine were operating in either Spain, Netherlands or the UK. The findings highlight the uncertainty in power performance and the importance of maintenance accuracy. It is shown that the decision-making of operators should not only consider the seasonality of the wind resource, but also the seasonality in electricity markets.

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  • Tautz-Weinert, Jannis & Yürüşen, Nurseda Y. & Melero, Julio J. & Watson, Simon J., 2019. "Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis," Renewable Energy, Elsevier, vol. 132(C), pages 93-105.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:93-105
    DOI: 10.1016/j.renene.2018.07.110
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    References listed on IDEAS

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    Cited by:

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    2. Bingchuan Sun & Hongmei Cui & Zhongyang Li & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang, 2022. "Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    3. Ravi Pandit & Davide Astolfi & Anh Minh Tang & David Infield, 2022. "Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations," Energies, MDPI, vol. 15(19), pages 1-20, October.
    4. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    5. Giovanni Ottomano Palmisano & Annalisa De Boni & Rocco Roma & Claudio Acciani, 2021. "Influence of Wind Turbines on Farmlands’ Value: Exploring the Behaviour of a Rural Community through the Decision Tree," Sustainability, MDPI, vol. 13(17), pages 1-25, August.
    6. Alberto Pliego Marug'an & Fausto Pedro Garc'ia M'arquez & Jes'us Mar'ia Pinar P'erez, 2024. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Papers 2401.08251, arXiv.org.
    7. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    8. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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