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Wind turbine availability: Should it be time or energy based? – A case study in Ireland

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  • Conroy, Niamh
  • Deane, J.P.
  • Ó Gallachóir, Brian P.

Abstract

This paper describes a method for quantifying wind farm availability using two different approaches and comparing the results. Wind turbine suppliers regularly guarantee turbine availability in terms of time. A typical value of 97% is generally taken as the industry standard. This paper shows that this guarantee can potentially under-compensate the wind farm operator for losses sustained depending on when the period of non-availability occurs. Here we present an alternative method to quantify wind farm availability based on energy, which relates the energy losses in an Irish wind farm in 2007 to periods of turbine non-availability. It is shown in this analysis completed at this operational wind farm that while the technical non-availability as a percentage of time is 3%, the percentage of energy lost during downtimes is actually 11%. Based on the financial analysis above, the financial losses are significant. To answer the question should wind turbine availability be time or energy based, this paper shows that it can be advantageous for wind turbine owners to have energy-based calculations as long as the developers have sufficient monitoring of not only wind speed but also SCADA data.

Suggested Citation

  • Conroy, Niamh & Deane, J.P. & Ó Gallachóir, Brian P., 2011. "Wind turbine availability: Should it be time or energy based? – A case study in Ireland," Renewable Energy, Elsevier, vol. 36(11), pages 2967-2971.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:2967-2971
    DOI: 10.1016/j.renene.2011.03.044
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    References listed on IDEAS

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    1. Mabel, M. Carolin & Raj, R. Edwin & Fernandez, E., 2011. "Analysis on reliability aspects of wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1210-1216, February.
    2. Abderrazzaq, M.A. & Hahn, B., 2006. "Analysis of the turbine standstill for a grid connected wind farm (case study)," Renewable Energy, Elsevier, vol. 31(1), pages 89-104.
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    Cited by:

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    2. Chun Su & Longfei Cheng, 2018. "An availability-based warranty policy considering preventive maintenance and learning effects," Journal of Risk and Reliability, , vol. 232(6), pages 576-586, December.
    3. Sliz-Szkliniarz, B. & Eberbach, J. & Hoffmann, B. & Fortin, M., 2019. "Assessing the cost of onshore wind development scenarios: Modelling of spatial and temporal distribution of wind power for the case of Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 514-531.
    4. Rodrigues, R.B. & Mendes, V.M.F. & Catalão, J.P.S., 2012. "Protection of interconnected wind turbines against lightning effects: Overvoltages and electromagnetic transients study," Renewable Energy, Elsevier, vol. 46(C), pages 232-240.
    5. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    6. Graeme S Hawker & David A McMillan, 2015. "The impact of maintenance contract arrangements on the yield of offshore wind power plants," Journal of Risk and Reliability, , vol. 229(5), pages 394-402, October.
    7. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    8. Jung, Christopher & Schindler, Dirk, 2022. "On the influence of wind speed model resolution on the global technical wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    9. Niu, Briana & Hwangbo, Hoon & Zeng, Li & Ding, Yu, 2018. "Evaluation of alternative power production efficiency metrics for offshore wind turbines and farms," Renewable Energy, Elsevier, vol. 128(PA), pages 81-90.
    10. Rubert, T. & McMillan, D. & Niewczas, P., 2018. "A decision support tool to assist with lifetime extension of wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 423-433.
    11. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
    12. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    13. Foley, A.M. & Ó Gallachóir, B.P. & McKeogh, E.J. & Milborrow, D. & Leahy, P.G., 2013. "Addressing the technical and market challenges to high wind power integration in Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 692-703.

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