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Wind Power Error Estimation in Resource Assessments

Author

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  • Osvaldo Rodríguez
  • Jesús A del Río
  • Oscar A Jaramillo
  • Manuel Martínez

Abstract

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies.

Suggested Citation

  • Osvaldo Rodríguez & Jesús A del Río & Oscar A Jaramillo & Manuel Martínez, 2015. "Wind Power Error Estimation in Resource Assessments," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0124830
    DOI: 10.1371/journal.pone.0124830
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    References listed on IDEAS

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    1. Oliver Probst & Diego Cárdenas, 2010. "State of the Art and Trends in Wind Resource Assessment," Energies, MDPI, vol. 3(6), pages 1-55, June.
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    1. Mary, Asare-Addo, 2021. "Geospatial mapping of micro-wind energy for district electrification in Ghana," Energy, Elsevier, vol. 225(C).
    2. Osvaldo Rodriguez-Hernandez & Manuel Martinez & Carlos Lopez-Villalobos & Hector Garcia & Rafael Campos-Amezcua, 2019. "Techno-Economic Feasibility Study of Small Wind Turbines in the Valley of Mexico Metropolitan Area," Energies, MDPI, vol. 12(5), pages 1-26, March.
    3. Erik Möllerström & Sean Gregory & Aromal Sugathan, 2021. "Improvement of AEP Predictions with Time for Swedish Wind Farms," Energies, MDPI, vol. 14(12), pages 1-12, June.
    4. Monowar Hossain & Saad Mekhilef & Firdaus Afifi & Laith M Halabi & Lanre Olatomiwa & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2018. "Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-31, April.
    5. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.

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