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The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

Author

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  • Markus Gross

    (CICESE, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, CP. 22860 Ensenada, B.C., Mexico
    These authors contributed equally to this work.)

  • Vanesa Magar

    (CICESE, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, CP. 22860 Ensenada, B.C., Mexico
    These authors contributed equally to this work.)

  • Alfredo Peña

    (Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
    These authors contributed equally to this work.)

Abstract

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.

Suggested Citation

  • Markus Gross & Vanesa Magar & Alfredo Peña, 2020. "The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3431-:d:349082
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    References listed on IDEAS

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    1. Pimenta, Felipe & Kempton, Willett & Garvine, Richard, 2008. "Combining meteorological stations and satellite data to evaluate the offshore wind power resource of Southeastern Brazil," Renewable Energy, Elsevier, vol. 33(11), pages 2375-2387.
    2. Muhammad Shoaib & Imran Siddiqui & Shafiqur Rehman & Saif Ur Rehman & Shamim Khan & Aref Lashin, 2016. "Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function," Energies, MDPI, vol. 9(10), pages 1-18, October.
    3. Wang, Yi-Hui & Walter, Ryan K. & White, Crow & Farr, Hayley & Ruttenberg, Benjamin I., 2019. "Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast," Renewable Energy, Elsevier, vol. 133(C), pages 343-353.
    4. Dvorak, Michael J. & Archer, Cristina L. & Jacobson, Mark Z., 2010. "California offshore wind energy potential," Renewable Energy, Elsevier, vol. 35(6), pages 1244-1254.
    5. Jin-Young Kim & Hyun-Goo Kim & Yong-Heack Kang, 2017. "Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula," Energies, MDPI, vol. 10(7), pages 1-15, July.
    6. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    7. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    8. Wang, Meina & Ullrich, Paul & Millstein, Dev, 2018. "The future of wind energy in California: Future projections with the Variable-Resolution CESM," Renewable Energy, Elsevier, vol. 127(C), pages 242-257.
    9. Li, Delei & Geyer, Beate & Bisling, Peter, 2016. "A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea," Applied Energy, Elsevier, vol. 179(C), pages 575-589.
    10. Gadad, Sanjeev & Deka, Paresh Chandra, 2016. "Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale," Applied Energy, Elsevier, vol. 176(C), pages 157-170.
    11. Fant, Charles & Gunturu, Bhaskar & Schlosser, Adam, 2016. "Characterizing wind power resource reliability in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 565-573.
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    Cited by:

    1. Collados-Lara, Antonio-Juan & Baena-Ruiz, Leticia & Pulido-Velazquez, David & Pardo-Igúzquiza, Eulogio, 2022. "Data-driven mapping of hourly wind speed and its potential energy resources: A sensitivity analysis," Renewable Energy, Elsevier, vol. 199(C), pages 87-102.
    2. Vanesa Magar & Alfredo Peña & Andrea Noemí Hahmann & Daniel Alejandro Pacheco-Rojas & Luis Salvador García-Hernández & Markus Sebastian Gross, 2023. "Wind Energy and the Energy Transition: Challenges and Opportunities for Mexico," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    3. Peña, Alfredo & Mirocha, Jeffrey D., 2024. "One-year-long turbulence measurements and modeling using large-eddy simulation domains in the Weather Research and Forecasting model," Applied Energy, Elsevier, vol. 363(C).

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