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Spatial distribution of offshore wind statistics on the coast of Portugal using Regional Frequency Analysis

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  • Campos, R.M.
  • Guedes Soares, C.

Abstract

This paper investigates the spatial characteristics of wind speed statistics in oceanic areas of Portugal. The main goal is to apply a regionalization method to define statistically homogeneous regions and then analyze the probabilistic moments, the wind power density, percentiles and extreme values within each region. The Regional Frequency Analysis based on L-moments was implemented using five years of atmospheric simulations. The domain convers latitudes 36.0ºN to 42.2ºN and longitudes 11.0ºW to 7.2ºW, with resolution of 0.081° X 0.097° and 6 h. The investigation using the L-moment ratios resulted in four regions: north (1), center (2), southwest (3) and south (4) of Portugal. A discussion about the potential associated with each region, in terms of the wind energy available and the extreme events severity, enriches the final analyses. Region 1 has the benefit of the highest mean wind power density, equal to 909 W/m2, but it presented the most extreme winds with return value of 25.4 m/s for the return period of 20 years. It was observed that extreme winds are reduced moving south, together with the wind power densities, which are analyzed in detail. The spatial and regional characteristics of wind power densities and extreme values are deeply explored.

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  • Campos, R.M. & Guedes Soares, C., 2018. "Spatial distribution of offshore wind statistics on the coast of Portugal using Regional Frequency Analysis," Renewable Energy, Elsevier, vol. 123(C), pages 806-816.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:806-816
    DOI: 10.1016/j.renene.2018.02.051
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    1. Nawri, Nikolai & Petersen, Guðrún Nína & Bjornsson, Halldór & Hahmann, Andrea N. & Jónasson, Kristján & Hasager, Charlotte Bay & Clausen, Niels-Erik, 2014. "The wind energy potential of Iceland," Renewable Energy, Elsevier, vol. 69(C), pages 290-299.
    2. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    3. Wang, Yingguang & Xia, Yiqing & Liu, Xiaojun, 2013. "Establishing robust short-term distributions of load extremes of offshore wind turbines," Renewable Energy, Elsevier, vol. 57(C), pages 606-619.
    4. Gualtieri, Giovanni & Secci, Sauro, 2014. "Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy," Renewable Energy, Elsevier, vol. 62(C), pages 164-176.
    5. Bento, Nuno & Fontes, Margarida, 2015. "The construction of a new technological innovation system in a follower country: Wind energy in Portugal," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 197-210.
    6. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    7. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    8. Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    9. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Alam, Md. Mahbub, 2017. "Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters," Renewable Energy, Elsevier, vol. 102(PA), pages 35-49.
    10. 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.
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    Cited by:

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    2. Carreno-Madinabeitia, Sheila & Ibarra-Berastegi, Gabriel & Sáenz, Jon & Ulazia, Alain, 2021. "Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)," Energy, Elsevier, vol. 226(C).
    3. Díaz, H. & Silva, D. & Bernardo, C. & Guedes Soares, C., 2023. "Micro sitting of floating wind turbines in a wind farm using a multi-criteria framework," Renewable Energy, Elsevier, vol. 204(C), pages 449-474.
    4. Salvação, Nadia & Bentamy, Abderrahim & Guedes Soares, C., 2022. "Developing a new wind dataset by blending satellite data and WRF model wind predictions," Renewable Energy, Elsevier, vol. 198(C), pages 283-295.
    5. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Monthly wind distribution prediction based on nonparametric estimation and modified differential evolution optimization algorithm," Renewable Energy, Elsevier, vol. 217(C).
    6. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).

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