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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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    2. 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.
    3. 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).
    4. 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).
    5. Díaz, H. & Guedes Soares, C., 2022. "A novel multi-criteria decision-making model to evaluate floating wind farm locations," Renewable Energy, Elsevier, vol. 185(C), pages 431-454.
    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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