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Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela

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  • González-Longatt, Francisco
  • Medina, Humberto
  • Serrano González, Javier

Abstract

This paper presents a wind resource assessment in Venezuela using an efficient combination of spatial interpolation and orographic correction for wind mapping. Mesoscale modelling offers a relatively accurate means to model meteorological conditions by solving the continuity and momentum equations. However, this approach is both time and computationally demanding. The methodology used in this work offers a computationally inexpensive solution by combining both a simple geo-statistical Kriging method to interpolate horizontal wind speed and an orographic correction to account for changes on terrain elevation. Hourly observations of wind speed and direction for 34 masts recorded during the period 2005–2009 have been analysed in order to define a statistical model of wind resources. The resulting method, which includes an exploratory statistical analysis of the wind data, is a computationally economical alternative to mesoscale modelling. Simulations results include equivalent mean wind speeds and wind power maps which have been created to a height of 50, 80 and 120m above the ground based on a horizontal resolution of 15×15km. Results show that the greatest wind energy resources are located in the coastal area of Venezuela with a potential for offshore applications. Preliminary findings provide a very positive evidence for offshore exploitation of wind power. Results also suggest that wind energy resources for commercial use (utility-scale) are available in northern Venezuela, additionally; they suggest excellent conditions for wind power production for micro-scale applications, both on- and off-grid.

Suggested Citation

  • González-Longatt, Francisco & Medina, Humberto & Serrano González, Javier, 2015. "Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 1-16.
    • Handle: RePEc:eee:rensus:v:48:y:2015:i:c:p:1-16
    DOI: 10.1016/j.rser.2015.03.042
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    3. Vidoza, Jorge A. & Gallo, Waldyr L.R., 2016. "Projection of fossil fuels consumption in the Venezuelan electricity generation industry," Energy, Elsevier, vol. 104(C), pages 237-249.
    4. Deockho Kim & Jin Hur, 2017. "Stochastic Prediction of Wind Generating Resources Using the Enhanced Ensemble Model for Jeju Island’s Wind Farms in South Korea," Sustainability, MDPI, vol. 9(5), pages 1-12, May.
    5. Nematollahi, Omid & Kim, Kyung Chun, 2017. "A feasibility study of solar energy in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 566-579.
    6. Gualtieri, Giovanni, 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site," Renewable Energy, Elsevier, vol. 120(C), pages 457-467.
    7. 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.
    8. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.

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