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Characterizing wind power resource reliability in southern Africa

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  • Fant, Charles
  • Gunturu, Bhaskar
  • Schlosser, Adam

Abstract

Producing electricity from wind is attractive because it provides a clean, low-maintenance power supply. However, wind resource is intermittent on various timescales, thus occasionally introducing large and sudden changes in power supply. A better understanding of this variability can greatly benefit power grid planning. In the following study, wind resource is characterized using metrics that highlight these intermittency issues; therefore identifying areas of high and low wind power reliability in southern Africa and Kenya at different time-scales. After developing a wind speed profile, these metrics are applied at various heights in order to assess the added benefit of raising the wind turbine hub. Furthermore, since the interconnection of wind farms can aid in reducing the overall intermittency, the value of interconnecting near-by sites is mapped using two distinct methods. Of the countries in this region, the Republic of South Africa has shown the most interest in wind power investment. For this reason, we focus parts of the study on wind reliability in the country. The study finds that, although mean Wind Power Density is high in South Africa compared to its neighboring countries, wind power resource tends to be less reliable than in other parts of southern Africa—namely central Tanzania. We also find that South Africa’s potential varies over different timescales, with higher reliability in the summer than winter, and higher reliability during the day than at night. This study is concluded by introducing two methods and measures to characterize the value of interconnection, including the use of principal component analysis to identify areas with a common signal.

Suggested Citation

  • Fant, Charles & Gunturu, Bhaskar & Schlosser, Adam, 2016. "Characterizing wind power resource reliability in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 565-573.
  • Handle: RePEc:eee:appene:v:161:y:2016:i:c:p:565-573
    DOI: 10.1016/j.apenergy.2015.08.069
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    1. Ucar, Aynur & Balo, Figen, 2009. "Investigation of wind characteristics and assessment of wind-generation potentiality in Uludag-Bursa, Turkey," Applied Energy, Elsevier, vol. 86(3), pages 333-339, March.
    2. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    3. Pryor, S.C. & Barthelmie, R.J., 2010. "Climate change impacts on wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 430-437, January.
    4. Eskin, N. & Artar, H. & Tolun, S., 2008. "Wind energy potential of Gökçeada Island in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 839-851, April.
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    11. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
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