International Journal of Energy and Power Engineering

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An Assessment of Wind Power Generation Potential for Margate Town in South Africa

Received: 29 January 2015    Accepted: 11 February 2015    Published: 26 February 2015
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Abstract

Before setting up a wind farm at any given site, it is very important to evaluate its wind power potential to find its physical and economic sustainability. To date, there is limited research output on wind resource assessment for Margate town in South Africa and this might be one of the factors affecting the uptake of wind technologies in this area. This study aimed to assess the wind power potential of Margate. Three-year-long, hourly average wind speed series between January 2010 and December 2012 for Margate town were statistically analysed using the Weibull distribution function. The dimensionless Weibull shape parameter (k) varied from 2.1 to 2.2 while the scale parameter (c) ranged between 4.1 and 4.3 ms-1. The most probable wind speed (vmp) ranged from 3.0 to 3.2 ms-1. The wind power densities fluctuated from 57.8 to 64.0 Wm-2.The average of the measured wind speeds (vm) for the whole period was less than 5 ms-1.Basing on the wind classification done by European Wind Energy Association (EWEA), Margate town is not favourable for the installation of wind turbines. However according to the rule of thumb for mean yearly wind speeds set by American Wind Energy Association (AWEA), the town is suitable for installation of stand-alone systems. It is thus recommended to small scale wind turbines for stand-alone applications such as supplying power to individual houses and irrigation in this town.

DOI 10.11648/j.ijepe.20150402.13
Published in International Journal of Energy and Power Engineering (Volume 4, Issue 2, April 2015)
Page(s) 32-37
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wind Speed, Weibull Distribution, Weibull Parameter, Wind Power Density

References
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[2] Energy. In: Van Niekerk L. South Africa Yearbook 2012/2013. 20th Ed. Pretoria: Government Communication and Information System (GCIS); 2013.
[3] Department of Energy. 2014. Basic electricity overview. Accessed 20 July 2014. Available: http://www.energy.gov.za/files/electricity_frame.html.
[4] Ayodele TR, Jimoh AA, Munda JL, Agee JT. Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters. JESA 2012;23:30.
[5] Oxfam International. Climate change, development and energy problems in South Africa: Another world is possible. Monograph on the Internet: Earthlife Africa; 2009. Accessed 20 July 2014. Available: http://www.oxfam.org/sites/www.oxfam.org/files/oi_climate_change_south_africa.pdf
[6] Marques AC, Fuinhas JA. Drivers promoting renewable energy: A dynamic panel approach. Renew Sust Energ Rev 2011;15: 1601-1608.
[7] Department of Minerals and Energy. Integrated resource plan for electricity 2010-2030. Final report; 2011, P. 73.
[8] Kolver L. Coega wind turbine manufacturing facility 60% complete. Engineering news; 2013. Accessed 24 July 2014. Available: http://www.engineeringnews.co.za/article/coega-wind-turbine-manufacturing-facility-60-complete-2013-11-11
[9] Nor KM, Shaaban M, Rahman HA. Feasibility assessment of wind energy resources in malaysia based on NWP models. Renew Energ 2014;62:147.
[10] Adaramola MS, Agelin-Chaab M, Paul SS. Assessment of wind power generation along the coast of Ghana. Energ Convers Manage 2014; 77 61-69.
[11] Tong W. Fundamentals of Wind Energy. In: Tong W, EDITOR. Wind power generation and wind turbine design. Southampton: wit press; 2010.
[12] American Wind Energy Association. Basic principles of wind resource evaluation. 1998. Accessed 01 July 2014. Available: http://archive.today/tATuf#selection-333.0-198.2
[13] Chang TP. Estimation of wind energy potential using different probability density functions. Appl Energ 2011;88: 1848-56.
[14] Mostafaeipour A, Sedaghat A, Dehghan-Niri AA, Kalantar V. Wind energy feasibility study for city of Shahrbabak in Iran. Renew Sust Energ Rev 2011;15: 2545- 56.
[15] Akpinar EK, Akpinar S. A statistical analysis of wind speed data used in installation of wind energy conversion systems. Energ Convers Manage 2005;46: 515-532.
[16] Nigim KA, Parker P. Heuristic and probabilistic wind power availability estimation procedures: improved tools for technology and site selection. Renew Energ 2007;32: 638-648.
[17] Shikha S, Bhatti TS, Kothari DP. Air concentrating nozzles: a promising option for wind turbines. INT J Energy Technology and Policy 2005;3: 394-412.
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  • APA Style

    Chipo Shonhiwa, Patrick Mukumba. (2015). An Assessment of Wind Power Generation Potential for Margate Town in South Africa. International Journal of Energy and Power Engineering, 4(2), 32-37. https://doi.org/10.11648/j.ijepe.20150402.13

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    ACS Style

    Chipo Shonhiwa; Patrick Mukumba. An Assessment of Wind Power Generation Potential for Margate Town in South Africa. Int. J. Energy Power Eng. 2015, 4(2), 32-37. doi: 10.11648/j.ijepe.20150402.13

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    AMA Style

    Chipo Shonhiwa, Patrick Mukumba. An Assessment of Wind Power Generation Potential for Margate Town in South Africa. Int J Energy Power Eng. 2015;4(2):32-37. doi: 10.11648/j.ijepe.20150402.13

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  • @article{10.11648/j.ijepe.20150402.13,
      author = {Chipo Shonhiwa and Patrick Mukumba},
      title = {An Assessment of Wind Power Generation Potential for Margate Town in South Africa},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {2},
      pages = {32-37},
      doi = {10.11648/j.ijepe.20150402.13},
      url = {https://doi.org/10.11648/j.ijepe.20150402.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20150402.13},
      abstract = {Before setting up a wind farm at any given site, it is very important to evaluate its wind power potential to find its physical and economic sustainability. To date, there is limited research output on wind resource assessment for Margate town in South Africa and this might be one of the factors affecting the uptake of wind technologies in this area. This study aimed to assess the wind power potential of Margate. Three-year-long, hourly average wind speed series between January 2010 and December 2012 for Margate town were statistically analysed using the Weibull distribution function. The dimensionless Weibull shape parameter (k) varied from 2.1 to 2.2 while the scale parameter (c) ranged between 4.1 and 4.3 ms-1. The most probable wind speed (vmp) ranged from 3.0 to 3.2 ms-1. The wind power densities fluctuated from 57.8 to 64.0 Wm-2.The average of the measured wind speeds (vm) for the whole period was less than 5 ms-1.Basing on the wind classification done by European Wind Energy Association (EWEA), Margate town is not favourable for the installation of wind turbines. However according to the rule of thumb for mean yearly wind speeds set by American Wind Energy Association (AWEA), the town is suitable for installation of stand-alone systems. It is thus recommended to small scale wind turbines for stand-alone applications such as supplying power to individual houses and irrigation in this town.},
     year = {2015}
    }
    

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    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
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    UR  - https://doi.org/10.11648/j.ijepe.20150402.13
    AB  - Before setting up a wind farm at any given site, it is very important to evaluate its wind power potential to find its physical and economic sustainability. To date, there is limited research output on wind resource assessment for Margate town in South Africa and this might be one of the factors affecting the uptake of wind technologies in this area. This study aimed to assess the wind power potential of Margate. Three-year-long, hourly average wind speed series between January 2010 and December 2012 for Margate town were statistically analysed using the Weibull distribution function. The dimensionless Weibull shape parameter (k) varied from 2.1 to 2.2 while the scale parameter (c) ranged between 4.1 and 4.3 ms-1. The most probable wind speed (vmp) ranged from 3.0 to 3.2 ms-1. The wind power densities fluctuated from 57.8 to 64.0 Wm-2.The average of the measured wind speeds (vm) for the whole period was less than 5 ms-1.Basing on the wind classification done by European Wind Energy Association (EWEA), Margate town is not favourable for the installation of wind turbines. However according to the rule of thumb for mean yearly wind speeds set by American Wind Energy Association (AWEA), the town is suitable for installation of stand-alone systems. It is thus recommended to small scale wind turbines for stand-alone applications such as supplying power to individual houses and irrigation in this town.
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Author Information
  • Mathematics and Physics Department, Bindura University of Science Education, Bindura, Zimbabwe

  • Mathematics and Physics Department, Bindura University of Science Education, Bindura, Zimbabwe

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