Wind Resource Assessment for southern part of Libya: Case Study of Hun
DOI:
https://doi.org/10.51646/jsesd.v8i1.18Keywords:
wind potential, wind energy, capacity factor, wind turbine classes, availabilityAbstract
The purpose of this study is to analyze the wind energy resource potential at Hun. Wind data was analyzed using diffrent statistical models and calculations were performed to forecast wind energy & power density at the site. Energy production was estimated using diffrent wind turbines which were selected according IEC standards criteria and performance of these wind turbines. Detailed wind resource data analysis was performed for the proposed site using Excel spreadsheet for one-year period from (April 2011 to March 2012). Th wind data are measured at four heights of (20 m, 40m, 60m and 61m) above ground level (a.g.l). Th analysis showed that the annual average wind speed is 5.69 m/s and the power density is about 190 W/m2 at 61m height. It could be noticed that at 61m height, the highest scale parameter is 7.25 m/s in April while the lowest scale parameter is 5.71 m/s in October. Th annual shape and scale parameters range from 2.27 at 61m to 2 at 20m, and from 6.42 m /s at 61m to 5 m /s at 20m, respectively. 90% of the speeds are below 11m/s, 84% are below 10m/s and 50% are above 6 m/s. Th maximum speed is 21 m/s with 0.14% occurrence. Th wind shear exponent was evaluated as 0.18 and the roughness length for the site as 0.17 m, which indicates that the roughness class for the location is 2.5. According to the performed analysis, the wind turbines suitable for this site should be of class III/B. Comparison of three wind turbines indicated that Vestas V112-3000 gave the highest capacity factor of 42% in April and an availability of 83% while Nordex N100- 2500 gave capacity factor of 41% for the same month and availability of 83.7%.
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References
IRENA- Renewable Capacity Statistics -2019
IRENA - Renewable energy highlights-March 2019
World Wind Resource Assessment Report, WWEA Technical Paper Series (TP-01-14), December 2014
W. B. El-Osta, A. El-Taher, F. Gumati “Evaluation of Wind Energy Potential in Libya”, Applied Energy, Special Issue Proceedings, 5th Arab Int. Solar Energy Conf., Bahrain, 13-16 Nov. 1995, pp. 675- 684, Elsevier Applied Science, 1995.
W. B. El-Osta, M. A. Muntaser, and Y. M. Khalifa, “Wind Atlas for the Northern Coast of Libya”, Proceedings of OFFSHORE WIND ENERGY IN THE MEDITERRAINEAN AND OTHER EUROPEAN SEAS: Technology and Potential Applications, OWEMES’97, 10-11 April 1997, Sardinia, Italy
A. M. Elmabrouk, “ESTIMATION OF WIND ENERGY AND WIND IN SOME AREAS (SECOND ZONE) IN LIBYA” EVRE, Monaco, March 26-29, 2009.
Dimitrios Mentis, Wind Energy Assessment in Africa A GIS-based approach, Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI-2013,
Estimating the Renewable Energy Potential in Africa A GIS-based approach, IRENA, 2014
Lu, Xi, Michael B. McElroy, and Juha Kiviluoma. 2009. Global potential for wind-generated electricity. Proceedings of the National Academy of Sciences of the U. S. A.,
Wedad El-Osta, Wind Energy Potential in Libyan and its Role in Future Libyan Energy Mix, Workshop on Energy Resources Choices in Libya for Future Energy Mix, Libyan Atomic Energy Establishment, Janzour, Libya, Jan. 28, 2015.
W.B. El-Osta, and Y. Khalifa, “Prospects of Wind Energy Plants in Libya: A Case Study”, Renewable Energy, 28 (2003) 363-371, Elsevier Science Ltd.
W.B El-Osta, M.A. Ekhlat, A. S. Yagub, Y. Khalifa, E Borass” Estimation of Capacity Credit for wind Power in Libya”, International Journal of Energy Technology and Policy, Inderscience Pub., Vol.3, No. 4, 2005, pp 363-377.
Sathyajith Mathew, Wind Energy Fundamentals, Resource Analysis and Economics, Springer-Verlag Berlin Heidelberg 2006.
Justus, C.G.; Hargraves, W.R.; Mikhail, A.; Graber, D. Methods for estimating wind speed frequency distributions. J. Appl. Meteorol. 1978, 17, 350–353
Salahaddin A. Ahmed, Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq, International Journal of Physical Sciences Vol. 8(5), pp. 186-192, 9 February, 2013
Ebru Kavak Akpinar, Sinan Akpinar, Nilay Balpetek, Statistical Analysis of Wind Speed Distribution Based on Weibull and Rayleigh Methods of ISKENDERUN-TURKEY, European Journal of Technic- EJT, Vol 8, Number 1, 2018.
Mania A. W., Kamau, J.N., Timonah, S., Analysis of Wind Energy Potential by Different Methods Based on Weibull Statistics for A Site in Juja, Kenya, Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 2458-9403 Vol. 3 Issue 6, June - 2016.
Qing X, Statistical analysis of wind energy characteristics in Santiago island, Cape Verde, Renewable Energy (2017).
Emil Hernández Arroyo1, Edwin Córdoba Tuta, Gabriel García Sánchez, Comparative Analysis of the Weibull Model and Observed Wind Data in the City of Floridablanca, Colombia, TECCIENCIA, Vol. 13 No. 25, 65-70, 2018.
D.K. Kidmo 1*, R. Danwe 2, S.Y. Doka 3, and N. Djongyang, Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon, Revue des Energies Renouvelables Vol. 18, N°1 (2015) 105 – 125
H.S. Bagiorgas, G. Mihalakakou, S. Rehman, and L.M. Al-Hadhrami, ‘Wind Power Potential Assessment for Seven Buoys Data Collection Stations in Aegean Sea Using Weibull Distribution Function’, Journal of Renewable and Sustainable Energy, Vol. 4, N°1, pp. 013119-1 - 013119-16, 2012.
IEC 61400-1, 3rd -ed, Wind turbines – Part 1: Design Requirements, 2005.
H. Jiang, J. Wang, J. Wu, W. Geng, Comparison of numerical methods and metaheuristic, optimization algorithms for
estimating parameters for wind energy potential assessment in low wind regions. Renewable and Sustainable Energy Review 69, Dec. 2016.
Jianzhou Wang, Xiaojia Huang*, Qiwei Li, Xuejiao Ma, Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China, Energy 164 (2018).
G.M Masters,. (2004). Renewable and Efficient Electric Power Systems (pp- 320), John Wiley and Sons, ISBN 0471280607, USA.
Alan G. Davenport, C. Sue B. Grimmond, Tim R. Oke, and Jon Wieringa (200). Estimating the Roughness of Cities and Sheltered Country, American Meteorological Society.
Jon Wieringa, Ernest Rudel, Station Exposure Metadata Needed for Judging and Improving Quality of Observations of Wind, Temperature and Other Parameters.
https://en.wind-turbine-models.com/turbines/16-vestas-v90
https://en.wind-turbine-models.com/turbines/693-vestas-v112-3.3
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