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ASSESSMENT OF WIND CHARACTERISTICS AND WIND ENERGY POTENTIAL IN WEST BLACK SEA REGION OF TURKEY

Year 2019, Volume: 20 Issue: 3, 227 - 237, 26.09.2019
https://doi.org/10.18038/estubtda.624359

Abstract



In this study, wind characteristics
and wind energy potential of seven cities from The West of Black Sea Region in
Turkey were analyzed. The wind data were obtained by National State
Meteorological Service. It was measured at 10 meters’ height in the date range
2010-2014. Weibull probability density function was calculated and estimated
Weibull shape parameter k and scale
parameter c, with the data for those
locations. According to the power calculations of the region, annual mean power
densities of Zonguldak, Bartın, Kastamonu, Bolu, Karabük, Düzce and Sinop were
calculated as 105 W/m2, 37,4 W/m2, 40 W/m2,
27,15 W/m2, 27 W/m2, 26,3 W/m2 and 209 W/m2
at the height of 50 m, respectively. The results show that, the region has not
enough wind energy potential considering investment on wind power energy except
Sinop.

References

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  • [7] Fyrippis AI, Axaopoulos PJ, Panayiotou G. Wind energy potential assessment in Naxos Island–Greece. Appl Energy 2010; 87(2): 577–586.
  • [8] Weigt, H. Germany's wind energy: the potential for fossil capacity replacement and cost saving. Applied Energy 2009; 86: 1857–1863, doi: 10.1016/j.apenergy.2008.11.031.
  • [9] Karsli V, Geçit C. An investigation on wind power potential of Nurdağı-Gaziantep, Turkey, Renewable Energy 2003; 28: 823-830.
  • [10] Akpinar EK, Akpınar S. Determination of the Wind Energy Potential for Maden Elazig, Turkey. Energy Conversation and Management 2004; 45: 2901-2914.
  • [11] Akpinar E, Kavak A. Statistical investigation of wind energy potential. Energy Sourc, Part A: Recov Utilization Environ Effects 2006; 28(9): 807–820.
  • [12] Sahin B, Bilgili M, Akilli H. The wind power potential of the eastern Mediterranean region of Turkey. J Wind Eng Ind Aerodynam 2005; 93: 171–183.
  • [13] Akpınar A. Evaluation of Wind Energy Potentiality at Coastal Locations along the North Eastern Coasts of Turkey. Energy 2013; 50: 395–405. doi: 10.1016/j.energy.2012.11.019.
  • [14] Wu J, Wang J, Chi D. Wind energy potential assessment for the site of Inner Mongolia in China. Renewable and Sustainable Energy Reviews 2013; 21: 215–228.
  • [15] Köse B, Düz M, Güneşer MT, Recebli, Z. Estimating Wind Energy Potential with Predicting Burr Lsm Parameters: A Different Approach. Sigma J Eng & Nat Sci 2018; 36 (2): 387–402.
  • [16] Köse B, Küçük AZ, Ordu B, Çay Y. An overview of the probability distribution functions for forecasting of wind energy potential and Karabük wind data application. Anadolu University Journal of Science and Technology, An Applied Sciences and Engineering 2013; 3: 1-15.
  • [17] Akdağ SA, Dinler A. A new method to estimate weibull parameters for wind energy applications. Energy Conversion and Management 2009; 50(7): 1761–1766.
  • [18] Alamdari P, Nematollahi O, Mirhosseini M. Assessment of wind energy in Iran: A review. Renewable and Sustainable Energy Reviews 2012; 16 (1): 836–860.
  • [19] Usta I, Kantar YM. Analysis of some flexible families of distributions for estimation of wind speed distributions. Applied Energy 2012; 89: 355-367.
Year 2019, Volume: 20 Issue: 3, 227 - 237, 26.09.2019
https://doi.org/10.18038/estubtda.624359

Abstract

References

  • [1] Global Wind 2015 Report, [Online] : 31.10.2018. http://www.gwec.net/publications/global-wind-report-2/global-wind-report-2015-annual-market-update/.
  • [2] T.C. Enerji Bakanlığı, Strateji Geliştirme Daire Başkanlığı. Dünya ve Türkiye Enerji Kaynakları Görünümü; 3: 26., [Online]: 09.09.2016. http://www.enerji.gov.tr/tr-TR/Enerji-ve-Tabii-Kay¬naklar-Gorunumleri.
  • [3] Köse B. Ruzgar Hiz ve Potansiyelinin Stokastik Sureclerle Modellenerek Karabuk Ilinde Uygulanmasi, Doktora Tezi. Fen Bilimleri Enstitüsü, Karabük Üniversitesi, 2018.
  • [4] Köse B, Atila Ü, Güneşer MT, Recebli Z. An Approach to Estimate Hourly & Daily Mean Wind Speed and Comparison with Artificial Neural Network. In: 10th International Clean Energy Symposium, 24-26 October 2016; İstanbul.
  • [5] Gökçek M, Bayülken A, Bekdemir Ş. Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey, Renewable Energy 2007; 32:1739-1752.
  • [6] Carta JA, Ramirez P, Velazquez S. A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renewable and Sustainable Energy Reviews 2009; 13(5): 933-955.
  • [7] Fyrippis AI, Axaopoulos PJ, Panayiotou G. Wind energy potential assessment in Naxos Island–Greece. Appl Energy 2010; 87(2): 577–586.
  • [8] Weigt, H. Germany's wind energy: the potential for fossil capacity replacement and cost saving. Applied Energy 2009; 86: 1857–1863, doi: 10.1016/j.apenergy.2008.11.031.
  • [9] Karsli V, Geçit C. An investigation on wind power potential of Nurdağı-Gaziantep, Turkey, Renewable Energy 2003; 28: 823-830.
  • [10] Akpinar EK, Akpınar S. Determination of the Wind Energy Potential for Maden Elazig, Turkey. Energy Conversation and Management 2004; 45: 2901-2914.
  • [11] Akpinar E, Kavak A. Statistical investigation of wind energy potential. Energy Sourc, Part A: Recov Utilization Environ Effects 2006; 28(9): 807–820.
  • [12] Sahin B, Bilgili M, Akilli H. The wind power potential of the eastern Mediterranean region of Turkey. J Wind Eng Ind Aerodynam 2005; 93: 171–183.
  • [13] Akpınar A. Evaluation of Wind Energy Potentiality at Coastal Locations along the North Eastern Coasts of Turkey. Energy 2013; 50: 395–405. doi: 10.1016/j.energy.2012.11.019.
  • [14] Wu J, Wang J, Chi D. Wind energy potential assessment for the site of Inner Mongolia in China. Renewable and Sustainable Energy Reviews 2013; 21: 215–228.
  • [15] Köse B, Düz M, Güneşer MT, Recebli, Z. Estimating Wind Energy Potential with Predicting Burr Lsm Parameters: A Different Approach. Sigma J Eng & Nat Sci 2018; 36 (2): 387–402.
  • [16] Köse B, Küçük AZ, Ordu B, Çay Y. An overview of the probability distribution functions for forecasting of wind energy potential and Karabük wind data application. Anadolu University Journal of Science and Technology, An Applied Sciences and Engineering 2013; 3: 1-15.
  • [17] Akdağ SA, Dinler A. A new method to estimate weibull parameters for wind energy applications. Energy Conversion and Management 2009; 50(7): 1761–1766.
  • [18] Alamdari P, Nematollahi O, Mirhosseini M. Assessment of wind energy in Iran: A review. Renewable and Sustainable Energy Reviews 2012; 16 (1): 836–860.
  • [19] Usta I, Kantar YM. Analysis of some flexible families of distributions for estimation of wind speed distributions. Applied Energy 2012; 89: 355-367.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Bayram Köse 0000-0003-0256-5921

M. Tahir Guneser 0000-0003-3502-2034

Publication Date September 26, 2019
Published in Issue Year 2019 Volume: 20 Issue: 3

Cite

AMA Köse B, Guneser MT. ASSESSMENT OF WIND CHARACTERISTICS AND WIND ENERGY POTENTIAL IN WEST BLACK SEA REGION OF TURKEY. Estuscience - Se. September 2019;20(3):227-237. doi:10.18038/estubtda.624359