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INVESTIGATION OF WIND ENERGY POTENTIAL OF FOUR DIFFERENT SITES OF LIBYA BY USING WEIBULL DISTRIBUTION

Year 2021, Volume: 9 Issue: 3, 766 - 786, 01.09.2021
https://doi.org/10.36306/konjes.915428

Abstract

Bu çalışmada, Libyanın Espiaa, Msallata, Alqatrun ve Adirsiyah bölgesinin rüzgâr enerjisi kapasitesi Weibull dağılımı ile değerlendirilmiştir. Justus'un Ampirik Yöntemi (EMJ), Grafik Yöntemi (GM) ve Maksimum Olabilirlik Yöntemi (MLM) olmak üzere üç farklı Weibull dağılımı yöntemi kullanılmıştır. Sonuç olarak, en iyi hız tahmin performansı grafiksel ve maksimum olabilirlik yöntemleri ile elde edilmiştir. Güç yoğunluğu tahminlemesine göre Msallata'nın rüzgâr gücü potansiyelinin 60 m yükseklikte 444.743 W/m² güç yoğunluğu ile en iyi olduğu ve Espiaa'da ise 414.98 W/m² potansiyel ile ikinci sırada olduğunu görülmüştür. Alqatrun 184.134 W/m² güç yoğunluğu ile üçüncü sırada, sonuncu sırada ise 101.201 W / m² potansiyeli ile Adirsiyah belirlenmiştir. 20 m yükseklik için Msallata'nın güç yoğunluğu 418.502 W/m², Espiaa'da 414.873 W/m², Alqatrun'da 137.736 W/m² ve Adirsiyah'da 77.993 W/m² olarak belirlenmiştir. Maksimum güç potansiyeli Msallata'da, minimum ise Adirsiyah'da belirlenmiştir. Gerçek sonuçlara en yakın değerler sağlayan en uygun istatistiksel yöntemi belirlemek için varyans (R²), kök ortalama kare hata (RMSE), ortalama sistematik hata (MBE) ve ortalama mutlak hata (MAE) değerleri hesaplanmış ve değerlendirilmiştir. Adirsiyah'ta 20 m yükseklik için en iyi sonuçlar GM yöntemi ile elde edilmiştir. Bu durum hesaplanan 0.9948 maksimum R² ve minimum 0.0245, 0.00037 RMSE ve MAE değerleri ile değerlendirilmiştir. Alqatrun'da MLM yöntemi 0.9899 R², 0.0335 RMSE, 0.00049 MAE ile en uygun yöntem olarak ve en düşük karşılaştırma değeri olan 1.7e-5 MBE ile belirlenmiştir. Espiaa için GM yöntemi 0.9984 R², 0.0186 RMSE, 1.23e-06 MBE ve 0.00033 MAE değerleri ile en uygun yöntem olarak belirlendi. Msallata için EMJ yöntemi 0.9985 R², 0.0146 RMSE, 2,4e- 07 MBE ve 0.00022 MAE değerleri ile en iyi yöntem olarak belirlendi.60 m yükseklik için Adirsiyah'ta EMJ yöntemi 0,9957 R², 0.0221 RMSE ve 0,00027 MAE değerleri ile en uyumlu sonuçları vermiştir. Alqatrun'da MLM yöntemi 0,9979 R², 0,0151 RMSE, 2,63e-06 MBE ve 0.00024 MAE değeri ile en yakın sonuçları sağlamıştır. Espiaa için MLM yöntemi 0,9988 R², 0,0163 RMSE, 1,21e-06 MBE ve 0,00029 MAE değeri ile en uygun yöntem olarak belirlenmiştir. Msallata için EMJ yöntemi 0,9986 R², 0,0163 RMSE, 1,83e-07 MBE ve 0,00019 MAE ile en uygun yöntem olarak belirlenmiştir.

References

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Libya'nın Dört Farklı Bölgesinin Rüzgâr Enerji Potansiyelinin Weibull Dağılımı ile İncelenmesi

Year 2021, Volume: 9 Issue: 3, 766 - 786, 01.09.2021
https://doi.org/10.36306/konjes.915428

Abstract

In this study wind energy capacity of Libyan sites are Espiaa, Msallata, Alqatrun, and Adirsiyah has been assessed with the Weibull distribution. Three different methods of Weibull distribution that are the Empirical Method of Justus (EMJ), the Graphical Method (GM), and the Maximum Likelihood Method (MLM) was used. As a result, the best velocity estimation performance has been obtained with graphical and maximum likelihood methods. The power density estimation showed that the wind power potential of Msallata is best with the value of 444.743 W/m² power density at 60 m and in Espiaa is in the second order with 414.98 W/m² potentials. Alqatrun is in the third order with the 184.134 W/m² power density and the last one is Adirsiyah with 101.201 W/m² potentials. When ordered for an elevation of 20 m, the power density of Msallata was found 418.502 W/m², 414.873 W/m² at Espiaa, 137.736 W/m² at Alqatrun, and 77.993 W/m² at Adirsiyah. The maximum power potential was determined at Msallata and the minimum at Adirsiyah. To investigate the most appropriate statistical method that provides the closest values to the real results, variance (R²), root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) values were calculated and assessed that at Adirsiyah, the best results were obtained with the GM method for a height of 20 m. This situation was evaluated with a calculated maximum R² of 0.9948 and minimum values of 0.0245, 0.00037 RMSE, and MAE. For Espiaa, the GM method was determined as the most appropriate method with 0.9984 R², 0.0186 RMSE, 1.23e-06 MBE, and 0.00033 MAE values. For Msallata, the EMJ method was determined as the best method with 0.9985 R², 0.0146 RMSE, 2,4e-07 MBE, and 0.00022 MAE. For 60 m altitude, the EMJ method gave the most compatible results with 0.9957 R², 0.0221 RMSE, and 0.00027 MAE values in Adirsiyah. In Alqatrun, the MLM method provided the closest results with 0.9979 R², 0.0151 RMSE, 2.63e-06 MBE, and 0.00024 MAE. The MLM method for Espiaa was determined as the most suitable method with 0.9988 R², 0.0163 RMSE, 1.21e-06 MBE, and 0.00029 MAE. For Msallta, the EMJ method was determined as the most suitable method with 0.9986 R², 0.0163 RMSE, 1.83e-07 MBE, and 0.00019 MAE.

References

  • [1] Ansari, M., 2018, "Libyan oil: Prospects for stability and growth", Apicorp Energy Research, Vol. 3, No. 14, pp. 1–4.
  • [2] Morrow, S., "Blockade will halve Libya oil output: National Oil Co.", https://www.aa.com.tr/en/energy/oil/blockade-will-halve-libya-oil-output-national-oil-co/29834, access date: February 18, 2021.
  • [3] St John, R.B., 2008, "The changing Libyan economy: Causes and consequences", The Middle East Journal, Vol. 62, No. 1, pp. 75–91.
  • [4] Khalil, A., Asheibi, A., " The chances and challenges for renewable energy in Libya", 4th International Conference on Renewable Energy Research and Applications, Palermo, Italy, 1-6, November 22-25, 2015.
  • [5] Mrehel, O.G., Gerara Salama, A., "Energy generation potential from wind power in the Southern Libyan Regions", 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, 548–553, Tripoli, Libya, May 25-27, 2021.
  • [6] Elmnifi, M., Alshilmany, M., Abdraba, M., 2018, "Potential of municipal solid waste in Libya for energy utilization", Open Journal of Mechanical Engineering, Vol. 2, No. 1, pp. 1–5.
  • [7] Kutucu, H., Almryad, A., "Modeling of solar energy potential in Libya using an artificial neural network model", 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), 356–359, Lviv, Ukraine, August 23-27, 2016.
  • [8] Belgasim, B., Aldali, Y., Abdunnabi, M.J.R., Hashem, G., Hossin, K., 2018, "The potential of concentrating solar power (CSP) for electricity generation in Libya", Renewable and Sustainable Energy Reviews, Vol. 90, pp. 1–15.
  • [9] Mohamed, O.A., Masood, S.H., 2018, "A brief overview of solar and wind energy in libya: Current trends and the future development", IOP Conference Series: Materials Science and Engineering, Vol. 377, No. 1, 1-12.
  • [10] Nassar, Y.F., Salem, M.A., Iessa, K.R., AlShareef, I.M., Ali, K.A., Fakher, M.A., 2021, "Estimation of CO2 emission factor for the energy industry sector in libya: a case study", Environment, Development and Sustainability, doi: 10.1007/s10668-021-01248-9.
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  • [14] Kassem, Y., Çamur, H., Aateg, R.A.F., 2020, "Exploring solar and wind energy as a power generation source for solving the electricity crisis in Libya", Energies, Vol. 13, No. 14, 3708.
  • [15] Schafer, I., "The renewable energy sector and youth employement in Algeria, Libya, Morocco and Tunisia", https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/The_Renewable_Energy_Sector_and_Youth_Employment_in_Algeria__Libya__Morocco_and_Tunisia.pdf, access date: February 18, 2021.
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  • [17] Hasan, S.H.A., Guwaeder, A., Gao, W., 2017, "Wind energy assessment of the Zawiya Region, in northwest Libya", Energy and Power Engineering, Vol. 09, No. 06, pp. 325–331.
  • [18] Kassem, Y., Çamur, H., AbuGharara, M.A., 2019, "Assessment of wind energy potential for selecting small-scale wind turbines in low wind locations in Libya: A comparative study", International Journal of Engineering Research and Technology, Vol. 12, No. 6, pp. 820–836.
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  • [20] Ali, H.M., Khamiss, R.E., Ahmed, M.Z., "Statistical study and evaluation of six numerical methodes to predict wind speed parameters of the Weibull function in Al- Aziziyah, Libya", 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, 463–467, Tripoli, Libya, May 25-27, 2021.
  • [21] Elfarra, M.A., Salem, S., 2018, "Technical and economical evaluation and GHG analysis of wind power generation in four sites using different Weibull parameters", International Journal of Renewable Energy Research, Vol. 8, No. 3, pp. 1–12.
  • [22] Katsigiannis, Y.A., Stavrakakis, G.S., Pharconides, C., 2013, "Effect of wind turbine classes on the electricity production of wind farms in Cyprus Island", Conference Papers in Energy, Vol. 2013, No. 5, pp. 1–6.
  • [23] Al-Nassar, W., Alhajraf, S., Al-Enizi, A., Al-Awadhi, L., 2005, "Potential wind power generation in the State of Kuwait", Renewable Energy, Vol. 30, No. 14, pp. 2149–2161.
  • [24] Mostafaeipour, A., Sedaghat, A., Dehghan-Niri, A.A., Kalantar, V., 2011, "Wind energy feasibility study for city of Shahrbabak in Iran", Renewable and Sustainable Energy Reviews, Vol. 15, No. 6, pp. 2545–2556.
  • [25] Ko, D.H., Jeong, S.T., Kim, Y.C., 2015, "Assessment of wind energy for small-scale wind power in Chuuk State, Micronesia", Renewable and Sustainable Energy Reviews, Vol. 52, No. 4, pp. 613–622.
  • [26] Irwanto, M., Gomesh, N., Mamat, M.R., Yusoff, Y.M., 2014, "Assessment of wind power generation potential in Perlis, Malaysia", Renewable and Sustainable Energy Reviews, Vol. 38, pp. 296–308.
  • [27] Ahwide, F., Spena, A., El-Kafrawy, A., 2013, "Estimation of electricity generation in libya using processing technology of wind available data: The case study in Derna", APCBEE Procedia, Vol. 5, No. 2–3, pp. 451–467.
  • [28] Kassem, Y., Gökcekus, H., Faraj, R.A., 2019, "Evaluation of the wind energy potential in Libya's Eastern Mediterranean Coast area using Weibull Distribution Function", International Journal of Applied Engineering Research, Vol. 14, No. 10, pp. 2483–2491.
  • [29] Artigao, E., Vigueras-Rodríguez, A., Honrubia-Escribano, A., Martín-Martínez, S., Gómez-Lázaro, E., 2021, "Wind resource and wind power generation assessment for education in engineering", Sustainability, Vol. 13, No. 5, 2444.
  • [30] Irwin, J.S., 1979, "A theoretical variation of the wind profile power-law exponent as a function of surface roughness and stability", Atmospheric Environment, Vol. 13, pp. 191–194.
  • [31] Chaurasiya, P.K., Ahmed, S., Warudkar, V., 2018, "Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground-based Doppler SODAR instrument", Alexandria Engineering Journal, Vol. 57, No. 4, pp. 2299–2311.
  • [32] Aririguzo, J.C., Ekwe, E.B., 2019, "Weibull distribution analysis of wind energy prospect for Umudike, Nigeria for power generation", Robotics and Computer-Integrated Manufacturing, Vol. 55, pp. 160–163.
  • [33] Hulio, Z.H., Jiang, W., Rehman, S., 2019, "Techno - Economic assessment of wind power potential of Hawke's Bay using Weibull parameter: A review", Energy Strategy Reviews, Vol. 26, 100375.
  • [34] Saeed, M.A., Ahmed, Z., Yang, J., Zhang, W., 2020, "An optimal approach of wind power assessment using Chebyshev metric for determining the Weibull distribution parameters", Sustainable Energy Technologies and Assessments, Vol. 37, 100612.
  • [35] Fazelpour, F., Markarian, E., Soltani, N., 2017, "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran", Renewable Energy, Vol. 109, Part A, pp. 646–667.
  • [36] Kaoga, D.K., Danwe, R., Yamigno, S.D., Djongyang, N., 2015, "Performance analysis of methods for estimating Weibull Parameters for wind speed distribution in the district of Maroua", Journal of Fundamental and Applied Sciences, Vol. 6, No. 2, pp. 153-174.
  • [37] Ouahabi, M.H., Elkhachine, H., Benabdelouahab, F., Khamlichi, A., 2020, "Comparative study of five different methods of adjustment by the Weibull model to determine the most accurate method of analyzing annual variations of wind energy in Tetouan - Morocco", Procedia Manufacturing, Vol. 46, pp. 698–707.
  • [38] Akdag, S.A., Dinler, A., 2009, "A new method to estimate Weibull parameters for wind energy applications", Energy Conversion and Management, Vol. 50, No. 7, pp. 1761–1766.
  • [39] Tiam Kapen, P., Jeutho Gouajio, M., Yemélé, D., 2020, "Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon", Renewable Energy, Vol. 159, No. 2, pp. 1188–1198.
  • [40] Saeed, M.A., Ahmed, Z., Zhang, W., 2020, "Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters", Renewable Energy, Vol. 161, pp. 1092–1109.
  • [41] Wen, Y., Kamranzad, B., Lin, P., 2021, "Assessment of long-term offshore wind energy potential in the south and southeast coasts of China based on a 55-year dataset", Energy, Vol. 224, 120225.
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There are 45 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Selcuk Selimli 0000-0002-2551-3860

Fauzi Ammar Shtewı This is me 0000-0002-4846-1776

Abdel Karim Amar Fahed 0000-0003-1995-7630

Çagıl Yaman Koymatcık This is me 0000-0001-7529-8348

Prof. Dr. Mehmet Özkaymak 0000-0002-4575-8988

Publication Date September 1, 2021
Submission Date April 14, 2021
Acceptance Date August 12, 2021
Published in Issue Year 2021 Volume: 9 Issue: 3

Cite

IEEE S. Selimli, F. A. Shtewı, A. K. A. Fahed, Ç. Yaman Koymatcık, and P. D. M. Özkaymak, “INVESTIGATION OF WIND ENERGY POTENTIAL OF FOUR DIFFERENT SITES OF LIBYA BY USING WEIBULL DISTRIBUTION”, KONJES, vol. 9, no. 3, pp. 766–786, 2021, doi: 10.36306/konjes.915428.