Techno-Economic Analysis for Wind Energy Projects: A Comparative Study With Three Wind Turbines Based on Real-Site Data

Year 2023, Volume: 3 Issue: 3, 115 - 124, 31.10.2023

Gül Nihal Güğül , Gaye Demirhan Başbilen Derek Baker

https://doi.org/10.5152/tepes.2023.23019

https://izlik.org/JA28TZ38DS

Abstract

The majority of the techno-economic studies in literature utilize the meteorological stations data or satellite data which generally underestimate the wind energy production potential. This leads to a low reliability in economic decisions. This study is unique in the sense that it uses real-site data which comes from an operating wind power plant and derives economical results based on this real-site data. The current data were obtained from 12 wind locations in Bursa, Turkey, at 10-min intervals, at 100-m hub height over 2 years. Weibull, turbulence, and power density parameters were calculated. Wind energy investigations were performed based on three alternative wind turbine types. High scale parameters (7.6-9.1) were observed in all locations. Low shape parameters (1.3-2.9) and high standard deviation resulted in high turbulence index (0.54 on average). Monthly mean wind speed varied between 4.6 and 11.7 m/s. The financial calculations show that the lowest levelized cost of energy (LCOE) belongs to the alternative with Goldwind turbine alternative. The models used for invest- ment decision in this study have both macro- and microlevel implications. The political actors can require site-specific measurements from the wind project applications while economic actors should reshape their decision-making policies based on site-specific site measurements and alternative economic analysis such as LCOE.

Keywords

Electricity generation , LCOE , techno-economic analysis , wind turbine

Thanks

The authors would like to express their great appreciation to “Artı Enerji” for their support.

References

• 1. A. G. Olabi, and M. A. Abdelkareem, “Renewable energy and climate change,” Renew. Sustain. Energy Rev., vol. 158, p. 112111, 2022.
• 2. Y. Yuchen, K. Javanroodi, and V. M. Nik, “Climate change and renewable energy generation in Europe—Long-term impact assessment on solar and wind energy using high-resolution future climate data and considering climate uncertainties,” Energies, vol. 15, p. 1, 302, 2022.
• 3. B. Bilal, K. H. Adjallah, K. Yetilmezsoy, M. Bahramian, and E. Kıyan, “Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for northwest Africa,” Energy, vol. 218, p. 119558, 2021.
• 4. O. Rodriguez-Hernandez, M. Martinez, C. Lopez-Villalobos, H. Garcia, R. Campos-Amezcua, and T.-E. Area, “Feasibility study of small wind turbines in the valley of Mexico Metropolitan area,” Energies, vol. 12, p. 5, 890, 2019.
• 5. S. Ali, and C.-M. Jang, “Selection of best-suited wind turbines for new wind farm sites using techno-economic and GIS analysis in South Korea,” Energies, vol. 12, no. 16, p. 16, 3140, 2019.
• 6. F. Al-Turjman, Z. Qadir, M. Abujubbeh, and C. Batunlu, “Feasibility analysis of solar photovoltaic-wind hybrid energy system for household applications,” Comput. Electr. Eng., vol. 86, p. 106743, 2020.
• 7. Y. Wen, B. Kamranzad, and P. Lin, “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, p. 120225, 2021.
• 8. Y. S. Torres, and P. R. Ramos, “Techno-Economic Feasibility Study of Small Wind Turbines in Havana city,” Rev. Cuba. Ingeniería, vol. 7, no. 1, pp. 47–57, 2021.
• 9. A. F. Gomes da Silva, C. Regina de Andrade, and E. L. Zaparoli, “Wind power generation prediction in a complex site by comparing different numerical tools,” J. Wind Eng. Ind. Aerodyn., vol. 216, p. 104728, 2021.
• 10. H. Arslan, H. Baltaci, B. O. Akkoyunlu, S. Karanfil, and M. Tayanc, “Wind speed variability and wind power potential over Turkey: Case studies for Çanakkale and İstanbul,” Renew. Energy, vol. 145, pp. 1020–1032, 2020.
• 11. M. Purlu, S. Beyarslan, and B. E. Turkay, “Optimal Design of Hybrid Grid-connected Microgrid with Renewable Energy and Storage in a Rural Area in Turkey by Using HOMER,” International Conference on Electrical and Electronics Engineering, Bursa, 2021.
• 12. M. Pürlü, S. Beyarslan and B. E. Türkay, “On-Grid and Off-Grid Hybrid Renewable Energy System Designs with HOMER: A Case Study of Rural Electrification in Turkey,” Turk J Electr Power Energy Syst, pp. 75-84, 2022.
• 13. DEWI GmbH, Site-Related Wind Potential Analysis and Enegy Yield Assessment at the Site Karacabey. Wilhelmshaven: DEWI GmbH, 2010.
• 14. General Directorate of Meteorology, “Station information database”. Available: https://mgm.gov.tr/kurumsal/istasyonlarimiz.aspx?il=Bursa. [Accessed: 21 11 2022].
• 15. T.R. Ministry of Energy and Natural Resources, “Wind,”. Available: https://enerji.gov.tr/eigm-yenilenebilir-enerji-kaynaklar-ruzgar. [Accessed:10 10 2022].
• 16. N. Sarma, P. M. Tuohy, O. Özgönenel, and S. Djurović, “Early life failure modes and downtime analysis of onshore type-III wind turbines in Turkey,” Electr. Power Syst. Res., vol. 216, p. 108956, 2023.
• 17. TEIAS, “Power Plants Installed Capacity Reports,” [Online]. Available: https://www.teias.gov.tr/kurulu-guc-raporlari. [Access: 21 10 2022].
• 18. Wind Europe, “Turkey Reaches 10 GW wind energy milestone,” 9 September 2021. Available: https://windeurope.org/newsroom/news/turkey- reaches-10-gw-wind-energy-milestone/. [Accessed: 25 10 2022].
• 19. Nordex, “N100/2500,”, 2020. Available: https://www.scribd.com/document/400033062/Nordex-GammaN100-2500-En. [Accessed: 29 09 2023].
• 20. Arti Energy. 2020. Available: http://www.artienerji.com.tr/index-eng.htm. [Accessed: 21 10 2022].
• 21. T.-P. Chang, F.-J. Liu, H.-H. Ko, S.-P. Cheng, L.-C. Sun, S.-C. Kuo, “Comparative analysis on power curve models of wind turbine generator in estimating capacity factor,” Energy, vol. 73, pp. 88–95, 2014.
• 22. C. G. Justus, W. R. Hargraves, A. Mikhail, and D. Graber, “Methods for estimating wind speed frequency distributions,” J. Appl. Meteorol., vol. 17, no. 3, pp. 350–353, 1978.
• 23. IRENA, IRENA Renewable Power Generation Costs in 2021, 2021. Available: https://www.irena.org/publications/2022/Jul/Renewable-Power- Generation-Costs-in-2021 [Accessed: 12 10 2022].
• 24. IRENA, Renewable power generation costs in 2017, Abu Dhabi, 2018. Available: https://www.irena.org/-/media/Files/IRENA/Agency/Publica- tion/2018/Jan/IRENA_2017_Power_Costs_2018_summary.pdf [Accessed: 12 10 2022].
• 25. K. Hansen, “Decision-making based on energy costs: Comparing levelized cost of energy and energy system costs,” Energy Strategy Rev., vol.24, pp. 68–82, 2019.
• 26. X. Ouyang, and B. Lin, “Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China,” Energy Policy, vol. 70, pp. 64–73, 2014.
• 27. S. Larsson, D. Fantazzini, S. Davidsson, S. Kullander, and M. Höök, “Reviewing electricity production cost assessments,” Renew. Sustain. En. Rev., vol. 30, pp. 170–183, 2014.
• 28. R. Gross, W. Blyth, and P. Heptonstall, “Risks, revenues and investment in electricity generation: Why policy needs to look beyond costs,” Energy Economics, vol. 32, no. 4, pp. 796–804, 2010.
• 29. M. Purlu, and U. Ozkan, “Economic and environmental analysis of grid- connected rooftop photovoltaic system using HOMER,” Turk. J. Electr. Power Energy Syst, vol. 3, no. 1, pp. 39–46, 2023.
• 30. CEIC, Turkey Central Bank discount rate. Available: https://www.cei cdata.com/en/turkey/saving-discount-rate-and-interbank-rate/centr al-bank-discount-rate. [Accessed: 27 09 2022].
• 31. US Department of Energy, US 2021 discount rates, 01 04 2021. Available: https://www.energy.gov/sites/default/files/2021-04/2021discountra tes.pdf. [Accessed: 01 10 2022].
• 32. NREL, Levelized Cost of Energy Calculator, Available: https://www.nrel. gov/analysis/tech-lcoe.html [Accessed: 25 09 2023].