Techno-Economic Assessment of Onshore Wind Power Plant Site Selection in Türkiye Through a Decision Support Model Based on Transmission Cost Scenarios

Abstract

In recent years, renewable energy (RE) has become a significant topic both globally and in Türkiye. Renewable energy systems (RESs), in particular, appear to play a significant role in decreasing many of the risks the world faces, such as global warming and increased carbon emissions. This issue is currently on the agenda of the United Nations Sustainable Development Goals (SDGs) under the heading of SDG 7: Affordable and Clean Energy, and research in this area is increasing over time. In this study, a decision-support model was developed to evaluate suitable sites for wind power plant (WPP) investments. In addition to considering the plant installation cost and operation and maintenance (O&M) cost, the model incorporates different pricing scenarios for transmission costs. Break-even times (BETs) were calculated for representative cities across the seven regions of Türkiye, taking into account the revenues obtained from the sale of energy generated by the WPPs. In addition, net present value (NPV), internal rate of return (IRR), and sensitivity analyses were conducted to further evaluate the long-term economic feasibility and robustness of the candidate investment locations. The results revealed that the developed framework and the comparative economic analysis could serve as a guide from an innovative perspective for entrepreneurs planning to invest in this sector in Türkiye.

Keywords

• Renewable Energy
• Techno-Economic Assessment
• Energy Transmission Cost
• Decision Support Model
• Site Selection

References

1. [1]. Østergaard, PA, Duic, N, Noorollahi, Y, Mikulcic, H, Kalogirou, S. 2020. Sustainable development using renewable energy technology. Renewable Energy; 146: 2430-2437. https://doi.org/10.1016/j.renene.2019.08.094.
2. [2]. Al-Shetwi, AQ. 2022. Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges. Science of the Total Environment; 822: 153645. https://doi.org/10.1016/j.scitotenv.2022.153645.
3. [3]. United Nations Environment Programme (UNEP). 2026. SDG Goal 7: Affordable and Clean Energy. Available online: https://www.unep.org/topics/sustainable-development-goals/why-do-sustainable-development-goals-matter/goal-7 (accessed at 15.02.2026).
4. [4]. International Renewable Energy Agency (IRENA). 2025. Renewable Capacity Highlights; IRENA: Abu Dhabi, United Arab Emirates. Available online: https://static.poder360.com.br/2025/04/capacidade-energia-renovavel-mar2025-irena.pdf (accessed at 15.02.2026).
5. [5]. Subramanian, ASR, Gundersen, T, Adams, TA. 2018. Modeling and simulation of energy systems: A review. Processes; 6: 238. https://doi.org/10.3390/pr6120238.
6. [6]. Schlachtberger, DP, Brown, T, Schäfer, M, Schramm, S, Greiner, M. 2018. Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints. Energy; 163: 100-114. https://doi.org/10.1016/j.energy.2018.08.070.
7. [7]. McCalley, JD, Krishnan, V. 2014. A survey of transmission technologies for planning long-distance bulk transmission overlay in US. International Journal of Electrical Power & Energy Systems; 54: 559-568. https://doi.org/10.1016/j.ijepes.2013.08.008.
8. [8]. Ruderer, D., Zöttl, G. 2019. Transmission pricing and investment incentives. Utilities Policy; 55: 14-30. https://doi.org/10.1016/j.jup.2018.08.005.

9. [9]. Lewis, G.M. 2010. Estimating the value of wind energy using electricity locational marginal pricing. Energy Policy; 38(7): 3221–3231. https://doi.org/10.1016/j.enpol.2009.07.045.
10. [10]. Pollitt, M.G. 2023. Locational Marginal Prices (LMPs) for Electricity in Europe? The Untold Story. Energy Policy Research Group Working Paper, University of Cambridge. https://www.repository.cam.ac.uk/items/748513c4-ca09-4537-90a9-8e4bf7e428a3.
11. [11]. Zabihi, A., Badesa, L., Hernández, A. 2026. On the impact of voltage unbalance on distribution locational marginal prices. International Journal of Electrical Power & Energy Systems; 177: 111808. https://doi.org/10.1016/j.ijepes.2026.111808.
12. [12]. Hadadi, A., Aliabadi, M.H., Rabbanifar, P. 2025. A stochastic power-based distribution locational marginal pricing framework for frequency regulation in isolated micro-grids using battery energy storage systems. International Journal of Electrical Power & Energy Systems; 172: 111180. https://doi.org/10.1016/j.ijepes.2025.111180.
13. [13]. Eicke, A., Schittekatte, T. 2022. Fighting the wrong battle? A critical assessment of arguments against nodal electricity prices in the European debate. Energy Policy; 170: 113220. https://doi.org/10.1016/j.enpol.2022.113220.
14. [14]. Knörr, J., Bichler, M., Dobos, T. 2024. Zonal vs. Nodal Pricing: An Analysis of Different Pricing Rules in the German Day-Ahead Market. arXiv preprint; arXiv:2403.09265. https://doi.org/10.48550/arXiv.2403.09265.
15. [15]. Holmberg, P., Lazarczyk, E. 2015. Comparison of Congestion Management Techniques: Nodal, Zonal and Discriminatory Pricing. The Energy Journal; 36(2): 145–166. https://doi.org/10.5547/01956574.36.2.7.
16. [16]. Kang, C.Q., Chen, Q.X., Lin, W.M., Hong, Y.R., Xia, Q., Chen, Z.X., Wu, Y., Xin, J.B. 2013. Zonal marginal pricing approach based on sequential network partition and congestion contribution identification. International Journal of Electrical Power & Energy Systems; 51: 321–328. http://dx.doi.org/10.1016/j.ijepes.2013.02.033.
17. [17]. Yoro, KO, Daramola, MO, Sekoai, PT, Armah, EK, Wilson, UN. 2021. Advances and emerging techniques for energy recovery during absorptive CO₂ capture: A review of process and non-process integration-based strategies. Renewable and Sustainable Energy Reviews; 147: 111241. https://doi.org/10.1016/j.rser.2021.111241.
18. [18]. Ahuja, D, Tatsutani, M. 2009. Sustainable energy for developing countries. S.A.P.I.EN.S; 2: 1. Available online: https://journals.openedition.org/sapiens/823 (accessed at 15.02.2026).
19. [19]. Hossain, SM, Hasan, MM. 2018. Energy management through biogas based electricity generation system during load shedding in rural areas. TELKOMNIKA; 16: 525-532. https://doi.org/10.12928/TELKOMNIKA.v16i2.5190.
20. [20]. Sogukpinar, H, Bozkurt, I, Oyucu, S, Aksoz, A. 2025. Unveiling the wind energy future of Türkiye with policies, technologies and potential. Heliyon; 11: e42592. https://doi.org/10.1016/j.heliyon.2025.e42592.
21. [21]. Bulut, A, Bingöl, O. 2023. Analysis and comparison of Weibull parameters for wind energy potential using different estimation methods: A case study of Isparta province in Turkey. Electric Power Components and Systems; 51: 1829-1845. https://doi.org/10.1080/15325008.2023.2210574.
22. [22]. Kaplan, YA. 2021. Comparison of the performance of the methods used to find Weibull parameters at different heights. Arabian Journal for Science and Engineering; 46: 12145-12153. https://doi.org/10.1007/s13369-021-05866-3.
23. [23]. Onay, AE, Dokur, E, Kurban, M. 2021. Performance comparison of new generation parameter estimation methods for Weibull distribution to compute wind energy density. Elektronika ir Elektrotechnika; 27(5): 41-48. https://doi.org/10.5755/j02.eie.28919.
24. [24]. Bilir, L, Imir, M, Devrim, Y, Albostan, A. 2015. Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function. International Journal of Hydrogen Energy; 40: 15301-15310. https://doi.org/10.1016/j.ijhydene.2015.04.140.
25. [25]. İlkiliç, C. 2012. Wind energy and assessment of wind energy potential in Turkey. Renewable and Sustainable Energy Reviews; 16: 1165-1173. https://doi.org/10.1016/j.rser.2011.11.021.
26. [26]. Kılıç, B. 2019. Determination of wind dissipation maps and wind energy potential in Burdur province of Turkey using geographic information system (GIS). Sustainable Energy Technologies and Assessments; 36: 100555. https://doi.org/10.1016/j.seta.2019.100555.
27. [27]. Yaniktepe, B, Koroglu, T, Savrun, MM. 2013. Investigation of wind characteristics and wind energy potential in Osmaniye, Turkey. Renewable and Sustainable Energy Reviews; 21: 703-711. https://doi.org/10.1016/j.rser.2013.01.005.
28. [28]. Ağbulut, Ü. 2022. A novel stochastic model for very short-term wind speed forecasting in the determination of wind energy potential of a region: A case study from Turkey. Sustainable Energy Technologies and Assessments; 51: 101853. https://doi.org/10.1016/j.seta.2021.101853.
29. [29]. Rüstemli, S, Güntaş, O, Şahin, G, Koç, A, van Sark, W, Doğan, SŞ. 2024. Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey. Energy Strategy Reviews; 56: 101552. https://doi.org/10.1016/j.esr.2024.101552.
30. [30]. Özkan Aksu, E, Temel Gencer, C. 2025. GIS-based optimum location selection for offshore wind energy: A case study for Turkey. Ocean Engineering; 320: 120292. https://doi.org/10.1016/j.oceaneng.2025.120292.
31. [31]. Aktekin, M, Genç, MS, Azgın, ST, Genç, G. 2024. Assessment of techno-economic analyzes of grid-connected nuclear and PV/wind/battery/hydrogen renewable hybrid system for sustainable and clean energy production in Mersin-Türkiye. Process Safety and Environmental Protection; 190: 340-353. https://doi.org/10.1016/j.psep.2024.07.032.
32. [32]. Insel, MA, Sadıkoglu, H, Melikoglu, M. 2022. Assessment and determination of 2030 onshore wind and solar PV energy targets of Türkiye considering several investment and cost scenarios. Results in Engineering; 16: 100733. https://doi.org/10.1016/j.rineng.2022.100733.
33. [33]. Gönül, Ö, Duman, AC, Deveci, K, Güler, Ö. 2021. An assessment of wind energy status, incentive mechanisms and market in Turkey. Engineering Science and Technology, an International Journal; 24: 1383-1395. https://doi.org/10.1016/j.jestch.2021.03.016.
34. [34]. Tagliapietra, S, Zachmann, G, Fredriksson, G. 2019. Estimating the cost of capital for wind energy investments in Turkey. Energy Policy; 131: 295-301. https://doi.org/10.1016/j.enpol.2019.05.011.
35. [35]. Dayi, F, Yucel, M, Demirkol, Z, Cilesiz, A. 2024. Management of sustainable investments: A comprehensive financial evaluation of wind energy facilities in Kastamonu. Energy for Sustainable Development; 81: 101501. https://doi.org/10.1016/j.esd.2024.101501.
36. [36]. Deng, X, Lv, T. 2020. Power system planning with increasing variable renewable energy: A review of optimization models. Journal of Cleaner Production; 246: 118962. https://doi.org/10.1016/j.jclepro.2019.118962.
37. [37]. T.C. Enerji ve Tabii Kaynaklar Bakanlığı (ETKB), Enerji İşleri Genel Müdürlüğü (EİGM). 2026. Available online: https://enerji.gov.tr/eigm-yenilenebilir-enerji-kaynaklar-ruzgar (accessed at 15.02.2026).
38. [38]. Stehly, T, Duffy, P. 2021. 2020 Cost of Wind Energy Review; National Renewable Energy Laboratory (NREL): Golden, CO, USA; NREL/TP-5000-81209. Available online: https://www.nrel.gov/docs/fy22osti/81209.pdf (accessed at 15.02.2026).
39. [39]. Artigao, E, Martín-Martínez, S, Honrubia-Escribano, A, Gómez-Lázaro, E. 2018. Wind turbine reliability: A comprehensive review towards effective condition monitoring development. Applied Energy; 228: 1569-1583. https://doi.org/10.1016/j.apenergy.2018.07.037.
40. [40]. T.C. Enerji ve Tabii Kaynaklar Bakanlığı (ETKB). 2026. Yenilenebilir Enerji Potansiyeli Atlası (REPA). Available online: https://repa.enerji.gov.tr/REPA/ (accessed at 15.02.2026).
41. [41]. Türkiye Elektrik İletim A.Ş. (TEİAŞ). 2026. Tarifeler. Available online: https://www.teias.gov.tr/tarifeler (accessed at 15.02.2026).
42. [42]. Enerji Piyasası Düzenleme Kurumu (EPDK). 2026. Yenilenebilir Enerji Kaynakları Destekleme Mekanizması (YEKDEM). Available online: https://www.epdk.gov.tr/Detay/Icerik/3-0-0-122/yenilenebilir-enerji-kaynaklari-destekleme-mekanizmasi-yekdem (accessed at 15.02.2026).
43. [43]. Mishnaevsky Jr., L., Branner, K., Petersen, H.N., Beauson, J., McGugan, M., Sørensen, B.F. 2017. Materials for wind turbine blades: An overview. Materials; 10(11): 1285. https://doi.org/10.3390/ma10111285.
44. [44]. Oxera. 2011. Discount rates for low-carbon and renewable generation technologies. Prepared for the Committee on Climate Change. April 2011. https://www.oxera.com/wp-content/uploads/2018/03/Oxera-report-on-low-carbon-discount-rates.pdf.
45. [45]. Yanbuawi, S.M., Imam, A.A., Alhussainy, A.A., Alghamdi, S., Rawa, M. 2024. Optimization and sensitivity analysis of using renewable energy resources for Yanbu City. Sustainability; 16(23): 10487. https://doi.org/10.3390/su162310487.
46. [46]. Nallolla, C.A., Perumal, V. 2022. Optimal design of a hybrid off-grid renewable energy system using techno-economic and sensitivity analysis for a rural remote location. Sustainability; 14(22): 15393. https://doi.org/10.3390/su142215393.