Türkiye Karasal Rüzgar Enerji Santrallerin Maliyet Analizi: Adilcevaz Bölgesi Vaka Çalışması

Yıl 2023, Cilt: 25 Sayı: 75, 539 - 550, 27.09.2023

Soner Çelikdemir Mahmut Temel Özdemir

https://doi.org/10.21205/deufmd.2023257502

Öz

Elektrik enerjisi kullanımının artması yeni yatırımları da gerektirmektedir. Bu nedenle bir çok farklı yeni elektrik enerjisi üretim sistemi yatırımlarının yapılması kaçınılmazdır. Bu bağlamda gerçekçi bir maliyet tahminine sahip olmak yatırımcılara birçok avantaj sağlamaktadır. Bu avantajı kullanmak için bu çalışmada RES’lerin gerçekçi yatırım maliyetlerinin belirlenmesine odaklanılmıştır. Çalışmada, Türkiye'deki RES yatırım maliyetleri analiz edilerek yeni RES yatırım maliyetlerinin tahmini için ilk kez bir denklem modeli önerilmiştir. Önerilen denklem modelinde parametreleri belirlemek için Kaos Gömülü Adaptif Parçacık Sürü Optimizasyonu tercih edilmiştir. Önerilen denklem modelindeki parametrelerin hesaplanması için MATLAB programı kullanılmıştır. Yapılan analiz sonucunda ortalama hata %6,37 olarak hesaplanmıştır. Farklı türbin türlerindeki RES’ler ile önerilen denklem modelinin doğruluğu test edilmiştir. Test sonucunda önerilen denklem modelinin geçerliliği gösterilmiştir. Yapılan analiz sonucunda ortalama hata % 6,77 olarak hesaplanmıştır. Ayrıca çalışmada duyarlılık analizi yapılmış ve farklı parametre değerlerindeki maliyet değişimleri ifade edilmiştir. Duyarlılık analizine göre maliyet üzerinde kurulu güç değerindeki değişimin, rotor çapı ve kule yüksekliğindeki değişime göre daha baskın olduğu görülmektedir. Son olarak Adilcevaz bölgesinde bir vaka çalışması yapılmıştır. Belirlenen bölgede kurulacak rüzgar enerji çiftliği için 4 farklı türbin markasına ait 14 farklı türbin modeli için maliyetler hesaplanmıştır. Bu hesaplamalar sonucunda birim maliyet olarak uygun türbin modeli Nordex N117-3/3 olarak belirlenmiştir.

Anahtar Kelimeler

Rüzgar Enerjisi Potansiyeli, Kaos Gömülü Adaptif Parçacık Sürü Optimizasyonu, Maliyet Analizi, Duyarlılık Analizi

Cost Analysis of Türkiye Onshore Wind Power Plants: Adilcevaz Region Case Study

Öz

The increase in the use of electrical energy also requires new investments. For this reason, it is inevitable to make many different new electrical energy generation system investments. In this context, having a realistic cost estimation provides many advantages to investors. In order to use this advantage, this study focuses on determining the realistic investment costs of WPP. In the study, an equation model is proposed for the first time for the estimation of new WPP investment costs by analyzing WPP investment costs in Turkey. Chaos Embedded Adaptive Particle Swarm Optimization was preferred to determine the parameters in the proposed equation model. The MATLAB program was used to calculate the parameters in the proposed equation model. As a result of the analysis, the average error was calculated as 6.37%. The accuracy of the proposed equation model was tested with WPPs of different turbine types. As a result of the test, the validity of the proposed equation model is shown. As a result of the analysis, the average error was calculated as 6.77%. In addition, sensitivity analysis was performed in the study and cost changes in different parameter values were expressed. According to the sensitivity analysis, it is seen that the change in the installed power value on the cost is more dominant than the change in rotor diameter and tower height. Finally, a case study was conducted in the Adilcevaz region. Costs were calculated for 14 different turbine models belonging to 4 different turbine manufacturers for the wind energy farm to be established in the determined region. As a result of these calculations, the turbine model suitable for unit cost was determined as Nordex N117-3/3.

Anahtar Kelimeler

Estimation of Wind Power Plant Energy Potential, Chaos Embedded Adaptive Particle Swarm Optimization, Cost Analysis, Sensitivity Analysis

Kaynakça

• [1] Cali U, Erdogan N, Kucuksari S, Argin M. Techno-economic analysis of high potential offshore wind farm locations in Turkey. Energy Strateg Rev 2018;22:325–36. DOI: 10.1016/j.esr.2018.10.007.
• [2] Argin M, Yerci V, Erdogan N, Kucuksari S, Cali U. Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection. Energy Strateg Rev 2019;23:33–46. DOI: 10.1016/j.esr.2018.12.005.
• [3] Satir M, Murphy F, McDonnell K. Feasibility study of an offshore wind farm in the Aegean Sea, Turkey. Renew Sustain Energy Rev 2018;81:2552–62. DOI: 10.1016/j.rser.2017.06.063.
• [4] Çelikdemir S, Özdemir MT. Adilcevaz Bölgesinde Rüzgar Enerji Potansiyelinin İncelenmesi. Bitlis Eren Üniversitesi Fen Bilim Derg 2020. DOI: 10.17798/bitlisfen.526670.
• [5] Souloukngaa MH, Coban HH. Determination of Feasibility Analysis of Wind Turbines Using Weibull Parameter for Chad. J Smart Sci Technol 2022;2:1–15. DOI: 10.24191/jsst.v2i2.33.
• [6] Khanh PQ, Duong TL, Khoa HQ, Truong V-A. Determination of Profitable Wind Farm Generating Capacity Based on Weibull Distribution of Wind Speed in the Competitive Electricity Market, 2023, p. 389–400. DOI: 10.1007/978-3-031-19694-2_35.
• [7] Ali B, Abbas G, Memon A, Mirsaeidi S, Koondhar MA, Chandio S, et al. A comparative study to analyze wind potential of different wind corridors. Energy Reports 2023;9:1157–70. DOI: 10.1016/j.egyr.2022.12.048.
• [8] Yang Z, Huang W, Dong S, Li H. Mixture bivariate distribution of wind speed and air density for wind energy assessment. Energy Convers Manag 2023;276:116540. DOI: 10.1016/j.enconman.2022.116540.
• [9] Singh R, Kumar S, Gautam B. Assessment of wind power at various height using Weibull parameters at four selected locations. Int J Energy a Clean Environ 2022. DOI: 10.1615/InterJEnerCleanEnv.2022038082.
• [10] Mytilinou V, Kolios AJ. Techno-economic optimisation of offshore wind farms based on life cycle cost analysis on the UK. Renew Energy 2019;132:439–54. DOI: 10.1016/j.renene.2018.07.146.
• [11] Ioannou A, Angus A, Brennan F. Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters. Energy Sources, Part B Econ Planning, Policy 2018;13:281–90. DOI: 10.1080/15567249.2018.1461150.
• [12] Shafiee M, Brennan F, Espinosa IA. A parametric whole life cost model for offshore wind farms. Int J Life Cycle Assess 2016;21:961–75. DOI: 10.1007/s11367-016-1075-z.
• [13] Dicorato M, Forte G, Pisani M, Trovato M. Guidelines for assessment of investment cost for offshore wind generation. Renew Energy 2011;36:2043–51. DOI: 10.1016/j.renene.2011.01.003.
• [14] Çelikdemir S, Özdemir MT. Turkey’s Offshore Hybrid Energy Potential and Techno-Economic Analysis in the Eastern Mediterranean. In: Dincer İ;, Midilli A, Timurkutluk B, Çelik S, editors. 5 th Int. Hydrog. Technol. Congr., Niğde: n.d., p. 140–2.
• [15] Çelikdemir S. and Özdemir MT. Techno-Economic Analysis of Onshore and Offshore Wind Power Plant. TÜBA World Conf Energy Sci Technol 2021.
• [16] Çelikdemir S. and Özdemir MT. A New Alternative Solution for Wind Power Plants. Int Conf Ser Altern Fuels, Energy Environ Futur Challenges 2021.
• [17] Çelikdemir S, Özdemir MT. A new approach in the cost estimation of a hydroelectric power plants in Türkiye based on geographical features. Int J Energy Res 2022;46:20858–72. DOI: 10.1002/er.8384.
• [18] Ioannou A, Angus A, Brennan F. A lifecycle techno-economic model of offshore wind energy for different entry and exit instances. Appl Energy 2018;221:406–24. DOI: 10.1016/j.apenergy.2018.03.143.
• [19] Kim J-Y, Oh K-Y, Kang K-S, Lee J-S. Site selection of offshore wind farms around the Korean Peninsula through economic evaluation. Renew Energy 2013;54:189–95. DOI: 10.1016/j.renene.2012.08.026.
• [20] Spyridonidou S, Vagiona DG, Loukogeorgaki E. Strategic planning of offshore wind farms in Greece. Sustain 2020. DOI: 10.3390/su12030905.
• [21] Deveci M, Ozcan E, John R. Offshore Wind Farms: A Fuzzy Approach to Site Selection in a Black Sea Region. 2020 IEEE Texas Power Energy Conf., IEEE; 2020, p. 1–6. DOI: 10.1109/TPEC48276.2020.9042530.
• [22] Caceoğlu E, Yildiz HK, Oğuz E, Huvaj N, Guerrero JM. Offshore wind power plant site selection using Analytical Hierarchy Process for Northwest Turkey. Ocean Eng 2022;252:111178. DOI: 10.1016/j.oceaneng.2022.111178.
• [23] Ozerdem B, Ozer S, Tosun M. Feasibility study of wind farms: A case study for Izmir, Turkey. J Wind Eng Ind Aerodyn 2006. DOI: 10.1016/j.jweia.2006.02.004.
• [24] Hong L, Möller B. Offshore wind energy potential in China: Under technical, spatial and economic constraints. Energy 2011. DOI: 10.1016/j.energy.2011.03.071.
• [25] Alatas B, Akin E, Ozer AB. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 2009;40:1715–34. DOI: 10.1016/j.chaos.2007.09.063.
• [26] Koç E, Şenel MC. 2013. Dünyada ve Turkiye’de Enerji Durumu - Genel Değerlendirme. Mühendis ve Makina. Cilt 54, s. 32-44.
• [27] Atilgan I. Türkiye’nin enerji potansiyeline bakiş. J Fac Eng Archit Gazi Univ 2000;15:31–47. DOI: 10.17341/gummfd.57526.
• [28] Anonim. 2022. https://www.teias.gov.tr/tr-TR/kurulu-guc-raporlari. (Erişim Tarihi: 11.10.2022).
• [29] Elbatran AH, Yaakob OB, Ahmed YM, Shabara HM. Operation, performance and economic analysis of low head micro-hydropower turbines for rural and remote areas: A review. Renew Sustain Energy Rev 2015;43:40–50. DOI: 10.1016/j.rser.2014.11.045.
• [30] Özdemir MT. Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization. Int J Hydrogen Energy 2021;46:16465–80. DOI: 10.1016/j.ijhydene. 2020.12.203.
• [31] Özdemir MT. A novel optimum PI controller design based on stability boundary locus supported particle swarm optimization in AVR system. Turk J Elec Eng Comp Sci 2021;29:291–309. DOI: 10.3906/elk-1910-81.
• [32] Yıldız S, Gunduz H, Yildirim B, Özdemir MT. An islanded microgrid energy system with an innovative frequency controller integrating hydrogen-fuel cell. Fuel 2022;326:125005. DOI: 10.1016/j.fuel.2022. 125005.
• [33] Daghan IH, Gencoglu MT, Ozdemir MT. Chaos Embedded Particle Swarm Optimization Technique for Solving Optimal Power Flow Problem. 18th IEEE Int. Multi-Conference Syst. Signals Devices, SSD 2021, 2021. DOI: 10.1109/SSD52085.2021.9429520.
• [34] Bilgili M, Yasar A, Simsek E. Offshore wind power development in Europe and its comparison with onshore counterpart. Renew Sustain Energy Rev 2011;15:905–15. DOI: 10.1016/j.rser.2010.11.006.
• [35] The Ministry of Environment urbanisation and climate change. E-ced report. 2022. http://eced.csb.gov. tr/ced/jsp/portal/main2.htm. (Erişim Tarihi: 25.05.2022).
• [36] Yıldırım H. 2019. Turkish Wind Energy Association. https://tureb.com.tr//lib/uploads/4e77501b714739a9.pdf. (Erişim Tarihi: 01.01.2023)