RÜZGAR SANTRALLERİNİN YERLEŞİM PLANLAMASINI ETKİLEYEN FAKTÖRLERİN BELİRLENMESİ: DEMATEL YÖNTEMİ İLE BİR ANALİZ

Öz

Rüzgar enerjisinden elektrik üretmek, şimdi en ucuz yenilenebilir enerji türleri arasındadır ve yeni kömür ve nükleer enerji santrallerinden üretilenlere kıyasla daha ucuzdur. Rüzgar enerjisi yatırımları arttıkça kapasite gittikçe artmaya devam etmektedir. Gelecek için rüzgar enerjisi sayılarının şaşırtıcı olması beklenmektedir. Bu makale, rüzgar enerjisi projeleri alanındaki planlamacılara ve yöneticilere önemli destek verebilecek rüzgar santrallerinin kurulum lokasyonunun belirlenebilmesi adına önemli olan faktörleri tanımlamak için bir model sunmaktadır. Önerilen model çok kriterli bir karar verme yöntemi olan DEMATEL yaklaşımına dayanmaktadır. DEMATEL yönteminin uygulanması sonucunda rüzgar yönü koşulunun bölgedeki rüzgar santrallerine yer belirlemek için en önemli faktör olduğu belirlenmiştir. Bu nedenle, rüzgar santralleri için potansiyel uygun alanların başarılı bir şekilde tanımlanması amacıyla rüzgar yönü koşullarının ayrıntılı bir şekilde analiz edilmesi ve meteorologlarla iletişim halinde kalması önerilmektedir.

Anahtar Kelimeler

• Rüzgar Enerjisi,Yer Seçimi,DEMATEL,Yenilenebilir Enerji,Yatırımlar

DETERMINING FACTORS THAT AFFECT LOCATION PLANNING OF WIND FARMS: AN ANALYSIS WITH DEMATEL METHOD

Öz

Generating electricity from wind energy is now amongst the cheapest forms of renewable kind and less expensive than that produced by new coal and nuclear power plants. As wind energy investments have increased, capacity has continued to grow more and more. The numbers for wind power for the future are expected to be astounding. This paper presents a model in order to identify factors that are significant for the wind farms installment which can have important support for the planners and the managers in the area of wind energy projects. The suggested model is based on a multi-criteria decision-making method, which is DEMATEL. The implementation of DEMATEL method has shown that wind direction condition is the most important factor for the wind farm in the area. Therefore, analyzing wind direction conditions in detail and keeping in touch with meteorologists in building process are suggested in order to successfully identify the potential suitable sites for the wind farms.

Anahtar Kelimeler

• Wind Energy,Location Selection,DEMATEL,Renewable Energy,Investments

Kaynakça

1. Alayi, R., Kasaeian, A., Najafi, A., & Jamali, E. (2019). Optimization and evaluation of a wind, solar and fuel cell hybrid system in supplying electricity to a remote district in national grid. International Journal of Energy Sector Management.
2. Bagavathsingh, A., Srinivas, C. V., Maran, P. S., Baskaran, R., & Venkatraman, B. (2016). Wind Direction Dependent Vertical Wind Shear and Surface Roughness Parameterization in Two different Coastal Environments. IJGAES, 4(3), 1-9.
3. Bashir, N., Modu, B., & Harcourt, P. (2018). Techo-Economic Analysis of Off-grid Renewable Energy Systems for Rural Electrification in North-eastern Nigeria. International Journal of Renewable Energy Research, 8(3), 1217-1228.
4. Bennaceur, F., Merzouk, N. K., Merzouk, M., & Hadji, A. (2019). Technical and economic viability of a wind farm installed in a windy area of Algerian western south region. Euro-Mediterranean Journal for Environmental Integration, 4(1), 7.
5. Bentouba, S., & Bourouis, M. (2016). Feasibility study of a wind–photovoltaic hybrid power generation system for a remote area in the extreme south of Algeria. Applied Thermal Engineering, 99, 713-719.
6. Bucksteeg, M. (2019). Modelling the impact of geographical diversification of wind turbines on the required firm capacity in Germany. Applied Energy, 235, 1476-1491.
7. Devrim, Y., & Bilir, L. (2016). Performance investigation of a wind turbine–solar photovoltaic panels–fuel cell hybrid system installed at İncek region–Ankara, Turkey. Energy Conversion and Management, 126, 759-766.
8. Dinçer, H., & Yüksel, S. (2019). Multidimensional evaluation of global investments on the renewable energy with the integrated fuzzy decision‐making model under the hesitancy. International Journal of Energy Research, 43(5), 1775-1784.

9. Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2017). Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment. Journal of Air Transport Management, 63, 17-33.
10. Dinçer, H., Yüksel, S., & Martinez, L. (2019). Balanced scorecard-based Analysis about European Energy Investment Policies: A hybrid hesitant fuzzy decision-making approach with Quality Function Deployment. Expert Systems with Applications, 115, 152-171.
11. Duman, A. C., & Güler, Ö. (2018). Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households. Sustainable Cities and Society, 42, 107-126.
12. Feilat, E. A., Azzam, S., & Al-Salaymeh, A. (2018). Impact of large PV and wind power plants on voltage and frequency stability of Jordan’s national grid. Sustainable cities and society, 36, 257-271.
13. Gao, C. K., Na, H. M., Song, K. H., Dyer, N., Tian, F., Xu, Q. J., & Xing, Y. H. (2019). Environmental impact analysis of power generation from biomass and wind farms in different locations. Renewable and Sustainable Energy Reviews, 102, 307-317.
14. Han, Y., & Deng, Y. (2018). An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors. Soft computing, 22(15), 5073-5090.
15. Hart, E., Turnbull, A., Feuchtwang, J., McMillan, D., Golysheva, E., & Elliott, R. (2019). Wind turbine main‐bearing loading and wind field characteristics. Wind Energy, 22(11), 1534-1547.
16. Izquierdo, J., Márquez, A. C., Uribetxebarria, J., & Erguido, A. (2020). On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects. Renewable Energy, 153, 1100-1110.
17. Jahangiri, M., Haghani, A., Shamsabadi, A. A., Mostafaeipour, A., & Pomares, L. M. (2019). Feasibility study on the provision of electricity and hydrogen for domestic purposes in the south of Iran using grid-connected renewable energy plants. Energy Strategy Reviews, 23, 23-32.
18. Kumar, Y., Ringenberg, J., Depuru, S. S., Devabhaktuni, V. K., Lee, J. W., Nikolaidis, E., ... & Afjeh, A. (2016). Wind energy: Trends and enabling technologies. Renewable and Sustainable Energy Reviews, 53, 209-224.
19. Li, Y., Wu, X. P., Li, Q. S., & Tee, K. F. (2018). Assessment of onshore wind energy potential under different geographical climate conditions in China. Energy, 152, 498-511.
20. Maeda, T., & Kamada, Y. (2019). Study Effect of Extreme Wind Direction Change on 3-Bladed Horizontal Axis Wind Turbine. International Journal of Renewable Energy Development, 8(3).
21. Milanese, M., Tornese, L., Colangelo, G., Laforgia, D., & de Risi, A. (2017). Numerical method for wind energy analysis applied to Apulia Region, Italy. Energy, 128, 1-10.
22. Nematollahi, O., Hoghooghi, H., Rasti, M., & Sedaghat, A. (2016). Energy demands and renewable energy resources in the Middle East. Renewable and Sustainable Energy Reviews, 54, 1172-1181.
23. Pagnini, L. C., Burlando, M., & Repetto, M. P. (2015). Experimental power curve of small-size wind turbines in turbulent urban environment. Applied Energy, 154, 112-121.
24. Poozesh, P., Baqersad, J., Niezrecki, C., Avitabile, P., Harvey, E., & Yarala, R. (2017). Large-area photogrammetry based testing of wind turbine blades. Mechanical Systems and Signal Processing, 86, 98-115.
25. Qiu, D., Dinçer, H., Yüksel, S., & Ubay, G. G. (2020). Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets. Energies, 13(6), 1423.
26. Rehman, S., Alam, M., Alhems, L. M., & Rafique, M. M. (2018). Horizontal axis wind turbine blade design methodologies for efficiency enhancement—A review. Energies, 11(3), 506.
27. Rubert, T., Perry, M., Fusiek, G., McAlorum, J., Niewczas, P., Brotherston, A., & McCallum, D. (2018). Field demonstration of real-time wind turbine foundation strain monitoring. Sensors, 18(1), 97.
28. Wu, G., Zhang, C., Cai, C., Yang, K., & Shi, K. (2020). Uncertainty prediction on the angle of attack of wind turbine blades based on the field measurements. Energy, 117515.
29. Yu, Z., Liu, W., Chen, L., Eti, S., Dinçer, H., & Yüksel, S. (2019). The effects of electricity production on industrial development and sustainable economic growth: A VAR analysis for BRICS countries. Sustainability, 11(21), 5895.
30. Yüksel, S. (2017). The impacts of research and development expenses on export and economic growth. International Business and Accounting Research Journal, 1(1), 1-8
31. Yüksel, S., & Ubay, G. G. (2020). Identifying The Influencing Factors of Renewable Energy Consumption in Turkey With MARS Methodology. Ekonomi İşletme ve Maliye Araştırmaları Dergisi, 2(1), 1-14.
32. Yüksel, S., Dinçer, H., & Meral, Y. (2019). Financial analysis of international energy trade: a strategic outlook for EU-15. Energies, 12(3), 431.
33. Zhang, W., & Deng, Y. (2019). Combining conflicting evidence using the DEMATEL method. Soft computing, 23(17), 8207-8216.