Zekeriya KONURHAN, Melih YÜCESAN, Muhammet GÜL

36536

Açık deniz rüzgar enerjisi santrali saha seçimi için entegre bir Bayesian En İyi-En Kötü Yöntemi ve CBS tabanlı yaklaşım: Kuzey Ege ve Marmara Denizi’nde (Türkiye) bir vaka çalışması

Günümüz dünyasında fosil yakıtların çevreye olan olumsuz etkilerinden dolayı yenilenebilir enerji kaynakları büyük talep görmektedir. Rüzgâr santralleri, fosil yakıt tüketimine alternatif önemli bir yenilenebilir enerji kaynağıdır. Kıyı bölgelerinde ve denizlerde kurulan offshore rüzgâr santralleri dünyanın birçok yerinde etkin bir şekilde kullanılmaktadır. Rüzgâr santralleri dikkate alındığında özellikle Türkiye’nin Kuzeybatı bölgesi ve Ege kıyıları önemli bir potansiyel oluşturmaktadır. Bu çalışmanın amacı, Bayesian Best-Worst yöntemini (BWM) CBS’ye entegre ederek Türkiye’nin Marmara Denizi ve Kuzey Ege Kıyılarında açık deniz rüzgâr santralleri için uygun yer seçimini belirlemektir. Bayesian BWM, birden çok uzmanın tercihlerini etkili bir şekilde entegre ederek orijinal BWM’yi optimize eder. Çalışmada BWM modeli kullanılarak “teknik”, “sosyo-ekonomik”, “çevre” ve “konum” olmak üzere dört ana kriter altında 17 kriter belirlenmiş, kriterleri içeren anketler uzmanlar tarafından doldurulmuş ve son ağırlıkları verilmiştir. Bayesian-BWM modeli kullanılarak bulunan kriter ağırlıkları CBS’ye entegre edilmiş ve açık deniz rüzgâr çiftliği için uygun yerler bulunmuştur. Buna göre, Kuzey Ege kıyılarındaki Aliağa, Bozcaada ve Gökçeada açıklarındaki çalışma alanı ile Marmara Denizi’nin kısmen güneyi ve Kapıdağ Yarımadası çevresi rüzgâr santrali için uygun alanlar olarak önerilmektedir.
Anahtar Kelimeler:

Offshore Rüzgar Enerjisi, Yer Seçimi, Bayesyen BWM, CBS.

An integrated Bayesian Best-Worst Method and GIS-based approach for offshore wind power plant site selection: A case study in North Aegean and Marmara Sea (Türkiye)

In today’s world, renewable energy sources are in great demand due to the negative effects of fossil fuels on the environment. Wind power plants are an important renewable energy source alternative to fossil fuel consumption. Offshore wind farms established in coastal areas and seas are used effectively in many parts of the world. The wind power plants, especially in the Northwest region of Turkey and the Aegean coasts, constitute an important potential. This study selects suitable sites for offshore wind farms in the Marmara Sea and North Aegean Coasts of Turkey by integrating the Bayesian Best-Worst method (BWM) and GIS. Bayesian BWM improves the traditional BWM integrating the preferences of multiple experts. In the study, 17 sub-criteria were determined under four main criteria of “technical”, “socio-economic”, “environment,” and “location”. Experts’ judgments through the filled enabled the criterion weights to be obtained. The criteria weights found using the Bayesian-BWM model were integrated into the GIS, and suitable locations for the offshore wind farm were determined. Accordingly, the study area off the coasts of Aliağa, Bozcaada, and Gökçeada on the North Aegean coast, and the part south of the Marmara Sea and the area around Kapıdağ Peninsula are suggested as suitable areas for wind power plants.
Keywords:

Offshore wind energy, Site selection, Bayesian BWM, GIS,

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  • A. Fetanat, A., & Khorasaninejad, E. (2015). A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. Ocean & Coastal Management, 109, 17-28. https://doi. org/10.1016/j.ocecoaman.2015.02.005
  • Adumene, S., Okwu, M., Yazdi, M., Afenyo, M., Islam, R., Orji, C.U., Obeng, F., Goerlandt, F., (2021). Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters. Maritime Transport Research, 2, 1-20. https://doi.org/10.1016/j. martra.2021.100039
  • Akalın, S., (2018). Açık Deniz Rüzgâr Enerjisi Türbinlerinin Kurulum Yeri Seçimi İçin Bir Model Önerisi [Yüksek Lisans Tezi] Gazi Universitesi. Akyüz, L. (Ed.) (2007). Bilimsel açıdan Marmara Denizi. Türkiye Barolar Birliği
  • Atalay, İ. (1982). Türkiye jeomorfolojisine giriş. Ege University Social Sciences University Publications.
  • Aydin NY., Kentel E. & Sebnem Duzgun H. (2013). GIS-based site selection methodology for hybrid renewable energy systems: a case study from western. Energy Conversion and Management, 70, 90–106. https://doi.org/10.1016/j.enconman.2013.02.004
  • Ayodele, T. R., & Ogunjuyigbe, A. S. O. (2016). Wind energy potential of Vesleskarvet and the feasibility of meeting the South African ׳ s SANAE IV energy demand. Renewable and Sustainable Energy Reviews, 56, 226-234. https://doi.org/10.1016/j. rser.2015.11.053
  • Ayodele, T.R., Ogunjuyigbe, A.S.O., Odigie, O. & Munda, J.L. (2018). A multicriteria GIS-based model for wind farm site selection using interval type-2. fuzzy analytic hierarchy process: The case study of Nigeria, Applied Energy, 228, 1853-1869. https://doi. org/10.1016/j.apenergy.2018.07.051
  • Barka, A.A., & Kadinsky-Cade, K. (1988). Strike-slip fault geometry in Turkey and its influence on earthquake activity. Tectonics 7, 663–684. https://doi.org/10.1029/TC007i003p00663
  • Bhandari, J., Abbassi, R., Garaniya, V., & Khan, F. (2015). Risk analysis of deepwater drilling operations using Bayesian network. Journal of Loss Prevention in the Process Industries. 38, 11–23. https://doi.org/10.1016/J.JLP.2015.08.004
  • Bhandari, J., Arzaghi, E., Abbassi, R., Garaniya, V., & Khan, F., (2016). Dynamic risk-based maintenance for offshore processing facility. Process Saf. Progress. 35(4), 399–406. https://doi.org/10.1002/ prs.11829
  • BirdLife International BirdLife International (2022 November 30). World database of key biodiversity areas. https://www.birdlife. org/worldwide/programmes/sites-habitats-ibasandkbas Boru Hatları İle Petrol Taşıma Anonim Şirketi (2022 November 30). BOTAŞ, https://www.botas.gov.tr/
  • Brans, J. P., Mareschal, B., & Vincke, P. (1984). In J. Brans (Ed.), Operational research, PROMETHEE: A new family of outranking methods in multicriteria analysis. Operational Research, 3, 477–490.
  • Caceoğlu, R., Yildiz, Kübra, H., Oğuz, E., Huvaj, N., and Josep, Guerrero, M. (2022). Offshore wind power plant site selection using Analytical Hierarchy Process for Northwest Turkey, Ocean Engineering, 252, 111178. https://doi.org/10.1016/j. oceaneng.2022.111178
  • Carlo Bien Salvador, Bien, C., Arzaghi, E., Yazdi, M., Jahromi, A.F, H., & Abbassi, R. (2022). A multicriteria decision-making framework for site selection of offshore wind farms in Australia, Ocean & Coastal Management, 224, 106196, https://doi.org/10.1016/j. ocecoaman.2022.106196
  • Carriger, J.F., Yee, S.H., Fisher, W.S., (2019). An introduction to Bayesian networks as assessment and decision support tools for managing coral reef ecosystem services. Ocean & Coastal Management. 177, 188–199. https://doi.org/10.1016/j.ocecoaman. 2019.05.008
  • Castro-Santos, L., Lamas-Galdo, M. I., & Filgueira-Vizoso, A. (2020). Managing the oceans: Site selection of a floating offshore wind farm based on GIS spatial analysis. Marine Policy, 113, 103803. https://doi.org/10.1016/j.marpol.2019.103803
  • Deveci, M., Ozcan, E. & John, R., (2020). Offshore wind farms: a fuzzy approach to site selection in a black sea region. IEEE Texas Power and Energy Conference (TPEC), 10–16. https://doi.org/10.1109/ TPEC48276.2020.9042530
  • Deveci, M., Özcan, E., John, R., Pamucar, D., & Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, 107532. https://doi.org/10.1016/j.asoc.2021.107532
  • Díaz, H., & Soares, C. G. (2020). Review of the current status, technology and future trends of offshore wind farms. Ocean Engineering, 209, 107381. https://doi.org/10.1016/j.oceaneng.2020.107381
  • Fan, G., Zhang, N., Liang, Z., & Wang, J. (2016). Analysis on the ‘three norths’ region wind and PV power limitation. North China Electric Technol., 12, 55-59.
  • Gao, J., Guo, F., Ma, Z., Huang, X., & Li, X. (2020). Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators. Energy, 204, 117899. https://doi.org/10.1016/j.energy. 2020.117899
  • Genç, M.S., Karipoğlu, F., Koca, K., Azgın, S., T., (2021). Suitable site selection for offshore wind farms in Turkey’s seas: GIS-MCDM based approach. Earth Sci. India 14, 1213–1225. https://doi. org/10.1007/s12145-021-00632-3
  • Gil-García, C. Isabel, Ramos-Escudero, A., García-Cascales, M.S., Dagher, H. and Molina-García, A. (2022). Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The Gulf of Maine case. Renewable Energy, 183(81) 130-147. https://doi. org/10.1016/j.renene.2021.10.058
  • Global Wind Atlas. (2022). Global Wind Atlas (Version 3.0). https:// globalwindatlas.info
  • Gonz´alez, P.H., Clímaco, G., Mauri, G.R., Vieira, B.S., Ribeiro, G.M., Orrico Filho, R.D., Simonetti, L., Perim, L.R., & Hoffmann, I.C.S., (2019). New approaches for the traffic counting location problem. Expert Syst. Appl. 132, 189–198. https://doi.org/10.1016/j. eswa.2019.04.068
  • Gul, M., & Yucesan, M. (2022). Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model. Socio-Economic Planning Sciences, 80, 101173. https://doi. org/10.1016/j.seps.2021.101173
  • GWEC (2022 November 30). Global Wind Report 2019-2022, Council- GWEC (2022). Global Wind Energy Council, Brussels. https:// gwec.net/
  • Havan, C. (2017). Türkiye’de yenilenebilir enerji politikasi: TR42 doğu Marmara bölgesi örneği. [Unpublished Master Thesis]. Kocaeli University
  • Höfer, T., Sunak, Y., Siddique, H. & Madlener, R. (2016) Wind farm siting using a spatial analytic hierarchy process approach: a case study of the Städteregion Aachen. Applied Energy, 163, 222–43. https://doi.org/10.1016/j.apenergy.2015.10.138
  • Hwang, C., L. & Yoon, K. (1981). Attribute Decision Methods and Applications, Springer, Berlin Heidelberg,
  • Interagency Ocean Observation Committee. IOOC, (2022). https:// www.iooc.us/ (Accessed 30 November 2022)
  • Kim, C.-K., Jang, S. & Kim, T. Y. (2018). Site selection for offshore wind farms in the southwest coast of South Korea. Renewable Energy, 120, 151-162, https://doi.org/10.1016/j.renene.2017.12.081
  • Kim, H., O’Kelly, M.E., (2009). Reliable p-hub location problems in telecommunication networks. Geographical. Analysis. 41, 283– 306. https://doi.org/10.1111/j.1538-4632.2009.00755.x
  • Kim, T., Park, J. I., & Maeng, J. (2016). Offshore wind farm site selection study around Jeju Island, South Korea. Renewable energy, 94, 619-628. https://doi.org/10.1016/j.renene.2016.03.083
  • Kiziroglu, I. & Erdogan, A. (2015). Relations between ecosystem and wind energy. Fresenius Environmental Bulletin, 24(1A), 163-171.
  • Ladenburg, J. (2009). Visual impact assessment of offshore wind farms and prior experience. Applied Energy, 86, 380–387. https://doi.org/10.1016/j.apenergy.2008.05.005
  • Lessin, A.M., Lunday, B.J., Hill, R.R., (2018). A bilevel exposure-oriented sensor location problem for border security. Computers & Operations Research, 98, 56–68. https://doi.org/10.1016/j. cor.2018.05.017
  • Lilley, M.B., Firestone, J. & Kempton, W. (2010). The effect of wind power installations on coastal tourism. Energies, 3(1), 1–22. https://doi.org/10.3390/en3010001
  • Maden Tetkik ve Arama, MTA (2022). https://www.mta.gov.tr/ (Accessed 30 November 2022).
  • Marine Traffic. Marine Traffic: Global Ship Tracking Intelligence (2022). AIS Marine Traffic. https://www.marinetraffic.com/en/ ais/home/centerx:-12.0/centery:25.0/zoom:4 (Accessed 30 November 2022).
  • Markard, J., & Petersen, R. (2009). The offshore trend: Structural changes in the wind power sector. Energy Policy, 37(9), 3545- 3556.
  • Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
  • Moriguchi, S., Mukai, H., Komachi, R. & Sekijima, T. (2019). Wind farm effects on migratory flight of swans and foraging distribution at their stopover site. Wind Energy and Wildlife Impacts, 125-133. https://doi.org/10.1007/978-3-030-05520-2_8
  • Munim, Z. H., Balasubramaniyan, S., Kouhizadeh, M., & Hossain, N. U. I. (2022). Assessing blockchain technology adoption in the Norwegian oil and gas industry using Bayesian Best Worst Method. Journal of Industrial Information Integration, 28, 100346. https://doi.org/10.1016/j.jii.2022.100346
  • Murali RM, Vidya PJ, Modi P. & Kumar SJ. (2014). Site selection for offshore wind farms along the Indian coast. Indian Journal of Geo-Marine Sciences 43(7), 1401-1406.
  • Nedaei, M., Assareh, E., & Biglari, M. (2014). An extensive evaluation of wind resource using new methods and strategies for development and utilizing wind power in Mahshahr station in Iran. Energy Conversion and Management, 81, 475-503. https://doi. org/10.1016/j.enconman.2014.02.025
  • Nedjati, A., Izbirak, G., Arkat, J., (2017). Bi-objective covering tour location routing problem with replenishment at intermediate depots: formulation and metaheuristics. Computers & Industrial Engineering. 110, 191–206. https://doi.org/10.1016/j. cie.2017.06.004
  • OpenStreetMap, (2022). https://www.openstreetmap.org/#map= 11/39.6420/27.9403 (Accessed 30 November 2022).
  • Oran, M. (1994). Kuzey Ege Denizi su kütleleri ve dağiliş özellikleri [Unpublished doctoral dissertation]. University of Istanbul.
  • Özşahin, E., & Kaymaz, Ç. (2013). Rüzgâr Enerji Santrallerinin (Res) Yapım Yeri Seçimi Üzerine Bir Cbs Analizi: Hatay Örneği. Türk Bilim Research Foundation Science Journal, 6(2) 1-18.
  • Pantaleo, A., Pellerano, A., Ruggiero, F. & Trovato, M. (2005). Feasibility study of offshore wind farms: an application to Puglia region. Sol Energy, 79(3), 321–331. https://doi.org/10.1016/j. solener.2004.08.030
  • Presidency of Republic of Turkey, 2019. Türk Boğazları Trafik Düzeni Yönetmeliği, vol. 22. Republic of Turkey Official Gazette, Turkey. Pui, G., Bhandari, J., Arzaghi, E., Abbassi, R., Garaniya, V., (2017).
  • Risk-based maintenance of offshore managed pressure drilling (MPD) operation. Journal of Petroleum Science and Engineering, 159, 513–521. https://doi.org/10.1016/j.petrol.2017.09.066
  • Regional Earthquake-Tsunami Monitoring Center (2021 November 30). Earthquake information system (Turkish). http://www.koeri. boun.edu.tr/sismo/zeqdb/
  • Rezaei, J. (2015). Best-worst multicriteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega. 2014.11.009
  • Rezaei, J. (2016). Best-worst multicriteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
  • Roy, B. (1978). ELECTRE III: Un algorithme de classements fonde sur une representation floue des preference en presence de criteres multiples. Cahiers de CERO, 20, 3–24.
  • Saner, H. S., Yucesan, M., & Gul, M. (2022). A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters. Natural hazards, 111(2), 1603-1635. https:// doi.org/10.1007/s11069-021-05108-7
  • Satir, M., Murphy, F., & McDonnell, K. (2018). Feasibility study of an offshore wind farm in the Aegean Sea, Turkey. Renewable and Sustainable Energy Reviews, 81, 2552–2562. https://doi. org/10.1016/j.rser.2017.06.063 Shorabeh, S. N., Argany, M., Rabiei, J., Firozjaei, H. K., & Nematollahi, O. (2021). Potential assessment of multi-renewable energy farms establishment using spatial multicriteria decision analysis: A case study and mapping in Iran. Journal of Cleaner Production, 295, 126318. https://doi.org/10.1016/j.jclepro.2021.126318
  • Sivri, G. (2013). Marmara Denizi’nde sualti heyelan kaynaklı bir tsunami: Tuzla açiklari için bir senaryo [Unpublished Master Thesis]. Istanbul Technical University.
  • Siyal, S.H., Mörtberg, U., Mentis, D., Welsch, M., Babelon, I. & Howells, M. (2015). Wind energy assessment considering geographic and environmental restrictions in Sweden: a GIS-based approach, Energy 83, 447-461, https://doi.org/10.1016/j.energy. 2015.02.044
  • Snyder, B., & Kaiser, M. J. (2009). Ecological and economic cost-benefit analysis of offshore wind energy. Renewable Energy, 34(6), 1567-1578. https://doi.org/10.1016/j.renene.2008.11.015
  • Sullivan, R.G., Kirchler, L.B., Cothren, J. & Winters, S.L. (2013). Research article: offshore wind turbine visibility and visual impact threshold distances. Environmental Practice, 15, 33–49. https:// doi.org/10.1017/S1466046612000464
  • Taoufik, M. & Fekri, A. (2021). GIS-based multicriteria analysis of offshore wind farm development in Morocco, Energy Conversion and Management: X, 11, 100103, 2590-1745, https://doi. org/10.1016/j.ecmx.2021.100103
  • Tercan, E. (2021). Land suitability assessment for wind farms through best-worst meth-od and GIS in Balıkesir province of Turkey. Sustainable Energy Technologies and Assessments, 47, 101491. https://doi.org/10.1016/j.seta.2021.101491
  • The General Bathymetric Chart of the Oceans, GEBCO. (2022 November 30) https://www.gebco.net/
  • Tortumluoğlu, M.İ. & Doğan, M. (2021). Açık Deniz Rüzgar Türbinleri için Uygun Yer Seçim Kriterlerinin İrdelenmesi ve Kuzey Ege Kıyılarına Uygulanması. DEUFMD, 23(67), 25-41. Turkish Wind Energy Association (2022 December 30). https://www. tureb.com.tr/
  • Van-Haaren R. & Fthenakis V. (2011). GIS based wind farm site selection using spatial multi criteria analysis (SMCA): evaluating the case of New York State. Renew Sustain Energy Rev, 15:3332–40. https://doi.org/10.1016/j.rser.2011.04.010
  • Vasileiou M, Loukogeorgaki E, & Vgiona D (2017) Gıs-based multicriteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece. Renew Sustain Energy Rev 73, 745–757. https://doi.org/10.1016/j.rser.2017.01.161
  • Offshore Wind in Europe – Key Trends and Statistics. Wind Europe. (2021). https://www.connaissancedesenergies.org/sites/default/ files/pdf-actual ites/WindEurope-Offshore-wind-in-Europe- statistics-2020.pdf. (Accessed 30 November 2022).
  • Yazdi, M., (2019). A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis. International Journal of System Assurance Engineering and Management, 10, 1–18. https://doi.org/10.1007/ s13198-018-00757-7
  • Yazdi, M., Nedjati, A., Abbassi, R., (2019b). Fuzzy dynamic riskbased maintenance investment optimization for offshore process facilities. J. Loss Prev. Process. Ind. 194–207. https://doi. org/10.1016/j.jlp.2018.11.014
  • Zhao, X. G., & Ren, L. Z. (2015). Focus on the development of offshore wind power in China: Has the golden period come. Renewable Energy, 81, 644-657. https://doi.org/10.1016/j. renene.2015.03.077
Türk Coğrafya Dergisi-Cover
  • ISSN: 1302-5856
  • Başlangıç: 1943
  • Yayıncı: Türk Coğrafya Kurumu
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