Multi Objective Approach for The Selection of Wind Farms’ Location

Abstract

The rise of population volume not only affects the current non-renewable energy sources but also renewable sources. Wind is one of the cleanest source of renewable energy. The design decision encompasses complex and conflicting parameters so that an analytical solution methodology for the problem consideration is necessary. In this study, considering the complex character of wind farm location selection problem, a two-phase fuzzy goal programming approach is applied. The aim of the model is to determine the appropriate locations of wind farms by considering the maximization of technical, social and economic objectives. The process of proper location determination consists of three main stages. The first stage is the determination of potential 30 districts from the Marmara Region and data collection. The second stage is the establishment and application of a single objective form of the model (social, technical and economic objective) separately. The third stage is the implementation of a multi-objective form with two-phase fuzzy goal programming approach. The proposed model is applied to the Marmara Region of Turkey. The most suitable alternatives have been selected out of 30 candidate districts. The inclusion of fuzzy logic within multi-objective approach provides proper evaluation of objectives’ satisfaction values. The results of the applied models identify Bozüyük, Taraklı, and Malkara districts as suitable locations for wind farms, based on the fulfillment of objective functions related to technical, social, and economic factors. The findings point out that on average 25% of total electricity demand can be met by installing the determined wind farms in selected locations.

Keywords

• Wind Farm
• Two-Phase Fuzzy Goal Programming Approach
• Multi-Objective Decision Making

Rüzgar Çiftliklerinin Yer Seçimi İçin Çok Amaçlı Yaklaşım

Abstract

Nüfus hacminin artması, yenilenemeyen kaynaklardan ziyade yenilenebilir kaynakları da etkilemektedir. Rüzgâr enerjisi, en temiz yenilenebilir enerji kaynaklarından biridir. Rüzgâr enerjisinin kullanımı ile ilgili tasarım kararları karmaşık ve birbiri ile çelişen parametrelerin varlığı sebebiyle analitik bir çözüm yaklaşımını gerektiren bir problemdir. Bu çalışmada, rüzgâr tarlası seçim probleminin karmaşık yapısı göz önüne alınarak, iki aşamalı hedef programlama yaklaşımı uygulanmıştır. Oluşturulan modelin amacı, rüzgâr tarlaları için, teknik, sosyal ve ekonomik amaçlarının en iyilenmesini göz önüne alarak, uygun lokasyon seçimidir. Uygun yer seçiminin tanımlanması üç ana aşamadan oluşmaktadır. İlk aşama, Marmara Bölgesinde yer alan 30 ilçenin incelenmesidir. İkinci aşama Önerilen modelin her bir amaç fonksiyonu (sosyal, teknik ve ekonomik) ile ayrı ayrı çözülmesidir. Son aşama ise modelin çok amaç fonksiyonlu versiyonunun, iki aşamalı hedef programlama yaklaşımı ile uygulanmasıdır. Önerilen model, Türkiye’de yer alan Marmara Bölgesinde uygulanmıştır. 30 aday ilçe içinden en uygun alternatifler seçilmiştir. Çok- amaç fonksiyonlu yaklaşıma, bulanık mantığın eklenmesi ile amaçların tatmin seviyeleri uygun bir şekilde değerlendirilmesi sağlanmıştır.

Keywords

• İki Aşamalı Bulanık Amaç Programlama Yaklaşımı
• Çok Amaçlı Karar Verme
• Rüzgar Tarlası

References

1. URL 1. Elektrik. https://enerji.gov.tr/bilgi-merkezi-enerji-elektrik, accessed 07.04.2025.
2. Konstantinos, I., Georgios, T., & Garyfalos, A.. (2019). A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece. Energy Policy, 132, 232–246.
3. Sediqi, K.J. (2015), GIS-Based Multi-Criteria Approach For Land-Use Suitabili ty Analysis of Wind Farms: The Case Study of Karaburun Peninsula, Izmir-Turkey
4. Değirmenci, S., Bingöl, F., & Sofuoglu, S. C. (2018). MCDM analysis of wind energy in Turkey: decision making based on environmental impact. Environmental Science and Pollution Research, 25(20), 19753
5. Langer, J., Zaaijer, M., Quist, J., & Blok, K. (2023). Introducing site selection flexibility to technical and economic onshore wind potential assessments: New method with application to Indonesia. Renewable Energy, 202, 320-335
6. Villacreses, G., Gaona, G., Martínez-Gómez, J., & Jijón, D. J. (2017). Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renewable Energy, 109, 275–286.
7. Badi, I., Pamučar, D., Stević, Ž., & Muhammad, L. J. (2023). Wind farm site selection using BWM-AHP-MARCOS method: A case study of Libya. Scientific African, 19, e01511.
8. Salvador, C. B., Arzaghi, E., Yazdi, M., Jahromi, H. A., & Abbassi, R. (2022). A multi-criteria decision-making framework for site selection of offshore wind farms in Australia. Ocean & Coastal Management, 224, 106196.

9. Ayodele, T. R., Ogunjuyigbe, A. S. O., Odigie, O., & Munda, J. L. (2018). A multi-criteria 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.
10. Fetanat, A., & Khorasaninejad, E. (2015). A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. Ocean and Coastal Management, 109, 17–28.
11. Nagababu, G., Puppala, H., Pritam, K., & Kantipudi, M. P. (2022). Two-stage GIS-MCDM based algorithm to identify plausible regions at micro level to install wind farms: a case study of India. Energy, 248, 123594.
12. Shorabeh, S. N., Firozjaei, H. K., Firozjaei, M. K., Jelokhani-Niaraki, M., Homaee, M., & Nematollahi, O. (2022). The site selection of wind energy power plant using GIS-multi-criteria evaluation from economic perspectives. Renewable and Sustainable Energy Reviews, 168, 112778.
13. Mokarram, M., Pourghasemi, H. R., & Mokarram, M. J. (2022). A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method. Environmental Science and Pollution Research, 29(29), 43891-43912.
14. Cazzaro, D., Trivella, A., Corman, F., & Pisinger, D. (2022). Multi-scale optimization of the design of offshore wind farms. Applied energy, 314, 118830..
15. Long, H., He, Y., Cui, H., Li, Q., Tan, H., & Tang, B. (2022). Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation. Energy Reports, 8, 14183-14199.
16. Atici, K. B., Simsek, A. B., Ulucan, A., & Tosun, M. U. (2015). A GIS-based Multiple Criteria Decision Analysis approach for wind power plant site selection. Utilities Policy, 37, 86–96.
17. Latinopoulos, D., & Kechagia, K. (2015). A GIS-based multi-criteria evaluation for wind farm site selection. A regional scale application in Greece. Renewable Energy, 78, 550–560.
18. 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, 222–243.
19. Noorollahi, Y., Yousefi, H., & Mohammadi, M. (2016). Multi-criteria decision support system for wind farm site selection using GIS. Sustainable Energy Technologies and Assessments, 13, 38–50.
20. Łaska, G. (2017). Wind Energy and multi-criteria analysis in making decisions on the location of wind farms. Procedia Engineering, 182, 418-424.
21. Anwarzai, M. A., & Nagasaka, K. (2017). Utility-scale implementable potential of wind and solar energies for Afghanistan using GIS multi-criteria decision analysis. Renewable and Sustainable Energy Reviews, 71, 150–160.
22. Petrov, A. N., & Wessling, J. M. (2015). Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA. Wind Energy, 18(4), 713-727.
23. Montusiewicz, J., Gryniewicz-Jaworska, M., & Pijarski, P. (2015). Looking for the optimal location for wind farms. Advances in science and technology research journal, 9(27).
24. Pambudi, G., & Nananukul, N. (2019). Wind Turbine Site Selection in Indonesia, based on a hierarchical Dual Data Envelopment Analysis model. Energy Procedia, 158, 3290-3295.
25. Koc, A., Turk, S., & Şahin, G. (2019). Multi-criteria of wind-solar site selection problem using a GIS-AHP-based approach with an application in Igdir Province/Turkey. Environmental Science and Pollution Research, 1-13.
26. Ali, S., Taweekun, J., Techato, K., Waewsak, J., & Gyawali, S. (2019). GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand. Renewable Energy: An International Journal, 132, 1360–1372.
27. Senel, M.C. and Koc. E (2015), Site Selection Principles in Horizontal Axis Wind Turbines-Energy Production Cost, Wind Energy Magazine, 4, 37-42.
28. Pokonieczny, K. (2016). Using artificial neural networks to determine the location of wind farms. Miedzna district case study. Journal of Water and Land Development, 30(1), 101-111.
29. Mahdy, M., & Bahaj, A. S. (2018). Multi criteria decision analysis for offshore wind energy potential in Egypt. Renewable Energy, 118, 278–289.
30. Sheng, X., Zeng, P. P., Xing, H., Lienhart, P., & Dai, Q. (2018). A GIS+ MCDA based assessment method of potential onshore wind power development sites in Mongolia, International Conference on Power System Technology (POWERCON), 1465-1471.
31. Irawan, C. A., Starita, S., Chan, H. K., Eskandarpour, M., & Reihaneh, M. (2023). Routing in offshore wind farms: A multi-period location and maintenance problem with joint use of a service operation vessel and a safe transfer boat. European Journal of Operational Research, 307(1), 328-350.
32. Irawan, C. A., Salhi, S., & Chan, H. K. (2022). A continuous location and maintenance routing problem for offshore wind farms: Mathematical models and hybrid methods. Computers & Operations Research, 144, 105825.
33. Fischetti, M., & Fischetti, M. (2023). Integrated layout and cable routing in wind farm optimal design. Management Science, 69(4), 2147-2164.
34. URL 2 Türkiye Rüzgar Enerjisi Potansiyeli Haritası https://www.enerjiatlasi.com/ruzgar-enerjisi-haritasi/turkiye, accessed, 21.04.2025.
35. Özşahin, E., & Kaymaz, Ç. (2013). Rüzgâr enerji santrallerinin (RES) kuruluş yeri seçiminin CBS ile analizi: Hatay örneği. TÜBAV Bilim Dergisi, 6(2), 1-18.
36. URL 3 Sahibinden.com, accessed, 21.04.2025.
37. Karik, F., SÖZEN, A., & Izgec, M. (2017). The importance of wind power forecasts: A case study in Turkish electricity market. Journal of Polytechnic-Politeknik Dergisi, 20(4).
38. Turkish Wind Energy Association (TWEA), 2020. Turkey Wind Energy Statistical Report, https://www.tureb.com.tr/.
39. Tuzkaya, G., Kilic, H. S., & Aglan, C. (2016). A multi-objective supplier selection and order allocation model for green supply chains. Journal of Management and Information Science, 4(3), 87-96.
40. URL4http://cografyaharita.com/haritalarim/4mmarmara-bolgesi-ilceler-haritasi.png, accessed 26.04.2025.
41. Kilic, H. S., & Yalcin, A. S. (2020). Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection. Applied Soft Computing, 93, 106371.