Pisagor Bulanık Küme Ortamında Yenilenebilir Enerji Kaynağı Seçimi

Yıl 2024, Cilt: 6 Sayı: 2, 96 - 115, 30.08.2024

Yasin Ölç , Fethullah Göçer

https://doi.org/10.46740/alku.1420828

Öz

Yenilenebilir Enerji seçiminde kriter belirleme aşaması, yenilenebilir enerji ile ilgili birçok karar kriterinden etkilenen faktörlere sahiptir. Bir seçim ortamında potansiyel kriterlerin değerlendirilmesi ve önceliklendirilmesi, çok kriterli karar verme problemi olarak ele alınabilir. Bu çalışmanın amacı, yenilenebilir enerji bağlamında kriter seçim süreçlerini teknik, ekonomik, sosyal ve çevresel yönleri ile analiz etmektir. Yazın taraması, çalışma bölgelerini, kısıtlamaları, değerlendirme ölçütlerini ve yenilenebilir enerji seçimi süreci için kullanılan yöntemleri sentezlemek ve kategorize etmek için sistematik bir inceleme yöntemi kullanılarak oluşturulmuştur. Kriter seçimi sürecinde, insan yargılarının öznelliği genellikle çatışmaya yol açar ve bir tür tereddüt yaratır. Önerilen çalışma, değerlendirme verileriyle ilişkili belirsizliği ve muğlaklığı Grup Karar Verme ortamında Pisagor Bulanık Küme kullanarak gidermeye çalışmıştır. Önerilen yöntem, Pisagor Bulanık Kümelerin geleneksel bulanık kümelere kıyasla daha doğru bilgi sağlama yeteneğinden ve grup karar vermenin karar bilgisinde önyargı ve öznellikten kaçınma kolaylığından yararlanır. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) tekniği, grup karar verme kullanılarak Pisagor Bulanık Küme ortamında uygulanmaktadır. Önerilen yaklaşımı doğrulamak için ampirik bir vaka çalışması uygulanmıştır. Son olarak, sentezlenmiş ve kategorize edilmiş bilgi ve araştırma boşluklarından oluşan kapsamlı bir havuz sağlayarak, bu çalışma, karar vericilerin yenilenebilir enerji seçiminde en uygun kriterleri belirlemeleri için bir yol haritası sunmaktadır.

Anahtar Kelimeler

Yenilenebilir Enerji Seçimi, Çok Kriterli Karar Verme, Pisagor Bulanık Kümeleri, Değerlendirme Kriteri, Grup Karar Verme

Renewable Energy Source Selection by Pythagorean Fuzzy Sets

Öz

The criteria determination in Renewable Energy selection has factors that are strongly influenced by many decision criteria regarding renewable energy. Evaluating and prioritizing potential criteria in a selection environment can be addressed as a multi-criteria decision-making problem. The aim of this study is to analyze criterion selection processes in the context of renewable energy with their technical, economic, social and environmental aspects. The literature review is created using a systematic review method to synthesize and categorize study regions, constraints, evaluation criteria, and methods used for the renewable energy selection process. In the process of criterion selection, the subjectivity of human judgments often leads to conflict and creates a kind of hesitation. To avoid uncertainty and ambiguity associated with evaluation data, the proposed work attempts to eliminate it using Pythagorean Fuzzy Set in a Group Decision Making environment. The proposed method takes advantage of the ability of Pythagorean Fuzzy Sets to provide more accurate information compared to traditional fuzzy sets and the ease it with group decision making which avoids bias and subjectivity in decision information. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique is implemented in the Pythagorean Fuzzy Set environment using group decision making. An empirical case study was applied to validate the proposed approach. Finally, by providing a comprehensive repository of synthesized and categorized knowledge and research gaps, this study offers a roadmap for decision-makers to determine the most appropriate criteria for choosing renewable energy.

Anahtar Kelimeler

Renewable Energy Selection, Multi-Criteria Decision Making, Pythagorean Fuzzy Sets, Evaluation Criteria, Group Decision Making

Kaynakça

• [1] Koç E and Kaya K, “Enerji Kaynakları–Yenilenebilir Enerji Durumu,” Mühendis ve Makina, vol. 56, no. 667, pp. 36–47, 2015.
• [2] A. Kayahan Karakul, “Bulanık AHP Yöntemi ile Yenilenebilir Enerji Kaynağı Seçimi,” Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, no. 19, pp. 127–150, Apr. 2020, doi: 10.29029/busbed.640162.
• [3] D. Ghose, S. Pradhan, and Shabbiruddin, “Development of model for assessment of renewable energy sources: a case study on Gujarat, India,” International Journal of Ambient Energy, vol. 43, no. 1, pp. 1157–1166, Dec. 2022, doi: 10.1080/01430750.2019.1691650.
• [4] H. Dinçer and S. Yüksel, “Multidimensional evaluation of global investments on the renewable energy with the integrated fuzzy decision-making model under the hesitancy,” Int J Energy Res, vol. 43, no. 5, pp. 1775–1784, Apr. 2019, doi: 10.1002/er.4400.
• [5] G. N. Yücenur, Ş. Çaylak, G. Gönül, and M. Postalcıoğlu, “An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility,” Renew Energy, vol. 145, pp. 2587–2597, Jan. 2020, doi: 10.1016/j.renene.2019.08.011.
• [6] Y. A. Solangi, Q. Tan, N. H. Mirjat, G. Das Valasai, M. W. A. Khan, and M. Ikram, “An Integrated Delphi-AHP and Fuzzy TOPSIS Approach toward Ranking and Selection of Renewable Energy Resources in Pakistan,” Processes, vol. 7, no. 2, p. 118, Feb. 2019, doi: 10.3390/pr7020118.
• [7] E. Billig and D. Thraen, “Renewable methane – A technology evaluation by multi-criteria decision making from a European perspective,” Energy, vol. 139, pp. 468–484, Nov. 2017, doi: 10.1016/j.energy.2017.07.164.
• [8] H.-C. Lee and C.-T. Chang, “Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan,” Renewable and Sustainable Energy Reviews, vol. 92, pp. 883–896, Sep. 2018, doi: 10.1016/j.rser.2018.05.007.
• [9] P. Rani, A. R. Mishra, K. R. Pardasani, A. Mardani, H. Liao, and D. Streimikiene, “A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India,” J Clean Prod, vol. 238, p. 117936, Nov. 2019, doi: 10.1016/j.jclepro.2019.117936.
• [10] F. Göçer, “A Novel Extension of Fermatean Fuzzy Sets into Group Decision Making: A Study for Prioritization of Renewable Energy Technologies,” Arab J Sci Eng, Oct. 2023, doi: 10.1007/s13369-023-08307-5.
• [11] C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making, vol. 186. Berlin, Heidelberg: Springer Berlin Heidelberg, 1981. doi: 10.1007/978-3-642-48318-9.
• [12] S. Dündar, F. Ecer, and Ş. Özdemir, “Fuzzy Topsis Yöntemi İle Sanal Mağazaların Web Sitelerinin Değerlendirilmesi,” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt. 21, Sayı. 1, ss.1- 19.
• [13] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, Jun. 1965, doi: 10.1016/S0019-9958(65)90241-X.
• [14] R. R. Yager, “Pythagorean Membership Grades in Multicriteria Decision Making,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 4, pp. 958–965, Aug. 2014, doi: 10.1109/TFUZZ.2013.2278989.
• [15] E. Ilbahar, A. Karaşan, S. Cebi, and C. Kahraman, “A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system,” Saf Sci, vol. 103, pp. 124–136, Mar. 2018, doi: 10.1016/j.ssci.2017.10.025.
• [16] R. R. Yager, “Pythagorean fuzzy subsets,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, Jun. 2013, pp. 57–61. doi: 10.1109/IFSA-NAFIPS.2013.6608375.
• [17] P. Rani, A. R. Mishra, G. Rezaei, H. Liao, and A. Mardani, “Extended Pythagorean Fuzzy TOPSIS Method Based on Similarity Measure for Sustainable Recycling Partner Selection,” International Journal of Fuzzy Systems, vol. 22, no. 2, pp. 735–747, Mar. 2020, doi: 10.1007/s40815-019-00689-9.
• [18] F. Göçer and G. Büyüközkan, “A novel extension of Pythagorean fuzzy MULTIMOORA approach for new product development,” Heliyon, vol. 9, no. 6, p. e16726, Jun. 2023, doi: 10.1016/j.heliyon.2023.e16726.
• [19] F. Göçer, “Improving sustainable supplier evaluation by an integrated MCDM method under pythagorean fuzzy environment,” Cumhuriyet Science Journal, vol. 42, no. 1, pp. 218–235, Mar. 2021, doi: 10.17776/csj.735674.
• [20] M. Akram, W. A. Dudek, and F. Ilyas, “Group decision‐making based on pythagorean fuzzy TOPSIS method,” International Journal of Intelligent Systems, vol. 34, no. 7, pp. 1455–1475, Jul. 2019, doi: 10.1002/int.22103.
• [21] M. Yucesan and M. Gul, “Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS,” Soft comput, vol. 24, no. 5, pp. 3237–3255, Mar. 2020, doi: 10.1007/s00500-019-04084-2.
• [22] Y. Dorfeshan and S. M. Mousavi, “A group TOPSIS-COPRAS methodology with Pythagorean fuzzy sets considering weights of experts for project critical path problem,” Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1375–1387, Mar. 2019, doi: 10.3233/JIFS-172252.
• [23] A. Biswas and B. Sarkar, “Pythagorean fuzzy TOPSIS for multicriteria group decision-making with unknown weight information through entropy measure,” International Journal of Intelligent Systems, vol. 34, no. 6, pp. 1108–1128, Jun. 2019, doi: 10.1002/int.22088.
• [24] M. Hajiaghaei-Keshteli, Z. Cenk, B. Erdebilli, Y. Selim Özdemir, and F. Gholian-Jouybari, “Pythagorean Fuzzy TOPSIS Method for Green Supplier Selection in the Food Industry,” Expert Syst Appl, vol. 224, p. 120036, Aug. 2023, doi: 10.1016/j.eswa.2023.120036.
• [25] Uysal F, “Türkiye’de Yenilenebilir Enerji Alternatiflerinin Seçimi İçin Graf Teori ve Matris Yaklaşım,” Ekonometri ve İstatistik Sayı:13 (12. Uluslararası Ekonometri, Yöneylem Araştırması, İstatistik Sempozyumu Özel Sayısı), pp. 23–40, 2011.
• [26] S.-K. Yi, H.-Y. Sin, and E. Heo, “Selecting sustainable renewable energy source for energy assistance to North Korea,” Renewable and Sustainable Energy Reviews, vol. 15, no. 1, pp. 554–563, Jan. 2011, doi: 10.1016/j.rser.2010.08.021.
• [27] A. Sadeghi, T. Larimian, and A. Molabashi, “Evaluation of Renewable Energy Sources for Generating Electricity in Province of Yazd: A Fuzzy Mcdm Approach,” Procedia Soc Behav Sci, vol. 62, pp. 1095–1099, Oct. 2012, doi: 10.1016/j.sbspro.2012.09.187.
• [28] Yakıcı Ayan Tuba and Pabuçcu Hakan, “Yenilenebilir Enerji Kaynakları Yatırım Projelerinin Analitik Hiyerarşi Süreci Yöntemi ile Değerlendirilmesi,” Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 18, pp. 89–110, 2013.
• [29] T. Ertay, C. Kahraman, and İ. Kaya, “Evaluation Of Renewable Energy Alternatives Using Macbeth And Fuzzy AHP Multicriteria Methods: The Case Of Turkey,” Technological and Economic Development of Economy, vol. 19, no. 1, pp. 38–62, Apr. 2013, doi: 10.3846/20294913.2012.762950.
• [30] A. Yazdani-Chamzini, M. M. Fouladgar, E. K. Zavadskas, and S. H. H. Moini, “Selecting The Optimal Renewable Energy Using Multi Criteria Decision Making,” Journal of Business Economics and Management, vol. 14, no. 5, pp. 957–978, Sep. 2013, doi: 10.3846/16111699.2013.766257.
• [31] A. Tasri and A. Susilawati, “Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia,” Sustainable Energy Technologies and Assessments, vol. 7, pp. 34–44, Sep. 2014, doi: 10.1016/j.seta.2014.02.008.
• [32] G. Büyüközkan and S. Güleryüz, “A new GDM based AHP framework with linguistic interval fuzzy preference relations for renewable energy planning,” Journal of Intelligent & Fuzzy Systems, vol. 27, no. 6, pp. 3181–3195, 2014, doi: 10.3233/IFS-141275.
• [33] A. Maxim, “Sustainability assessment of electricity generation technologies using weighted multi-criteria decision analysis,” Energy Policy, vol. 65, pp. 284–297, Feb. 2014, doi: 10.1016/j.enpol.2013.09.059.
• [34] M. Troldborg, S. Heslop, and R. L. Hough, “Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties,” Renewable and Sustainable Energy Reviews, vol. 39, pp. 1173–1184, Nov. 2014, doi: 10.1016/j.rser.2014.07.160.
• [35] Ü. Şengül, M. Eren, S. Eslamian Shiraz, V. Gezder, and A. B. Şengül, “Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey,” Renew Energy, vol. 75, pp. 617–625, Mar. 2015, doi: 10.1016/j.renene.2014.10.045.
• [36] Y. Çelikbilek and F. Tüysüz, “An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources,” Energy, vol. 115, pp. 1246–1258, Nov. 2016, doi: 10.1016/j.energy.2016.09.091.
• [37] Sağır H and Doğanalp B, “Bulanık Çok Kriterli Karar Verme Perspektifinden Türkiye İçin Enerji Kaynakları Değerlendirmesi,” Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Ocak 2016, Sayı:11, 2016.
• [38] L. Abdullah and L. Najib, “Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia,” International Journal of Sustainable Energy, vol. 35, no. 4, pp. 360–377, Apr. 2016, doi: 10.1080/14786451.2014.907292.
• [39] H. Al Garni, A. Kassem, A. Awasthi, D. Komljenovic, and K. Al-Haddad, “A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia,” Sustainable Energy Technologies and Assessments, vol. 16, pp. 137–150, Aug. 2016, doi: 10.1016/j.seta.2016.05.006.
• [40] G. Büyüközkan and S. Güleryüz, “Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations,” Energy, vol. 123, pp. 149–163, Mar. 2017, doi: 10.1016/j.energy.2017.01.137.
• [41] S. Ünlüsoy, T. Eren, and E. Özcan, “ANP VE TOPSIS Yöntemleriyle Türkiye’de Yenilenebilir Enerji Yatırım Alternatiflerinin Değerlendirilmesi,” Selcuk University Journal of Engineering ,Science and Technology, vol. 5, no. 2, pp. 204–219, Jun. 2017, doi: 10.15317/Scitech.2017.82.
• [42] O. Derse and E. Yontar, “SWARA-TOPSIS Yöntemi ile En Uygun Yenilenebilir Enerji Kaynağının Belirlenmesi,” Endüstri Mühendisliği, vol. 31, no. 3, pp. 389–419, Dec. 2020, doi: 10.46465/endustrimuhendisligi.798063.
• [43] S. Bilgiç, B. Torğul, and T. Paksoy, “Sürdürülebilir Enerji Yönetimi için BWM Yöntemi ile Yenilenebilir Enerji Kaynaklarının Değerlendirilmesi,” Verimlilik Dergisi, no. 2, pp. 95–110, Apr. 2021, doi: 10.51551/verimlilik.663721.
• [44] M. Sipa and I. Gorzeń-Mitka, “Assessment of the Progress towards the Management of Renewable Energy Consumption in the Innovativeness Context—A Country Approach,” Energies (Basel), vol. 14, no. 16, p. 5064, Aug. 2021, doi: 10.3390/en14165064. [45] Y. Xie, Y. Zhou, Y. Peng, H. Dincer, S. Yuksel, and P. an Xiang, “An Extended Pythagorean Fuzzy Approach to Group Decision-Making With Incomplete Preferences for Analyzing Balanced Scorecard-Based Renewable Energy Investments,” IEEE Access, vol. 9, pp. 43020–43035, 2021, doi: 10.1109/ACCESS.2021.3065294.
• [46] E. Yontar, “Determining the Importance Order of The Criteria Affecting the Problems of Regional Selection in Renewable Energy Studies,” Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, vol. 14, no. 2, pp. 475–491, Jul. 2022, doi: 10.29137/umagd.1034298.
• [47] F. Sitorus and P. R. Brito-Parada, “The selection of renewable energy technologies using a hybrid subjective and objective multiple criteria decision making method,” Expert Syst Appl, vol. 206, p. 117839, Nov. 2022, doi: 10.1016/j.eswa.2022.117839.
• [48] H. Rezk et al., “Multi-criteria decision making for different concentrated solar thermal power technologies,” Sustainable Energy Technologies and Assessments, vol. 52, p. 102118, Aug. 2022, doi: 10.1016/j.seta.2022.102118.
• [49] K. Govindan, “Pathways to low carbon energy transition through multi criteria assessment of offshore wind energy barriers,” Technol Forecast Soc Change, vol. 187, p. 122131, Feb. 2023, doi: 10.1016/j.techfore.2022.122131.
• [50] K. Barnaś et al., “Algorithm for the comprehensive thermal retrofit of housing stock aided by renewable energy supply: A sustainable case for Krakow,” Energy, vol. 263, p. 125774, Jan. 2023, doi: 10.1016/j.energy.2022.125774.
• [51] M. Kamali Saraji, E. Aliasgari, and D. Streimikiene, “Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach,” Technol Forecast Soc Change, vol. 189, p. 122399, Apr. 2023, doi: 10.1016/j.techfore.2023.122399.
• [52] M. Shao, Z. Han, J. Sun, H. Gao, S. Zhang, and Y. Zhao, “A novel framework for wave power plant site selection and wave forecasting based on GIS, MCDM, and ANN methods: A case study in Hainan Island, Southern China,” Energy Convers Manag, vol. 299, p. 117816, Jan. 2024, doi: 10.1016/j.enconman.2023.117816.
• [53] X. Peng and Y. Yang, “Fundamental Properties of Interval-Valued Pythagorean Fuzzy Aggregation Operators,” International Journal of Intelligent Systems, vol. 31, no. 5, pp. 444–487, May 2016, doi: 10.1002/int.21790.
• [54] X. Zhang and Z. Xu, “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets,” International Journal of Intelligent Systems, vol. 29, no. 12, pp. 1061–1078, Dec. 2014, doi: 10.1002/int.21676.
• [55] J.-J. Wang, Y.-Y. Jing, C.-F. Zhang, and J.-H. Zhao, “Review on multi-criteria decision analysis aid in sustainable energy decision-making,” Renewable and Sustainable Energy Reviews, vol. 13, no. 9, pp. 2263–2278, Dec. 2009, doi: 10.1016/j.rser.2009.06.021.
• [56] G. Büyüközkan, Y. Karabulut, and E. Mukul, “A novel renewable energy selection model for United Nations’ sustainable development goals,” Energy, vol. 165, pp. 290–302, Dec. 2018, doi: 10.1016/j.energy.2018.08.215.
• [57] https://kahramanmaras.csb.gov.tr/kahramanmaras-ili-caglayancerit-nurhak-ilcesindeki-duyuru-340537.