Yenilenebilir Enerji Alternatiflerinin Değerlendirilmesinde Copras ve Interval Type-2 Fuzzy Topsis ile Hibrit Yaklaşıma Dayalı Bir Karar Destek Sistemi

Year 2023, Volume: 6 Issue: 2, 61 - 73, 31.12.2023

Aysun Sağbaş , Muhammet Deveci , Ulviye Polat

https://doi.org/10.55581/ejeas.1392881

Abstract

Yenilenebilir enerji (YE), toplumun ve ekonominin sürdürülebilir gelişimi için hayati bir kaynaktır. Hem gelişmiş hem de gelişmekte olan ülkelerin enerji ihtiyacının karşılanmasında önemli rol oynamaktadır. Ayrıca yenilenebilir enerji, çevrenin iyileştirilmesi, yakıt çeşitliliğinin artması, enerji fiyatlarının düşmesi, ekonomilerde değişiklik etkisi, ulusal ekonomik güvenlik ve ekonomik verimliliğin artması gibi birçok fayda yaratmaktadır. Herhangi bir ülke için en uygun yenilenebilir enerji alternatiflerinin seçimi, bölgesel, ulusal ve küresel enerji sistemleri planlamacılarına yol gösterici olabilir. Yenilenebilir enerji kaynaklarının sıralanması konusu, birbiriyle çelişen birçok kriteri içermekte olup; teknik, ekonomik, maliyet, sosyo-politik ve çevresel kriterlerin eş zamanlı olarak bir araya getirilmesi bakımından karmaşık bir sorundur. Bu çalışmada, ulusal yenilenebilir enerji yatırımlarının doğrudan planlanmasında YE alternatiflerini önceliklendirmek amacıyla Aralıklı Tip-2 Bulanık TOPSIS Tekniği ve COPRAS yönteminden oluşan entegre çok kriterli karar verme (MCDM) yaklaşımı kullanılmıştır. Önerilen metodolojinin uygulanabilirliğini göstermek amacıyla uzman değerlendirmeleri yoluyla Türkiye için gerçek bir vaka uygulaması sunulmuştur.

Keywords

Aralıklı tip-2 bulanık kümeler, COPRAS, Çok kriterli karar verme, TOPSIS, Yenilenebilir enerji.

A Decision Support System Based on Hybrid Approach With Copras And Interval Type-2 Fuzzy Topsis For Evaluation Of Renewable Energy Alternatives

Abstract

Renewable energy (RE) is a vital source for the sustainable development of society and economy. It plays a significant role in meeting energy requirements of both developed and developing countries. Moreover, renewable energy creats multiple benefits such as environmental improvement, increases fuel diversity, reduction of energy price, volality effect on their economy, national economic security, and increases in economic productivity. Selection of the most appropriate RE alternatives for any country can provide guidelines to planners of regional, national and global energy systems. The issue of ranking renewable energy sources involves many conflicting criteria and is a complicated problem since it needs to simultaneously incorporate technical, economic, cost, social-political, and environmental criteria. In this study, an integrated multi criteria decision making (MCDM) aproach consisting of interval type-2 fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Compressed Proportional Assessment (COPRAS) method is conducted to prioritize RE alternatives in order to direct planning of the national RE investments. A real case application for Turkey has been presented via expert evaluations to demonstrate applicability of the proposed methodology.

Keywords

COPRAS, Interval type-2 fuzzy sets, Multi criteria decision making, Renewable energy, TOPSIS.

References

• Taha, R.A., & Daim, T. (2013). Multi-criteria applications in renewable energy analysis, a literature review. In T. Daim, T. Oliver, & J. Kim (Eds.) Research and technology management in the electricity industry, Springer, London, 17-30.
• Yazdani-Chamzini, A., Fouladgar, M.M., Zavadskas, E.K., & Moini, S.H.H. (2013). Selecting the optimal renewable energy using multi criteria decision making. Journal of Business Economics and Management, 14(5), 957-978.
• Kahraman, C., & Kaya, İ. (2010). A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications, 37(9), 6270-6281.
• Pohekar, S.D., & Ramachandran, M. (2004). Application of multi criteria decision making to sustainable energy planning- A review. Renewable and Sustainable Energy Reviews, 8(4), 365–381.
• San Cristóbal, J.R. (2011). Multi criteria decision making in the selection of a renewable energy project in Spain: The vikor method. Renewable Energy, 36(2), 498-502.
• Amer, M., & Daim, T.U. (2011). Selection of renewable energy technologies for a developing county: a case of Pakistan. Energy for Sustainable Development, 15(4), 420-435.
• Ahmad, S., & Tahar, R.M. (2014). Selection of renewable energy sources for sustainable development electricity generation system using analytic hierarchy process: A case of Malaysia. Renewable Energy, 63, 458-466.
• Tasri, A., & Susilawati, A. (2014). Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sustainable Energy Technologies and Assessments, 7, 34-44.
• Yi, S.K., Sin, H.Y., & Heo, E. (2011). Selecting sustainable renewable energy source for energy assistance to North Korea. Renewable and Sustainable Energy Reviews, 15(1), 554-563.
• Gitinavard, H., Mousavi, S.M., & Vahdani, B. (2017). Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study. Energy, 118, 556-577.
• McKenna, R., Bertsch, V., Mainzer, K., & Fichtner, W. (2018). Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities. European Journal of Operational Research, 268(3), 1092-1110.
• Kabak, M., & Dağdeviren, M. (2014). Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Conversion and Management, 79, 25-33.
• Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34(10), 1603-1616.
• Pak, B.K., Albayrak, Y.E., & Erensal, Y.C. (2015). Renewable energy perspective for Turkey using sustainability indicators. International Journal of Computational Intelligence Systems, 8(1), 187-197.
• Sengul, U., Eren, M., Shiraz, S.E., Gezder, V., & Sengul, A.B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy, 75, 617-625.
• Topcu, Y.I., & Uluengin, F. (2004). Energy for the future: an integrated decision aid for the case of Turkey. Energy, 29 (1), 137-154.
• Ulutaş, B.H. (2005). Determination of the appropriate energy policy for Turkey. Energy, 30(7), 1146-1161.
• Çolak, M., & Kaya, İ. (2017). Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and Sustainable Energy Reviews, 80, 840-853.
• Chen, S.M., & Lee, L.W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems With Applications, 37(4), 2790-2798.
• Baris, K., & Kucukali, S. (2012). Availibility of renewable energy sources in Turkey: Current situation, potential, government policies and the EU perspective. Energy Policy, 42, 377-391.
• Kaya, T., Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585.
• Atmaca, E., & Basar, H.B. (2012). Evaluation of power plants in Turkey using Analytic Network Process (ANP). Energy, 44(1), 555-563.
• Ertay, T., Kahraman, C., & Kaya, İ. (2013). Evaluation of renewable energy alternatives using MACBETH and fuzzy AHP multicriteria methods: the case of Turkey. Technological and Economic Development of Economy, 19(1), 38-62.
• Büyüközkan, G., & Güleryüz, S. (2016). An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. International Journal of Production Economics, 182(c), 435-448.
• Çelikbilek, Y., & Tüysüz, F. (2016). An integrated grey based multi-criteria decision making approach for the evaluation of renewable energy sources. Energy, 115, 1246-1258.
• Balin, A., & Baraçlı, H. (2017). A fuzzy multi criteria decision making methodology based upon the interval type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey. Technological and Economic Develeopment of Economy, 23, 742-76.
• MENR. (2019). Turkish national renewable energy action plan, Ankara, Turkey: Republic of Turkey Ministry of Energy and Natural Resources.
• Toksarı, M.D. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984-3990.
• Tefek, M.F., Uğuz, H., & Güçyetmez, M. (2019). A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Computing and Applications, 31, 2939–2954.
• Demirel, N.Ç., Demirel, T., Deveci, M., & Vardar, G. (2017). Location selection for underground natural gas storage using Choquet integral. Journal of Natural Gas Science and Engineering, 45, 368-379.
• Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications, New York, USA: Springer-Verlag.
• Dağdeviren, M., Yavuz, S., & Kılınç, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36 (4), 8143-8151.
• Önüt, S., & Soner, S. (2008). Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management, 28 (9), 1552-1559.
• Dağdeviren, M. (2010). A hybrid multi criteria decision-making model for personnel selection in manufacturing systems. Journal of Intelligent Manufacturing, 21, 451–460.
• Shih, H.S., Shyur, H.J., & Lee, E.S. (2007) An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801-813.
• Wang, P., Li, Y., Wang, Y.H., & Zhu, Z.Q. (2015). A new method based on TOPSIS and response surface method for MCDM problems with interval numbers. Mathematical Problems in Engineering, 938535, 11.
• Afgan, N.H., & Carvalho, M.G. (2002). Multi-criteria assessment of new and renewable energy power plants. Energy, 27(8), 739-755.
• Theodorou, S., Florides, G., & Tassou, S. (2010). The use of multiple criteria decision making methodologies for the promotion of RES through funding schemes in Cyprus, A review. Energy Policy, 38(12), 7783-7792.
• Al Garni, H., Kassem, A., Awasthi, A., Komljenovic, D., & Al-Haddad, K. (2016). A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable Energy Technologies and Assessments, 16, 137-150.
• Shmelev, S.E., Van den Bergh, J.C., & Jeroen, C.J.M. (2016). Optimal diversity of renewable energy alternatives under multiple criteria: An application to the UK. Renewable and Sustainable Energy Reviews, 60, 679-691.
• Lee, H.C., & Chang, C.T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews, 92, 883–896.
• Ghose, D., Pradhan, S., & Shabbiruddin (2019). Development of model for assessment of renewable energy sources: a case study on Gujarat, India. International Journal of Ambient Energy.
• Hassan, M., Afridi, M.K., & Khan, M.I. (2019). Energy policies and environmental security: A multi-criteria analysis of energy policies of Pakistan. International Journal of Green Energy, 16 (7), 510–519.
• Seddiki, M., & Bennadji, A. (2019). Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building. Renewable and Sustainable Energy Reviews, 110, 101–117.
• Ayağ, Z., & Samanlioglu, F. (2020). Fuzzy AHP-GRA approach to evaluating energy sources: a case of Turkey. International Journal of Energy Sector Management, 14 (1), 40-58.
• Çolak, M., & Kaya, İ. (2020). Multi criteria evaluation of energy storage technologies based on hesitant fuzzy information: A case study for Turkey. Journal of Energy Storage, 28, 101211.
• Ghenai, C., Albawab, M., & Bettayeb, M. (2020). Sustainability indicators for renewable energy systems using multicriteria decision making model and extended SWARA/ARAS hybrid method. Renewable Energy, 146, 580-597.
• Rani, P., Mishra, A.R., Mardani, A., Cavallaro, F., Alrasheedi, M., & Alrashidi, A. (2020). A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection. Journal of Cleaner Production, 257, 120352.
• Martín-Gamboa, M., Iribarren, D., García-Gusano, D., & Dufour, J. (2017). A review of life-cycle approaches coupled with data envelopment analysis within multi-criteria decision analysis for sustainability assessment of energy systems. Journal of Cleaner Production, 150, 164-174.
• Li, C., Negnevitsky, M., Wang, X., Yue, W.L., & Zou, X. (2019). Multi-criteria analysis of policies for implementing clean energy vehicles in China. Energy Policy, 129, 826–840.
• Naicker, P., & Thopil, G.A. (2019). A framework for sustainable utility scale renewable energy selection in South Africa. Journal of Cleaner Production, 224, 637-650.
• Zhang, L., Xin, H., Yong, H., & Kan, Z. (2019). Renewable energy project performance evaluation using a hybrid multi-criteria decision-making approach: Case study in Fujian. China, Journal of Cleaner Production, 206, 1123-1137.
• Nsafon, B.E.K., Butu, H.M., Owolabi, A.B., Roh, J.W., Suh, D., Huh & J.S. (2020). Integrating multi-criteria analysis with PDCA cycle for sustainable energy planning in Africa: Application to hybrid mini-grid system in Cameroon. Sustainable Energy Technologies and Assessments, 37, 100628.
• Deveci, K., & Güler, Ö. (2020). A CMOPSO based multi objective optimization of renewable energy planning: Case of Turkey. Renewable Energy, 155, 578-590.
• Troldborg, M., Heslop, S., & Hough, R.L. (2014). 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, 39, 1173-1184.
• Pasaoglu, G., Garcia, N. P., & Zubi, G. (2018). A multi criteria and multi expert decision aid approach to evaluate the future Turkish power plant portfolio. Energy Policy, 119, 654–665.
• Yang, Y., Ren, J., Solgaard, H. S., Xu, D., & Nguyen, T. T. (2018). Using multi criteria analysis to prioritize renewable energy home heating technologies. Sustainable Energy Technologies and Assessments, 29, 36–43.
• Alizadeh, R., Soltanisehat, L., Lund, P. D., & Zamanisabzi, H. (2020). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, 137, 111174.
• Diemuodeke, E. O., Addo, A., Oko, C.O.C., Mulugetta, Y., & Ojapah, M. M. (2019). Optimal mapping of hybrid renewable energy systems for locations using multi criteria decision-making algorithm. Renewable Energy, 134, 461-477.
• Zavadskas, E. K., & Kaklauskas, A. (1996). Pastatu sistemotechninis ivertinimas [Multiple criteria evaluation of buildings], Vilnius: Technika.
• Das, M. C., Sarkar, B., & Ray, S. (2012). A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology. Socio-Economic Planning Sciences, 46(3), 230-241.
• Sarıcalı, G, & Kundakcı, N. (2016). AHP ve COPRAS yöntemleri ile otel alternatiflerinin değerlendirilmesi. International Review of Economics and Management, 4(1), 45-66.
• SPOTR. (2009). Electricity Energy Market and Supply Security Strategy Paper. Ankara, Turkey: Republic of Turkey State Planning Organization.