Advertisement

Strategic Renewable Energy Source Selection for Turkey with Hesitant Fuzzy MCDM Method

  • Gülçin Büyüközkan
  • Yağmur Karabulut
  • Merve Güler
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)

Abstract

Renewable energy sources (RES) strengthen their hold on emerging economies. Record numbers of newly installed RES capacity are being observed in recent years. In 2016, the addition of renewable resources were more than 60% of new capacity investments globally, surpassing fossil fuel-based investments. The majority of these additions take place in developing countries, indicating the vital importance of selecting the best RES technologies for Turkey, an emerging economy. RES is not only becoming less expensive, they also contribute to employment and environmental protection. Selecting the most appropriate RES strategy among alternatives involves many criteria. This chapter introduces a novel RES evaluation model that can guide investors in identifying the most suitable RES strategy from a sustainability perspective. Complex socio-economic decision problems often make it more difficult for Decision Makers to consider different aspects, and to provide exact numerical values. Considering many, usually conflicting sustainability factors that affect this selection process, the chapter proposes a Multi-Criteria Decision-Making (MCDM) model by implementing hesitant fuzzy linguistic term sets (HFLTS) for an effective RES strategy evaluation problem. Group Decision Making (GDM) is also integrated to the method, as it is capable to offset individual DMs’ bias and partiality. HFLTS enables DMs to accurately provide their linguistic expressions. An integrated HFL SAW method (Simple Additive Weighting) and HFL TOPSIS method (Technique for Order Performance by Similarity to Ideal Solution) are employed for this purpose. The criteria priorities are determined with the HFL SAW method and the final RES strategy ranking results are determined with HFL TOPSIS method. The plausibility of the proposed framework is tested in a case study. This combination of MCDM techniques is applied for the first time in the literature for dealing with this problem setting.

Notes

Acknowledgements

The authors express their sincere thanks and gratitude to the industry experts for their invaluable feedback and support in the evaluations. This research was supported by Galatasaray University Research Fund (Projects number: 17.402.004 and 17.402.009).

References

  1. Balin, A., & Baraçli, 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 Development of Economy, 23, 742–763.  https://doi.org/10.3846/20294913.2015.1056276.CrossRefGoogle Scholar
  2. Beg, I., & Rashid, T. (2013). TOPSIS for hesitant fuzzy linguistic term sets. International Journal of Intelligent Systems, 28, 1162–1171.  https://doi.org/10.1002/int.21623.CrossRefGoogle Scholar
  3. Büyüközkan, G., & Güler, M. (2017). Hesitant fuzzy linguistic VIKOR method for e-health technology selection (in Press).Google Scholar
  4. Büyüközkan, G., & Güleryüz, S. (2017). Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations. Energy, 123, 149–163.  https://doi.org/10.1016/j.energy.2017.01.137.CrossRefGoogle Scholar
  5. Büyüközkan, G., & Güleryüz, S. (2014). A new GDM based AHP framework with linguistic interval fuzzy preference relations for renewable energy planning. Journal of Intelligent & Fuzzy Systems, 27, 3181–3195.MathSciNetGoogle Scholar
  6. Büyüközkan, G., & Güleryüz, S. (2016). An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey. International Journal of Production Economics, 182, 435–448.  https://doi.org/10.1016/j.ijpe.2016.09.015.CrossRefGoogle Scholar
  7. Büyüközkan, G., & Karabulut, Y. (2017). Energy project performance evaluation with sustainability perspective. Energy, 119, 549–560.  https://doi.org/10.1016/j.energy.2016.12.087.CrossRefGoogle Scholar
  8. Cevik Onar, S., Oztaysi, B., & Kahraman, C. (2014). Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: A case study. International Journal of Computational intelligence systems, 7, 1002–1021.CrossRefGoogle Scholar
  9. Chen, S.-J. J., Hwang, C.-L., Beckmann, M. J., & Krelle, W. (1992). Fuzzy multiple attribute decision making: Methods and applications. New York Inc: Springer.CrossRefGoogle Scholar
  10. Chou, S.-Y., Chang, Y.-H., & Shen, C.-Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189, 132–145.  https://doi.org/10.1016/j.ejor.2007.05.006.CrossRefzbMATHGoogle Scholar
  11. 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.  https://doi.org/10.1016/j.rser.2017.05.194.CrossRefGoogle Scholar
  12. Erdogan, M., & Kaya, I. (2015). An integrated multi-criteria decision-making methodology based on type-2 fuzzy sets for selection among energy alternatives in Turkey. Iranian Journal of Fuzzy Systems, 12, 1–25.MathSciNetGoogle Scholar
  13. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1995). A sequential selection process in group decision making with a linguistic assessment approach. Information Sciences, 85, 223–239.CrossRefzbMATHGoogle Scholar
  14. Hsu, C.-C., & Sandford, B. A. (2007). The Delphi technique: Making sense of consensus. Practical assessment, research & evaluation, 12, 1–8.Google Scholar
  15. Hwang, C., & Yoon, K. (1981). Multiple attribute decision making. Lecture notes in Economics and Mathematical Systems. http://www.doi.org/10.1007/978-3-642-48318-9.
  16. International Energy Agency (Ed.). (2015). World energy outlook 2016. Paris: OECD.Google Scholar
  17. Ishizaka, A., Nemery, P. (2013). Multi-criteria decision analysis: Methods and software. Wiley.Google Scholar
  18. Ishizaka, A., Siraj, S., & Nemery, P. (2016). Which energy mix for the UK (United Kingdom)? An evolutive descriptive mapping with the integrated GAIA (graphical analysis for interactive aid)–AHP (analytic hierarchy process) visualization tool. Energy, 95, 602–611.  https://doi.org/10.1016/j.energy.2015.12.009.CrossRefGoogle Scholar
  19. Işıklar, G., & Büyüközkan, G. (2007). Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Computer Standards & Interfaces, 29, 265–274.  https://doi.org/10.1016/j.csi.2006.05.002.CrossRefGoogle Scholar
  20. Iskin, I., Daim, T., Kayakutlu, G., & Altuntas, M. (2012). Exploring renewable energy pricing with analytic network process—Comparing a developed and a developing economy. Energy Economics, 34, 882–891.  https://doi.org/10.1016/j.eneco.2012.04.005.CrossRefGoogle Scholar
  21. 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.  https://doi.org/10.1016/j.enconman.2013.11.036.CrossRefGoogle Scholar
  22. 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, 1603–1616.  https://doi.org/10.1016/j.energy.2009.07.008.CrossRefGoogle Scholar
  23. Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8, 637–666.  https://doi.org/10.1080/18756891.2015.1046325.CrossRefzbMATHGoogle Scholar
  24. Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38, 6577–6585.  https://doi.org/10.1016/j.eswa.2010.11.081.CrossRefGoogle Scholar
  25. Kumar, A., Sah, B., Singh, A. R., et al. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609.  https://doi.org/10.1016/j.rser.2016.11.191.CrossRefGoogle Scholar
  26. Marchais-Roubelat, A., & Roubelat, F. (2011). The Delphi method as a ritual: Inquiring the Delphic Oracle. Technological Forecasting and Social Change, 78, 1491–1499.CrossRefGoogle Scholar
  27. Onar, S. Ç., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy QFD approach: An application to computer workstation selection. Applied Soft Computing, 46, 1–16.CrossRefGoogle Scholar
  28. Önüt, S., Tuzkaya, U. R., & Saadet, N. (2008). Multiple criteria evaluation of current energy resources for Turkish manufacturing industry. Energy Conversion and Management, 49, 1480–1492.  https://doi.org/10.1016/j.enconman.2007.12.026.CrossRefGoogle Scholar
  29. 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, 187–197.  https://doi.org/10.1080/18756891.2014.963987.CrossRefGoogle Scholar
  30. Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews, 8, 365–381.  https://doi.org/10.1016/j.rser.2003.12.007.CrossRefGoogle Scholar
  31. REN 21. (2017). Renewables 2017 Global Status Report. REN 21, Paris, France.Google Scholar
  32. Rodriguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20, 109–119.  https://doi.org/10.1109/tfuzz.2011.2170076.CrossRefGoogle Scholar
  33. Rodríguez, R. M., Martı́nez, L., & Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28–42.  https://doi.org/10.1016/j.ins.2013.04.006.
  34. Şengül, Ü., Eren, M., Eslamian Shiraz, S., et al. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy, 75, 617–625.  https://doi.org/10.1016/j.renene.2014.10.045.CrossRefGoogle Scholar
  35. Senvar, O., Otay, I., & Bolturk, E. (2016). Hospital site selection via hesitant fuzzy TOPSIS. IFAC-PapersOnLine, 49, 1140–1145.  https://doi.org/10.1016/j.ifacol.2016.07.656.CrossRefGoogle Scholar
  36. Suganthi, L., Iniyan, S., & Samuel, A. A. (2015). Applications of fuzzy logic in renewable energy systems—A review. Renewable and Sustainable Energy Reviews, 48, 585–607.  https://doi.org/10.1016/j.rser.2015.04.037.CrossRefGoogle Scholar
  37. 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 (pp. 17–30). London: Springer.CrossRefGoogle Scholar
  38. Tapia Garcı´a, J. M., del Moral, M. J., Martínez, M. A., Herrera-Viedma, E. (2012). A consensus model for group decision making problems with linguistic interval fuzzy preference relations. Expert Systems with Applications, 39, 10022–10030.  https://doi.org/10.1016/j.eswa.2012.02.008.
  39. Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25, 529–539.  https://doi.org/10.1002/int.20418.zbMATHGoogle Scholar
  40. Torra, V., Narukawa, Y. (2009). On hesitant fuzzy sets and decision. In: 2009 IEEE International Conference on Fuzzy Systems, pp. 1378–1382.Google Scholar
  41. Wang, J.-J., Jing, Y.-Y., Zhang, C.-F., & Zhao, J.-H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13, 2263–2278.  https://doi.org/10.1016/j.rser.2009.06.021.CrossRefGoogle Scholar
  42. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.MathSciNetCrossRefzbMATHGoogle Scholar
  43. Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37, 4938–4947.  https://doi.org/10.1016/j.apm.2012.10.002.MathSciNetCrossRefGoogle Scholar
  44. Zhang, Y., Xie, A., & Wu, Y. (2015). A hesitant fuzzy multiple attribute decision making method based on linear programming and TOPSIS**This work was supported by the Specialized Research Fund for the Doctoral Program of Higher Education under Project No. 20130009120040. IFAC-PapersOnLine, 48, 427–431.  https://doi.org/10.1016/j.ifacol.2015.12.165.CrossRefGoogle Scholar
  45. Zhou, H., Wang, J., Zhang, H., & Chen, X. (2016). Linguistic hesitant fuzzy multi-criteria decision-making method based on evidential reasoning. International Journal of Systems Science, 47, 314–327.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gülçin Büyüközkan
    • 1
  • Yağmur Karabulut
    • 2
  • Merve Güler
    • 1
  1. 1.Department of Industrial EngineeringGalatasaray UniversityIstanbulTurkey
  2. 2.Mavi ConsultantsUskudar/IstanbulTurkey

Personalised recommendations