Arabian Journal for Science and Engineering

, Volume 44, Issue 8, pp 7235–7247 | Cite as

A Hybrid Hesitant Fuzzy Decision-Making Approach for Evaluating Solar Power Plant Location Sites

  • Ahmet AktasEmail author
  • Mehmet Kabak
Research Article - Systems Engineering


Investments in energy projects require careful consideration, since inappropriate decisions can lead investors to waste finances, time and other resources. This paper will present the position that decisions for energy projects should be made after considerable, thoughtful and appropriate decision research and analyses. Due to the different aspects related to investment decisions in energy projects, using multi-criteria decision-making approaches is deemed to be a sensible approach to better ensure an appropriate course of action. In this study, the decision regarding location of a solar power plant, a critical issue pertaining to solar energy investments, is considered. Since solar power plant location decisions consist of conflicting criteria and varying possible locations, the solar power plant location problem is considered in this study. The main aim of this study is to propose a decision-making approach for solar power plant location problem which is integrating “Analytic Hierarchy Process” and “Technique for Order Preference by Similarity to Ideal Solution” methods under hesitant fuzzy environment. The applicability of the proposed approach is then tested on a case study. Besides the innovative contributions to industry literature, the results of the case study demonstrate that the proposed approach is applicable for the challenge of determining plant locations.


Solar power plant location problem Hesitant fuzzy sets Analytic hierarchy process Technique for order preference by similarity to ideal solution 


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  1. 1.
    Lambert, J.G.; Hall, C.A.S.; Balogh, S.; Gupta, A.; Arnold, M.: Energy, EROI and quality of life. Energy Policy 64, 153–167 (2014)CrossRefGoogle Scholar
  2. 2.
    Panwar, N.L.; Kaushik, S.C.; Kothari, S.: Role of renewable energy sources in environmental protection: a review. Renew. Sustain. Energy Rev. 15, 1513–1524 (2011)CrossRefGoogle Scholar
  3. 3.
    Siddique, M.N.; Ahmad, A.; Nawaz, M.K.; Bukhari, S.B.A.: Optimal integration of hybrid (wind-solar) system with diesel power plant using HOMER. Turk. J. Electr. Eng. Comput. Sci. 23, 1547–1557 (2015)CrossRefGoogle Scholar
  4. 4.
    Louhi, J.T.: Energy efficiency of modern cellular base stations. In: INTELEC 07-29th International Telecommunications Energy Conference, Rome, pp 475–476 (2007)Google Scholar
  5. 5.
    Büyüközkan, G.; Güleryüz, S.: Evaluation of renewable energy resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations. Energy 123, 149–163 (2017)CrossRefGoogle Scholar
  6. 6.
    Janjic, A.; Savic, S.; Velimirovic, L.; Nikolic, V.: Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process. Turk. J. Electr. Eng. Comput. Sci. 23, 1896–1912 (2015)CrossRefGoogle Scholar
  7. 7.
    Uyan, M.: GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renew. Sustain. Energy Rev. 28, 11–17 (2013)CrossRefGoogle Scholar
  8. 8.
    Rodriguez, R.M.; Martinez, L.; Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–11 (2014)CrossRefGoogle Scholar
  9. 9.
    Badri, M.A.: Combining the analytic hierarchy process and goal programming for global facility location–allocation problem. Int. J. Prod. Econ. 62, 237–248 (1999)CrossRefGoogle Scholar
  10. 10.
    Carrion, J.A.; Estrella, A.E.; Dols, F.A.; Toro, M.Z.; Rodriguez, M.; Ridao, A.R.: Environmental decision-support systems for evaluating the carrying capacity of land areas: optimal site selection for grid-connected photovoltaic power plants. Renew. Sustain. Energy Rev. 12, 2358–2380 (2008)CrossRefGoogle Scholar
  11. 11.
    Önüt, S.; Efendigil, T.; Kara, S.S.: A combined fuzzy MCDM approach for selecting shopping center site: an example from Istanbul, Turkey. Expert Syst. Appl. 37(3), 1973–1980 (2010)CrossRefGoogle Scholar
  12. 12.
    Lin, C.T.; Tsai, M.C.: Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS. Qual. Quant. 44(2), 375–390 (2010)CrossRefGoogle Scholar
  13. 13.
    Kuo, M.S.; Liang, G.S.: A novel hybrid decision-making model for selecting locations in a fuzzy environment. Math. Comput. Model. 54(1), 88–104 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Mokhtarian, M.N.; Hadi-Vencheh, A.: A new fuzzy TOPSIS method based on left and right scores: an application for determining an industrial zone for dairy products factory. Appl. Soft Comput. 12(8), 2496–2505 (2012)CrossRefGoogle Scholar
  15. 15.
    Liu, H.C.; You, J.X.; Chen, Y.Z.; Fan, X.J.: Site selection in municipal solid waste management with extended VIKOR method under fuzzy environment. Environ. Earth Sci. 72(10), 4179–4189 (2014)CrossRefGoogle Scholar
  16. 16.
    Sánchez-Lozano, J.M.; Antunes, C.H.; García-Cascales, M.S.C.; Dias, L.C.: GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renew. Energy 66, 478–494 (2014)CrossRefGoogle Scholar
  17. 17.
    Zavadskas, E.K.; Turskis, Z.; Bagočius, V.: Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Appl. Soft Comput. 26, 180–192 (2015)CrossRefGoogle Scholar
  18. 18.
    Lee, A.H.I.; Kang, H.Y.; Lin, C.Y.; Shen, K.C.: An integrated decision-making model for the location of a PV solar plant. Sustainability-Basel 7, 13522–13541 (2014)CrossRefGoogle Scholar
  19. 19.
    Çetinkaya, C.; Özceylan, E.; Erbaş, M.; Kabak, M.: GIS-based fuzzy MCDA approach for siting refugee camp: a case study for southeastern Turkey. Int. J. Disaster Risk Reduct. 18, 218–231 (2016)CrossRefGoogle Scholar
  20. 20.
    Erdogan, M.; Kaya, I.: A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Appl. Soft Comput. 39, 84–93 (2016)CrossRefGoogle Scholar
  21. 21.
    Baušys, R.; Juodagalviene, B.: Garage location selection for residential house by WASPAS-SVNS method. J. Civ. Eng. Manag. 23(3), 421–429 (2017)CrossRefGoogle Scholar
  22. 22.
    Kabak, M.; Keskin, İ.: Hazardous materials warehouse selection based on GIS and MCDM. Arab. J. Sci. Eng. 43(6), 3269–3278 (2018)CrossRefGoogle Scholar
  23. 23.
    Deveci, M.; Canıtez, F.; Gökaşar, I.: WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustain. Cities Soc. 41, 777–791 (2018)CrossRefGoogle Scholar
  24. 24.
    Kahraman, C.; Kaya, I.; Cebi, S.: A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 34, 1603–1616 (2009)CrossRefGoogle Scholar
  25. 25.
    Kabak, M.; Köse, E.; Kırılmaz, O.; Burmaoglu, S.: A fuzzy multi-criteria decision making approach to assess building energy performance. Energy Build. 72, 382–389 (2014)CrossRefGoogle Scholar
  26. 26.
    Kahraman, C.; Onar, S.C.; Cebi, S.; Oztaysi, B.: Extension of information axiom from ordinary to intuitionistic fuzzy sets: an application to search algorithm selection. Comput. Ind. Eng. 105, 348–361 (2017)CrossRefGoogle Scholar
  27. 27.
    Zhang, N.; Wei, G.: Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Appl. Math. Model. 37, 4938–4947 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)zbMATHGoogle Scholar
  29. 29.
    Khutsishvili, I.; Sirbiladze, G.; Tsulaia, G.: Hesitant fuzzy MADM approach in optimal selection of investment projects, EPiC series in computer science volume 36—Proceedings of the First Global Conference on Artificial Intelligence (GCAI 2015), Tbilisi, Georgia, pp 151–162 (2015)Google Scholar
  30. 30.
    Tsaur, S.H.; Chang, T.Y.; Yen, C.H.: The evaluation of airline service quality by fuzzy MCDM. Tour. Manag. 23, 107–115 (2002)CrossRefGoogle Scholar
  31. 31.
    Dagdeviren, M.; Yavuz, S.; Kilinc, N.: Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst. Appl. 36, 8143–8151 (2009)CrossRefGoogle Scholar
  32. 32.
    Lin, M.C.; Wang, C.C.; Chen, M.S.; Chang, C.A.: Using AHP and TOPSIS approaches in customer-driven product design process. Comput. Ind. 59, 17–31 (2008)CrossRefGoogle Scholar
  33. 33.
    Yavuz, M.; Oztaysi, B.; Onar, S.C.; Kahraman, C.: Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Syst. Appl. 42, 2835–2848 (2015)CrossRefGoogle Scholar
  34. 34.
    Xu, Z.; Zhang, X.: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowl.-Based Syst. 52, 53–64 (2013)CrossRefGoogle Scholar
  35. 35.
    Kuo, T.: A modified TOPSIS with a different ranking index. Eur. J. Oper. Res. 260, 152–160 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Hwang, C.L.; Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981)CrossRefzbMATHGoogle Scholar
  37. 37.
    Xu, Z.; Xia, M.: On distance and correlation measures of hesitant fuzzy information. Int. J. Intell. Syst. 26, 410–425 (2011)CrossRefzbMATHGoogle Scholar
  38. 38.
    REN21 Secretariat. Renewables 2016 Global Status Report. Paris, France (2016)Google Scholar
  39. 39.
    Uluatam, E.: Yenilenebilir Enerji Teşvikleri. Ekonomik Forum; Ekim: 34–41 (2010)Google Scholar
  40. 40.
    Wu, Y.; Geng, S.: Multi-criteria decision making on selection of solar-wind hybrid power station location: a case of China. Energy Convers. Manag. 81, 527–533 (2014)CrossRefGoogle Scholar
  41. 41.
    Boran, F.E.; Menlik, T.; Boran, K.: Multi-criteria axiomatic design approach to evaluate sites for grid-connected photovoltaic power plants: a case study in Turkey. Energy Source Part B 5(3), 290–300 (2010)CrossRefGoogle Scholar
  42. 42.
    Wu, Y.; Geng, S.; Zhang, H.; Gao, M.: Decision framework of solar thermal power plant site selection based on linguistic Choquet operator. Appl. Energy 136, 303–311 (2014)CrossRefGoogle Scholar
  43. 43.
    Tahri, M.; Hakdaoui, M.; Maanan, M.: The evaluation of solar farm locations applying geographic information system and multi-criteria decision-making methods: case study in southern Morocco. Renew. Sustain. Energy Rev. 51, 1354–1362 (2015)CrossRefGoogle Scholar
  44. 44.
    Watson, J.J.W.; Hudson, M.D.: Regional scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landsc. Urban Plan. 138, 20–31 (2015)CrossRefGoogle Scholar
  45. 45.
    Xiao, J.; Yao, Z.; Qu, J.; Sun, J.: Research on an optimal site selection model for desert photovoltaic power plants based on analytic hierarchy process and geographic information system. J. Renew. Sustain. Energy 5(2), 023132 (2014)CrossRefGoogle Scholar
  46. 46.
    Chen, C.R.; Huang, C.C.; Tsuei, H.J.: A hybrid MCDM model for improving GIS-based solar farms site selection. Int. J. Photoenergy; Article ID: 925370 (2014)Google Scholar
  47. 47.
    T.C. Başbakanlık Türkiye Yatırım Destek ve Tanıtım Ajansı. “Yatırım Teşvikleri”. Teşvikler—Invest in Turkey. Accessed Sept 19, 2017

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringGazi UniversityAnkaraTurkey

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