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A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information


  • Mousavi, M.
  • Gitinavard, H.
  • Mousavi, S.M.


In recent years, the selection of suitable renewable energy policy is very significant issue that could affect on environment and economic development. To address the issue, some researchers have focused on choosing the best renewable energy alternative by utilizing the decision-making analysis and fuzzy sets theory. In this paper, a new decision model based on modified elimination and choice translating reality (ELECTRE) is presented under a hesitant fuzzy environment for solving the multi-attribute group decision-making (MAGDM) problems in energy sector. Hesitant fuzzy set (HFS) is a powerful tool to cope with uncertainty in case of hesitant and incomplete information by considering some membership degrees for an energy alternative versus an evaluation criterion (attribute) under a set. In this model, a group of energy experts is provided to assess the potential alternatives among the conflicted attributes or criteria. Also, the decision matrix and relative importance of each attribute are considered by linguistic terms that can be transformed to hesitant fuzzy elements. In addition, the relative importance of each energy decision maker (DM) or expert is computed by proposed hesitant fuzzy modified preferences selection index (HF-M-PSI) method. Also, the significance of attributes is determined by an extended maximizing deviation method which is motivated by hesitant fuzzy Euclidean-Hausdorff distance measure. In this regard, opinions of each energy expert are applied to extend maximizing deviation method. Then, weights of attributes and experts are considered in the proposed hesitant fuzzy modified-ELECTRE (HF-M-ELECTRE) model. In the proposed decision model, the hesitant fuzzy effective outranking matrix may not help to rank the energy candidates. Thus, the proposed soft computing approach takes account of the thresholds as indifference, preference and veto for each attribute to compare the equivalent alternative. Finally, two real case studies in developing countries on renewable energy policy selection problem are presented to indicate the suitability and feasibility of the proposed HF-M-ELECTRE model in imprecise situations.

Suggested Citation

  • Mousavi, M. & Gitinavard, H. & Mousavi, S.M., 2017. "A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 774-787.
  • Handle: RePEc:eee:rensus:v:68:y:2017:i:p1:p:774-787
    DOI: 10.1016/j.rser.2016.09.125

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    References listed on IDEAS

    1. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    2. Hatami-Marbini, Adel & Tavana, Madjid, 2011. "An extension of the Electre I method for group decision-making under a fuzzy environment," Omega, Elsevier, vol. 39(4), pages 373-386, August.
    3. repec:wsi:ijitdm:v:13:y:2014:i:01:n:s0219622014500035 is not listed on IDEAS
    4. Topcu, Y.I & Ulengin, F, 2004. "Energy for the future: An integrated decision aid for the case of Turkey," Energy, Elsevier, vol. 29(1), pages 137-154.
    5. Zamani, Mehrzad, 2007. "Energy consumption and economic activities in Iran," Energy Economics, Elsevier, vol. 29(6), pages 1135-1140, November.
    6. Cho, Sangmin & Kim, Jinsoo & Heo, Eunnyeong, 2015. "Application of fuzzy analytic hierarchy process to select the optimal heating facility for Korean horticulture and stockbreeding sectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1075-1083.
    7. repec:spr:grdene:v:22:y:2013:i:2:d:10.1007_s10726-011-9259-1 is not listed on IDEAS
    8. Goumas, M. & Lygerou, V., 2000. "An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects," European Journal of Operational Research, Elsevier, vol. 123(3), pages 606-613, June.
    9. Erdogmus, Senol & Aras, Haydar & Koç, Eylem, 2006. "Evaluation of alternative fuels for residential heating in Turkey using analytic network process (ANP) with group decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(3), pages 269-279, June.
    10. Afgan, Nain H. & Carvalho, Maria G., 2008. "Sustainability assessment of a hybrid energy system," Energy Policy, Elsevier, vol. 36(8), pages 2893-2900, August.
    11. Balezentiene, Ligita & Streimikiene, Dalia & Balezentis, Tomas, 2013. "Fuzzy decision support methodology for sustainable energy crop selection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 17(C), pages 83-93.
    12. Govindan, Kannan & Jepsen, Martin Brandt, 2016. "ELECTRE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 250(1), pages 1-29.
    13. P. L. Yu, 1973. "A Class of Solutions for Group Decision Problems," Management Science, INFORMS, vol. 19(8), pages 936-946, April.
    14. Burak Omer Saracoglu, 2013. "Selecting industrial investment locations in master plans of countries," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 7(4), pages 416-441.
    15. Kaya, Tolga & Kahraman, Cengiz, 2010. "Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul," Energy, Elsevier, vol. 35(6), pages 2517-2527.
    16. Singh, Rana Pratap & Nachtnebel, Hans Peter, 2016. "Analytical hierarchy process (AHP) application for reinforcement of hydropower strategy in Nepal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 43-58.
    17. Papadopoulos, Agis & Karagiannidis, Avraam, 2008. "Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems," Omega, Elsevier, vol. 36(5), pages 766-776, October.
    18. Lee, Seong Kon & Mogi, Gento & Hui, K.S., 2013. "A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 347-355.
    19. Keyhani, A. & Ghasemi-Varnamkhasti, M. & Khanali, M. & Abbaszadeh, R., 2010. "An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran," Energy, Elsevier, vol. 35(1), pages 188-201.
    20. Heo, Eunnyeong & Kim, Jinsoo & Boo, Kyung-Jin, 2010. "Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2214-2220, October.
    21. van de Kaa, Geerten & Rezaei, Jafar & Kamp, Linda & de Winter, Allard, 2014. "Photovoltaic technology selection: A fuzzy MCDM approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 662-670.
    22. Choudhary, Devendra & Shankar, Ravi, 2012. "An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India," Energy, Elsevier, vol. 42(1), pages 510-521.
    23. Kahraman, Cengiz & Kaya, İhsan & Cebi, Selcuk, 2009. "A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process," Energy, Elsevier, vol. 34(10), pages 1603-1616.
    24. repec:wsi:ijitdm:v:14:y:2015:i:03:n:s0219622014500187 is not listed on IDEAS
    25. Beccali, M. & Cellura, M. & Mistretta, M., 2003. "Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology," Renewable Energy, Elsevier, vol. 28(13), pages 2063-2087.
    26. Afgan, Naim H. & Carvalho, Maria G., 2002. "Multi-criteria assessment of new and renewable energy power plants," Energy, Elsevier, vol. 27(8), pages 739-755.
    27. Various, 1973. "Conference Programs," NBER Chapters,in: The New Realities of the Business Cycle, pages 126-131 National Bureau of Economic Research, Inc.
    28. Şengül, Ümran & Eren, Miraç & Eslamian Shiraz, Seyedhadi & Gezder, Volkan & Şengül, Ahmet Bilal, 2015. "Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey," Renewable Energy, Elsevier, vol. 75(C), pages 617-625.
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    Cited by:

    1. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    2. repec:eee:rensus:v:80:y:2017:i:c:p:1544-1577 is not listed on IDEAS

    More about this item


    Renewable energy policy selection; Hesitant fuzzy sets; Modified-ELECTRE method; Modified-PSI method; Group decision making;

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • P18 - Economic Systems - - Capitalist Systems - - - Energy; Environment
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling


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