Clean Technologies and Environmental Policy

, Volume 20, Issue 2, pp 403–420 | Cite as

A novel integrated decision-making approach for the evaluation and selection of renewable energy technologies

  • Morteza Yazdani
  • Prasenjit Chatterjee
  • Edmundas Kazimieras Zavadskas
  • Dalia Streimikiene
Original Paper


The decision-making in energy sector involves finding a set of energy sources and conversion devices to meet the energy demands in an optimal way. Making an energy planning decision involves the balancing of diverse ecological, social, technical and economic aspects across space and time. Usually, technical and environmental aspects are represented in the form of multiple criteria and indicators that are often expressed as conflicting objectives. In order to attain higher efficiency in the implementation of renewable energy (RE) systems, the developers and investors have to deploy multi-criteria decision-making techniques. In this paper, a novel hybrid Decision Making Trial and Evaluation Laboratory and analytic network process (DEMATEL-ANP) model is proposed in order to stress the importance of the evaluation criteria when selecting alternative REs and the causal relationships between the criteria. Finally, complex proportional assessment and weighted aggregated sum product assessment methods are used to assess the performances of the REs with respect to different evaluating criteria. An illustrative example from Costs assessment of sustainable energy systems (CASES) project, financed by European Commission Framework 6 programme (EU FM 6) for EU member states is presented in order to demonstrate the application feasibility of the proposed model for the comparative assessment and ranking of RE technologies. Sensitivity analysis, result validation and critical outcomes are provided as well to offer guidelines for the policy makers in the selection of the best alternative RE with the maximum effectiveness.


Multi-criteria decision-making Renewable energy Decision Making Trial and Evaluation Laboratory Analytical network process 


  1. Ahmad S, Tahar RM (2014) Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: a case of Malaysia. Renew Energy 63:458–466CrossRefGoogle Scholar
  2. Aras H, Erdoğmuş Ş, Koç E (2004) Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renew Energy 29(8):1383–1392CrossRefGoogle Scholar
  3. Bagocius V, Zavadskas EK, Turskis Z (2014) Multi-person selection of the best wind turbine based on the multi-criteria integrated additive-multiplicative utility function. J Civ Eng Manag 20(4):590–599CrossRefGoogle Scholar
  4. Balezentiene L, Streimikiene D, Balezentis T (2013) Fuzzy decision support methodology for sustainable energy crop selection. Renew Sustain Energy Rev 17:83–93CrossRefGoogle Scholar
  5. CASES (2008a) Cost Assessment of Sustainable Energy System. Development of a set of full cost estimates of the use of different energy sources and its comparative assessment in EU countriesGoogle Scholar
  6. CASES (2008b) Cost Assessment of Sustainable Energy System. Report on policy instruments assessment methods and comparative analysesGoogle Scholar
  7. Chatterjee P, Athawale VM, Chakraborty S (2011) Materials selection using complex proportional assessment and evaluation of mixed data methods. Mater Des 32(2):851–860CrossRefGoogle Scholar
  8. Chatterjee P, Mondal S, Boral S, Banerjee A, Chakraborty S (2017) A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attributive border approximation method. Facta Univ Ser Mech Eng 15(3):439–456CrossRefGoogle Scholar
  9. Chen JK, Chen IS (2010) Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Syst Appl 37(3):1981–1990CrossRefGoogle Scholar
  10. Diakoulaki D, Karangelis F (2007) Multi-criteria decision analysis and cost–benefit analysis of alternative scenarios for the power generation sector in Greece. Renew Sustain Energy Rev 11(4):716–727CrossRefGoogle Scholar
  11. Ertay T, Kahraman C, Kaya İ (2013) Evaluation of renewable energy alternatives using MACBETH and fuzzy AHP multi-criteria methods: the case of Turkey. Technol Econ Dev Econ 19(1):38–62CrossRefGoogle Scholar
  12. EUSUSTEL (2007) European Sustainable Electricity; Comprehensive Analysis of Future European Demand and Generation of European Electricity and Its Security of Supply. Final technical reportGoogle Scholar
  13. Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, GenevaGoogle Scholar
  14. Georgiou D, Mohammed ES, Rozakis S (2015) Multi-criteria decision making on the energy supply configuration of autonomous desalination units. Renew Energy 75:459–467CrossRefGoogle Scholar
  15. Hashemkhani Zolfani S, Aghdaie Mohammad H, Derakhti A, Zavadskas EK, Varzandeh MHM (2013) Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating. Expert Syst Appl 40(17):7111–7121CrossRefGoogle Scholar
  16. Hiremath RB, Shikha S, Ravindranath NH (2007) Decentralized energy planning; modeling and application: a review. Renew Sustain Energy Rev 11:729–752CrossRefGoogle Scholar
  17. Hsu CH, Wang FK, Tzeng GH (2012) The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR. Resour Conserv Recycl 66:95–111CrossRefGoogle Scholar
  18. Hu SK, Lu MT, Tzeng GH (2014) Exploring smart phone improvements based on a hybrid MCDM model. Expert Syst Appl 41(9):4401–4413CrossRefGoogle Scholar
  19. Ignatius J, Rahman A, Yazdani M, Šaparauskas J, Haron SH (2016) An integrated fuzzy ANP–QFD approach for green building assessment. J Civ Eng Manag 22(4):551–563CrossRefGoogle Scholar
  20. Kabak M, Dağdeviren M (2014) Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Convers Manag 79:25–33CrossRefGoogle Scholar
  21. Kaya T, Kahraman C (2010) Multi-criteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: the case of Istanbul. Energy 35(6):2517–2527CrossRefGoogle Scholar
  22. Liu CH, Tzeng GH, Lee MH (2012) Improving tourism policy implementation the use of hybrid MCDM models. Tour Manag 33(2):413–426CrossRefGoogle Scholar
  23. Mardani A, Jusoh A, Nor KMD, Khalifah Z, Zakwan N, Valipour A (2015a) Multiple criteria decision-making techniques and their applications—a review of the literature from 2000 to 2014. Econ Res Ekon Istraz 28(1):516–571Google Scholar
  24. Mardani A, Jusoh A, Zavadskas EK, Cavallaro F, Khalifah Z (2015b) Sustainable and renewable energy: an overview of the application of multiple criteria decision making techniques and approaches. Sustainability 7(10):13947–13984CrossRefGoogle Scholar
  25. Mulliner E, Smallabone K, Maliene V (2013) An assessment of sustainable housing affordability using a multiple criteria decision making method. Omega 41(2):270–279CrossRefGoogle Scholar
  26. NEEDS (2005) New Energy Externalities Developments for Sustainability. Survey of criteria and indicatorsGoogle Scholar
  27. NEEDS (2006) New Energy Externalities Developments for Sustainability. Final report on technology foresight methodGoogle Scholar
  28. NEEDS (2007) New Energy Externalities Developments for Sustainability. Environmental, economic and social criteria and indicators for sustainability assessment of energy technologiesGoogle Scholar
  29. PSI (2003) Integrated Assessment of Sustainable Energy Systems in China—The China Energy Technology Program (CETP)—A Framework for Decision Support in the Electric Sector of Shandong ProvinceGoogle Scholar
  30. Roy B (1996) Multi-criteria methodology for decision aiding. Kluwer, DordrechtCrossRefGoogle Scholar
  31. Saaty TL (1996) The analytic network process: decision making with dependence and feedback; the organization and prioritization of complexity. RWS Publications, Pittsburgh, PAGoogle Scholar
  32. Saaty TL, Vargas LG (1998) Diagnosis with dependent symptoms: Bayes theorem and the analytic hierarchy process. Oper Res 46(4):491–502CrossRefGoogle Scholar
  33. Santoyo-Castelazo E, Azapagic A (2014) Sustainability assessment of energy systems: integrating environmental, economic and social aspects. J Clean Prod 80:119–138CrossRefGoogle Scholar
  34. Şengül Ü, Eren M, Shiraz SE, Gezder V, Şengül AB (2015) Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew Energy 75:617–625CrossRefGoogle Scholar
  35. Shyur HJ (2006) COTS evaluation using modified TOPSIS and ANP. Appl Math Comput 177(1):251–259Google Scholar
  36. Streimikienė D (2013) Assessment of energy technologies in electricity and transport sectors based on carbon intensity and costs. Technol Econ Dev Econ 19(4):606–620CrossRefGoogle Scholar
  37. Streimikiene D, Balezentis T, Krisciukaitienė I, Balezentis A (2012) Prioritizing sustainable electricity production technologies: MCDM approach. Renew Sustain Energy Rev 16(5):3302–3311CrossRefGoogle Scholar
  38. Tavana M, Yazdani M, Di Caprio D (2017) An application of an integrated ANP–QFD framework for sustainable supplier selection. Int J Logist Res Appl 20(3):254–275CrossRefGoogle Scholar
  39. Triantaphyllou E, Mann SH (1989) An examination of the effectiveness of multi-dimensional decision making methods: a decision-making paradox. Decis Support Syst 5(3):303–312CrossRefGoogle Scholar
  40. Troldborg M, Heslop S, Hough RL (2014) Assessing the sustainability of renewable energy technologies using multi-criteria analysis: suitability of approach for national-scale assessments and associated uncertainties. Renew Sustain Energy Rev 39:1173–1184CrossRefGoogle Scholar
  41. Tupėnaitė L, Zavadskas EK, Kaklauskas A, Turskis Z, Seniut M (2010) Multiple criteria assessment of alternatives for built and human environment renovation. J Civ Eng Manag 16(2):257–266CrossRefGoogle Scholar
  42. Tzeng G, Chiang C, Li C (2007) Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Syst Appl 32(4):1028–1044CrossRefGoogle Scholar
  43. Wang YL, Tzeng GH (2012) Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert Syst Appl 39(5):5600–5615CrossRefGoogle Scholar
  44. Wu WW (2008) Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Syst Appl 35(3):828–835CrossRefGoogle Scholar
  45. Yazdani M, Graeml FR (2014) VIKOR and its applications: a state-of-the-art survey. Int J Strateg Decis Sci 5(2):56–83CrossRefGoogle Scholar
  46. Yazdani M, Payam AF (2015) A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS. Mater Des 65:328–334CrossRefGoogle Scholar
  47. Yazdani M, Chatterjee P, Zavadskas EK, Zolfani SH (2017) Integrated QFD-MCDM framework for green supplier selection. J Clean Prod 142:3728–3740CrossRefGoogle Scholar
  48. Yazdani-Chamzini A, Fouladgar MM, Zavadskas EK, Moini SHH (2013) Selecting the optimal renewable energy using multi criteria decision making. J Bus Econ Manag 14(5):957–978CrossRefGoogle Scholar
  49. Zavadskas EK, Kaklauskas A, Sarka V (1994) The new method of multi-criteria proportional assessment of projects. Technol Econ Dev Econ 1(3):355–374Google Scholar
  50. Zavadskas EK, Kaklauskas A, Turskis Z, Tamosaitiene J (2009) Multi-attribute decision-making model by applying grey numbers. Informatica 20(2):305–320Google Scholar
  51. Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektron Elektrotech Electron Electr Eng 122(6):3–6Google Scholar
  52. Zavadskas EK, Antucheviciene J, Saparauskas J, Turskis Z (2013) MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions. Econ Comput Econ Cybern Stud Res 47(2):5–20Google Scholar
  53. Zavadskas EK, Turskis Z, Kildiene S (2014) State of art surveys of overviews on MCDM/MADM methods. Technol Econ Dev Econ 20(1):165–179CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Social Sciences and CommunicationsUniversidad Europea de MadridMadridSpain
  2. 2.Mechanical Engineering DepartmentMCKV Institute of EngineeringHowrahIndia
  3. 3.Institute of Sustainable ConstructionVilnius Gediminas Technical UniversityVilniusLithuania
  4. 4.Lithuanian Energy InstituteKaunasLithuania

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