A multi-criteria decision making for renewable energy selection using Z-numbers in uncertain environment
Abstract
In recent era of globalization, the world is perceiving an alarming rise in its energy consumption resulting in shortage of fossil fuels in near future. Developing countries like India, with fast growing population and economy, is planning to explore among its existing renewable energy sources to meet the acute shortage of overall domestic energy supply. For balancing diverse ecological, social, technical and economic features, selection among alternative renewable energy must be addressed in a multi-criteria context considering both subjective and objective criteria weights. In the proposed COPRAS-Z methodology, Z-number model fuzzy numbers with reliability degree to represents imprecise judgment of decision makers’ in evaluating the weights of criteria and selection of renewable energy alternatives. The fuzzy numbers are defuzzified and renewable energy alternatives are prioritized as per COmplex PropoRtional ASsessment (COPRAS) decision making method in terms of significance and utility degree. A sensitivity analysis is done to observe the variation in ranking of the criteria, by altering the coefficient of both subjective and objective weight. Also, the proposed methodology is compared with existing multi-criteria decision making (MCDM) methods for checking validity of the obtained ranking result.
Keyword : renewable energy, multi-criteria decision making (MCDM), COPRAS, Z number, fuzzy number
How to Cite
Chatterjee, K., & Kar, S. (2018). A multi-criteria decision making for renewable energy selection using Z-numbers in uncertain environment. Technological and Economic Development of Economy, 24(2), 739-764. https://doi.org/10.3846/20294913.2016.1261375
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Harish, V.; Kumar, A. 2014. Demand side management in India: action plan, policies and regulations, Renewable and Sustainable Energy Review 33: 613–624. https://doi.org/10.1016/j.rser.2014.02.021
Hirmer, S.; Cruickshank, H. 2014. The user-value of rural electrification: an analysis and adoption of existing models and theories, Renewable and Sustainable Energy Reviews 34: 145–154. https://doi.org/10.1016/j.rser.2014.03.005
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Kang, B.; Hu, Y.; Deng, Y.; Zhou, D. 2016. A new methodology of multicriteria decision-making in supplier selection based on Z-number, Mathematical Problems in Engineering vol. 2016: 17. https://doi.org/10.1155/2016/8475987
Kang, B.; Wei, D.; Li, Y.; Deng, Y. 2012a. Decision making using Z-numbers under uncertain environment, Journal of computational Information systems 8(7): 2807–2814 [online], [cited 18 March 2016]. Available from Internet: http://www/jofcis.com
Kang, B.; Wei, D.; Li, Y.; Deng, Y. 2012b. A method of converting Z-number to classical fuzzy number, Journal of Information and Computational Science 9(3): 703–709 [online], [cited 20 February 2016]. Available from Internet: http://www/joics.com
Kar, S.; Chatterjee, K. 2015. Supplier selection using ranking interval Type-2 fuzzy sets, in S. Satapathy, B. Biswal, S. Udgata, J. Mandal (Eds.). Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, 327: 9–17. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_2
Karakosta, C.; Doukas, H.; Psarras, J. 2010. Technology transfer through climate change: setting a sus-tainable energy pattern, Renewable and Sustainable Energy Reviews 14(6): 1546–1557. https://doi.org/10.1016/j.rser.2010.02.001
Kaya, T.; Kahraman, C. 2010. Multi-criteria renewable energy planning using an integrated Fuzzy VIKOR & AHP methodology: the case of Istanbul, Energy 35(6): 2517–2527. https://doi.org/10.1016/j.energy.2010.02.051
Keshavarz Ghorabaee, M.; Amiri, M.; Sadaghiani, J.; Goodarzi, G. 2014. Multiple criteria group decision-making for supplier selection based on COPRAS method with interval type-2 fuzzy sets, International Journal of Advanced Manuf Technology 75(5–8): 1115–1130. https://doi.org/10.1007/s00170-014-6142-7
Khare, V.; Nema, S.; Baredar, P. 2013. Status of solar wind renewable energy in India, Renewable and Sustainable Energy Reviews 27: 1–10. https://doi.org/10.1016/j.rser.2013.06.018
Kumar, A.; Kumar, K.; Kaushik, N.; Sharma, S; Mishra, S. 2010. Renewable energy in India: current status and future potentials, Renewable and Sustainable Energy Reviews 14(8): 2434–2442. https://doi.org/10.1016/j.rser.2010.04.003
Kumar, D.; Katoch, S. 2014. Sustainability indicators for run of the river (RoR) hydropower projects in hydro rich regions of India, Renewable and Sustainable Energy Reviews 35: 101–108. https://doi.org/10.1016/j.rser.2014.03.048
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Ma, J.; Fan, Z.; Huang, L. 1999. A subjective and objective integrated approach to determine attribute weights, European Journal of Operational Research 112: 397–404. https://doi.org/10.1016/S0377-2217(98)00141-6
Mahesh, A.; Shoba Jasmin, K. 2013. Role of renewable energy investment in India: an alternative to CO2 mitigation, Renewable and Sustainable Energy Reviews 26: 414–424. https://doi.org/10.1016/j.rser.2013.05.069
Melin, P.; Castillo, O. 2013. A review on the applications of type-2 fuzzy logic in classification and pattern recognition, Expert System with Applications 40(13): 5413–5423. https://doi.org/10.1016/j.eswa.2013.03.020
Mohamad, D.; Shaharani, S.; Kamis, N. 2014. A Z-number based decision making procedure with ranking fuzzy numbers method, in International Conference on Quantitative Sciences and it’s Applications (ICOQSIA 2014), 12–14 August 2014, Langkawi, Malaysia, AIP Conference Proceedings 1635: 160–166. https://doi.org/10.1063/1.4903578
Nguyen, N.; Duong, M.; Tran, T.; Shresth, R.; Nadaud, F. 2010. Barriers to the adoption of renewable and energy-efficient technologies in the Vietnamese power sector [online], [cited 20 March 2016]. CIRED Working Papers 2010-18, Halshs-00444826, 1–7. Available from Internet: http://halshs.archives-ourvertes.fr/halshs-00464675
Painuly, P. 2001. Barriers to renewable energy penetration – a framework for analysis, Renewable Energy 24(1): 73–89.
Pillai, R.; Banerjee, R. 2009. Renewable energy in India: status and potential, Energy 34(8): 970–980. https://doi.org/10.1016/j.energy.2008.10.016
Ratha, S.; Prasanna, R. 2012. Bio prospecting microalgae as potential sources of Green Energy – challenges and perspectives (Review), Applied Biochemistry and Microbiology 48(2): 109–125. https://doi.org/10.1134/S000368381202010X
Razavi Hajiagha, S.; Hashemi, S.; Zavadskas, E. 2013. A complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment, Technological and Economic Development of Economy 19(1): 22–37. https://doi.org/10.3846/20294913.2012.762953
Reddy, S.; Painuly, J. 2004. Diffusion of renewable energy technologies-barriers and stakeholders’ per-spectives, Renewable Energy 29: 1431–1447. https://doi.org/10.1016/j.renene.2003.12.003
Sawin, J. 2003. Charting a new energy future in State of the world 2003. Starke Linda (Ed.). New York: W.W. Norton & Company, 85–109.
Sengul, U.; Eren, M.; Shiraz, S.; Gezder, V.; Sengul, A. 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
Shannon, C. 1948. A mathematical theory of communication, Bell system Technical Journal 27(3): 379–592. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Shemshadi, A.; Shirazi, H.; Toreihi, M.; Tarokh, M. 2011. A fuzzy VIKOR method for supplier selection based on Entropy method for objective weighting, Expert Systems with Applications 38: 12160–12167. https://doi.org/10.1016/j.eswa.2011.03.027
Soroudi, A.; Amaraee, T. 2013. Decision making under uncertainty in energy systems: state of the art, Renewable and Sustainable Energy Reviews 28: 376–384. https://doi.org/10.1016/j.rser.2013.08.039
Srivastava, S.; Sharma, R. 2013. The future of energy in India, Journal of Petrotech VIII(8): 78–85.
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. https://doi.org/10.1016/j.seta.2014.02.008
Tavakkoli-Moghaddam, R.; Anvari, A.; Saidat, A. 2015. A multi-criteria group decision-making approach for facility location selection using PROMETHEE under a fuzzy environment, Outlooks and Insights on Group Decision and Negotiation, Lecture notes in Business Information Processing 218: 145–156. https://doi.org/10.1007/978-3-319-19515-5_12
Tsoutsos, T.; Frantzeskaki, N.; Gekas, V. 2005. Environmental impacts from the solar energy technologies, Energy Policy 33(3): 289–296.
Wang, J.; Jing, Y.; Zhang, C.; Zhao, J. 2009. Review on multi-criteria decision analysis aid in sustainable energy decision-making, Renewable and Sustainable Energy Reviews 13(9): 2263–2278. https://doi.org/10.1016/j.rser.2009.06.021
Wang, Y.; Yang, J.; Xu, D.; Chin, K. 2006. On the centroids of fuzzy numbers, Fuzzy Sets and Systems 157(7): 919–926. https://doi.org/10.1016/j.fss.2005.11.006
Wilkins, G. 2012. Technology transfer for renewable energy. India: CRC Press Ltd.
Xiao, Z. 2014. Application of Z-numbers in multi-criteria decision making, in IEEE International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), 9–10 October 2014, Shandong, China, 91–95. https://doi.org/10.1109/iccss.2014.6961822
Yaakob, A.; Gegov, A. 2016. Interactive TOPSIS based group decision making methodology using Z-number, International Journal of Computational Intelligence Systems 9(2): 311–324. https://doi.org/10.1080/18756891.2016.1150003
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