Published Dec 6, 2018
Cengiz Kahraman Irem Otay


One of the most used renewable energy systems to produce clean and sustainable energy are solar energy photovoltaic (PV) plants. The selection among solar energy PV plant location alternatives requires a multi-criteria decision making approach with several conflicting and linguistic criteria. The assessment process is generally done in a vague and imprecise environment. Fuzzy set theory is often very beneficial for evaluating the subjective judgments of decision makers. The Analytic Hierarchy Process is the most used multi-criteria decision making method in the world because of its simplicity and efficiency. In this paper, we select a location for a solar energy PV plant using a 4-level hierarchy. We consider several criteria and sub-criteria including initial cost, maintenance cost, slope and distance to highways. A Z-fuzzy number is a relatively new concept in fuzzy set theory that enables one to circumvent the limitations of ordinary fuzzy numbers. Z-fuzzy numbers can be viewed as a combination of crisp numbers, intervals, fuzzy numbers and random numbers because of their generality. They give a better representation than ordinary fuzzy numbers. This study solves the multi-criteria solar PV power plant location selection problem with a Z-fuzzy based AHP method. To check the applicability of the method proposed here, a real-life case study from Turkey is presented and solved.


Download data is not yet available.



Solar PV power plant, Location selection, fuzzy AHP, Z-fuzzy number, multi-criteria, uncertainty.

Abdel-Basset, M., Mohamed. M., & Sangaiah A.K. (2017). Neutrosophic AHP-Delphi group decision making model based on trapezoidal neutrosophic numbers. Journal of Ambient Intelligence and Humanized Computing, 1-17. Doi: https://doi.org/10.1007/s12652-017-0548-7

Abdullah, L., & Najib. L. (2014a). A new preference scale MCDM method based on interval-valued intuitionistic fuzzy sets and the Analytic Hierarchy Process, Soft Computing. 1-13. Doi: https://doi.org/10.1007/s00500-014-1519-y

Abdullah, L., & Najib. L. (2014b). Sustainable energy planning decision using the intuitionistic fuzzy Analytic Hierarchy Process: choosing energy technology in Malaysia. International Journal of Sustainable Energy, 35(4), 360-377. Doi: https://doi.org/10.1080/14786451.2014.907292

Abdullah, L., & Najib. L. (2016). Integration of interval Type-2 fuzzy sets and Analytic Hierarchy Process: Implication to computational procedures. AIP Conference Proceedings, 1750, 020019. Doi: https://doi.org/10.1063/1.4954532

Abdullah, L., Jaafar. S., & Taib. I. (2009). A new Analytic Hierarchy Process in multi-attribute group decision making. International Journal of Soft Computing, 4(5), 208-214.

Abu Bakar, A.S., Gegov, A. (2015). Multi-layer decision methodology for ranking Z-numbers. International Journal of Computational Intelligence Systems, 8(2), 395-406. Doi: http://dx.doi.org/10.1080/18756891.2015.1017371

Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87-96. Doi: https://doi.org/10.1016/S0165-0114(86)80034-3

Biswas, R. (2012). Fuzzy numbers redefined. Information, 15(4), 1369-1380.

Biswas, R. (2016). Is ‘fuzzy theory’ an appropriate tool for large size decision problems? Studies in Fuzziness and Soft Computing, 332, 93-118. Doi: https://doi.org/10.1007/978-3-319-26302-1_8

Boender, C.G.E., De Graan. J.G., & Lootsma. F.A. (1989). Multicriteria decision analysis with fuzzy pairwise comparisons. Fuzzy Sets and Systems, 29(2), 133-143. Doi: https://doi.org/10.1016/0165-0114(89)90187-5

Buckley, J.J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. Doi: https://doi.org/10.1016/0165-0114(85)90090-9

Chang, D.Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. Doi: https://doi.org/10.1016/0377-2217(95)00300-2

Cheng, C.H. (1997). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 96(2), 343-350. Doi: https://doi.org/10.1016/S0377-2217(96)00026-4

Deng, Y., Chan, F. T. S. (2011). A new fuzzy dempster MCDM method and its application in supplier selection. Expert Systems with Applications, 38, 9854-986. Doi: https://doi.org/10.1016/j.eswa.2011.02.017

Dutta. B., & Guha. D. (2015). Preference programming approach for solving intuitionistic fuzzy AHP. International Journal of Computational Intelligence Systems, 8(5), 977-991. Doi: http://dx.doi.org/10.1080/18756891.2015.1099904

Feng, X., Qian. X., & Wu. Q. (2012). A DS-AHP approach for multi-attribute decision making problem with intuitionistic fuzzy information. Information Technology Journal, 11(12), 1764-1769. Doi: http://dx.doi.org/10.3923/itj.2012.1764.1769

Garni, H.Z.A., & Awasthi. A. (2018). Solar PV power plants site selection. In I. Yahyaoui (Ed.) A review advances in renewable energies and power technologies, Volume 1: Solar and wind energies (57-75). Elsevier Science. Doi: https://doi.org/10.1016/B978-0-12-812959-3.00002-2

Ilbahar, E., Kara?an, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124-136. Doi: https://doi.org/10.1016/j.ssci.2017.10.025

Kahraman, C., Çevik Onar, S., & Öztay?i, B. (2017). B2C Marketplace prioritization using hesitant fuzzy linguistic AHP. International Journal of Fuzzy Systems, 20(7), 2202-2215. Doi: https://doi.org/10.1007/s40815-017-0429-4

Kahraman, C., Öztay?i, B., Sar?, ?.U. & Turano?lu, E. (2014). Fuzzy Analytic Hierarchy Process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48-57. Doi: https://doi.org/10.1016/j.knosys.2014.02.001

Kaur, P. (2014). Selection of vendor based on intuitionistic fuzzy Analytical Hierarchy Process. Advances in Operations Research, 2014, 1-10. Doi: http://dx.doi.org/10.1155/2014/987690.

Keshavarzfard, R.. & Makui, A. (2015). An IF-DEMATEL-AHP based on triangular intuitionistic fuzzy numbers (TIFNs). Decision Science Letters, 4(2), 237-246. Doi: 10.5267/j.dsl.2014.11.002
Khan, G., Rathi, S. (2014). Optimal site selection for solar PV power plant in an Indian state using Geographical Information System (GIS). International Journal of Emerging Engineering Research and Technology, 2(7), 260-266.

Kilic, M., & Kaya, I. (2015). Investment project evaluation by a decision making methodology based on type-2 fuzzy sets. Applied Soft Computing, 27, 399-410. Doi: https://doi.org/10.1016/j.asoc.2014.11.028

Li, C., Sun, Y.. & Du, Y. (2008). Selection of 3PL service suppliers using a fuzzy Analytic Network Process. Control and Decision Conference, 2008, CCDC 2008, China, 2174-2179. Doi: https://doi.org/10.1109/CCDC.2008.4597709

Mikhailov, L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, 134(3), 365-385. Doi: https://doi.org/10.1016/S0165-0114(02)00383-4

Onar, S. C., Oztaysi, B., Otay, ?., & Kahraman, C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90, 274-285. Doi: https://doi.org/10.1016/j.energy.2015.06.086

Ozdemir, S., Sahin, G. (2018). Multi-criteria decision-making in the location selection for a solar PV power plant using AHP. Measurement, 129, 218-226. Doi: https://doi.org/10.1016/j.measurement.2018.07.020

Oztaysi, B., Cevik Onar, S., Bolturk, E.. & Kahraman, C. (2015). Hesitant fuzzy Analytic Hierarchy Process, IEEE International Conference on Fuzzy Systems, 1-7. Doi: https://doi.org/10.1109/FUZZ-IEEE.2015.7337948

Radwan, N.M., Senousy, M.B., & Riad, A.E.D.M. (2016). Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system. International Journal of Advancements in Computing Technology (IJACT), 8(5), 95-105.

Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy Analytic Hierarchy Process (IF-AHP). Stochastic Environmental Research and Risk Assessment, 23(1), 75-91. Doi: https://doi.org/10.1007/s00477-007-0197-z

Samanlioglu, F., Aya?, Z. (2017). A fuzzy AHP-PROMETHEE II approach for evaluation of solar power plant location alternatives in Turkey. Journal of Intelligent & Fuzzy Systems, 33(2), 859-871. Doi: 10.3233/JIFS-162122

Shura (2018). Increasing the share of renewables in Turkey’s power system: Options for transmission expansion and flexibility (https://www.shura.org.tr/wp-content/uploads/2018/05/Grid-Study-eng.pdf Visited on 01.09.2018).

Smarandache, F. (1998). Neutrosophy, neutrosophic probability, set, and logic. Rehoboth: American Research Press.

Tüysüz, F. & ?im?ek, B. (2017). A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector. Complex & Intelligent Systems, 3(3), 167-175. Doi: https://doi.org/10.1007/s40747-017-0044-x

Van Laarhoven, P.J.M., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11(1), 199-227. Doi: https://doi.org/10.1016/S0165-0114(83)80082-7

Wang, H., Qian, G., & Feng, X. (2011). An intuitionistic fuzzy AHP based on synthesis of eigenvectors and its application. Information Technology Journal, 10(10), 1850-1866. Doi: http://dx.doi.org/10.3923/itj.2011.1850.1866

Wang, Y.M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186(2), 735-747. Doi: https://doi.org/10.1016/j.ejor.2007.01.050

Wu, J., Huang, H.B., & Cao, Q.W. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multicriteria decision making problems. Applied Mathematical Modelling, 37(24), 9898-9906. Doi: https://doi.org/10.1016/j.apm.2013.05.035

Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy Analytic Hierarchy Process. IEEE Transactions on Fuzzy Systems, 22(4), 749-761. Doi: https://doi.org/10.1109/TFUZZ.2013.2272585

Yager, R.R. (2013). Pythagorean fuzzy subsets. Proceedings of Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, Canada, 57-61. Doi: https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375

Yu, J.R., & Cheng, S.J. (2007). An integrated approach for deriving priorities in Analytic Network Process. European Journal of Operational Research, 180(3), 1427-1432. Doi: https://doi.org/10.1016/j.ejor.2006.06.005

Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(3), 199-249. Doi: https://doi.org/10.1016/0020-0255(75)90036-5

Zadeh, L.A. (2011). A note on Z-numbers. Information Sciences, 181, 2923-2932.

Zhang, C., Li. W., & Wang, L. (2011). AHP under the intuitionistic fuzzy environment, Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2011), 1, 583-587. Doi: https://doi.org/10.1109/FSKD.2011.6019593

Web references
1. https://www.rvo.nl/sites/default/files/2015/10/Renewable%20Energy%20Turkey.pdf (Visited on 01.09.2018)
2. http://www.pv-financing.eu/wp-content/uploads/2017/06/PV-Financing-webinar-IT-ES-TR-business-models-slides-170517.pdf (Visited on 10.09.2018)
3. http://www.enerji.gov.tr/en-US/Pages/Solar (Visited on 10.09.2018)
Special Topic Articles