Wind Energy Investment Analyses Based on Fuzzy Sets

  • Cengiz Kahraman
  • Sezi Çevik Onar
  • Başar Öztayşi
  • İrem Uçal Sarı
  • Esra İlbahar
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)


Engineering economics deals with the investment decisions, where the investment parameters are very hard to estimate exactly. In the cases where we do not have the required data for parameter estimation, possibilistic approaches may be used. In this chapter, a brief literature review on wind energy investments is first presented. Later, the chapter gives present worth analysis (PWA) methods extended to fuzzy sets. The chapter introduces ordinary fuzzy PWA, type-2 fuzzy PWA, intuitionistic fuzzy PWA, and hesitant fuzzy PWA. A numerical application for each extension is presented.


  1. Al-Sharafi, A., Sahin, A. Z., Ayar, T., & Yilbas, B. S. (2017). Techno-economic analysis and optimization of solar and wind energy systems for power generation and hydrogen production in Saudi Arabia. Renewable and Sustainable Energy Reviews, 69, 33–49.CrossRefGoogle Scholar
  2. Aquila, G., Junior, P. R., de Oliveira Pamplona, E., & de Queiroz, A. R. (2017). Wind power feasibility analysis under uncertainty in the Brazilian electricity market. Energy Economics, 65, 127–136.CrossRefGoogle Scholar
  3. Ashkaboosi, M., Nourani, S. M., Khazaei, P., Dabbaghjamanesh, M., & Moeini, A. (2016). An optimization technique based on profit of investment and market clearing in wind power systems. American Journal of Electrical and Electronic Engineering, 4(3), 85–91.Google Scholar
  4. Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory. Berlin, Heidelberg: Springer.CrossRefzbMATHGoogle Scholar
  5. Atanassov, K.T. (1986), Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96 (1986).Google Scholar
  6. Aydin, N. Y., Kentel, E., & Duzgun, S. (2010). GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey. Renewable and Sustainable Energy Reviews, 14(1), 364–373.CrossRefGoogle Scholar
  7. Baringo, L., & Conejo, A. J. (2013). Risk-constrained multi-stage wind power investment. IEEE Transactions on Power Systems, 28(1), 401–411.CrossRefGoogle Scholar
  8. Caralis, G., Diakoulaki, D., Yang, P., Gao, Z., Zervos, A., & Rados, K. (2014). Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties. Renewable and Sustainable Energy Reviews, 40, 224–236.CrossRefGoogle Scholar
  9. Cavallaro, F., & Ciraolo, L. (2005). A multicriteria approach to evaluate wind energy plants on an Italian island. Energy Policy, 33(2), 235–244.CrossRefGoogle Scholar
  10. Celik, A. N. (2003). Energy output estimation for small-scale wind power generators using Weibull-representative wind data. Journal of Wind Engineering and Industrial Aerodynamics, 91(5), 693–707.CrossRefGoogle Scholar
  11. Chang, C. T. (2017). Fuzzy score technique for the optimal location of wind turbines installations. Applied Mathematical Modelling, 44, 576–587.MathSciNetCrossRefGoogle Scholar
  12. Chen, D., Zhang, L., & Jiao, J. (2010). Triangle fuzzy number intuitionistic fuzzy aggregation operators and their application to group decision making. In International Conference on Artificial Intelligence and Computational Intelligence AICI 2010 (pp. 350–357).Google Scholar
  13. Chiu, C. Y., & Park, C. S. (1994). Fuzzy cash flow analysis using present worth criterion. The Engineering Economist, 39(2), 113–138.CrossRefGoogle Scholar
  14. Cunico, M. L., Flores, J. R., & Vecchietti, A. (2017). Investment in the energy sector: An optimization model that contemplates several uncertain parameters. Energy, 138, 831–845.CrossRefGoogle Scholar
  15. Ersoz, S., Akinci, T. C., Nogay, H. S., & Dogan, G. (2013). Determination of wind energy potential in Kirklareli-Turkey. International Journal of Green Energy, 10(1), 103–116.CrossRefGoogle Scholar
  16. Fazelpour, F., Markarian, E., & Soltani, N. (2017). Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran. Renewable Energy, 109, 646–667.CrossRefGoogle Scholar
  17. Gumus, S., Kucukvar, M., & Tatari, O. (2016). Intuitionistic fuzzy multi-criteria decision making framework based on life cycle environmental, economic and social impacts: The case of US wind energy. Sustainable Production and Consumption, 8, 78–92.CrossRefGoogle Scholar
  18. Kahraman, C., Çevik Onar, S., & Oztaysi, B. (2015). Engineering economic analyses using intuitionistic and hesitant fuzzy sets. Journal of Intelligent & Fuzzy Systems, 29(3), 1151–1168.MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kahraman, C., Cevik Onar, S., & Oztaysi, B. (2016a). A comparison of wind energy investment alternatives using interval-valued intuitionistic fuzzy benefit/cost analysis. Sustainability, 8(2), 118.CrossRefGoogle Scholar
  20. Kahraman, C., Oztaysi, B., & Cevik Onar, S. (2016b). A comprehensive literature review of 50 years of fuzzy set theory. International Journal of Computational Intelligence Systems, 9(sup1), 3–24.CrossRefGoogle Scholar
  21. Kahraman, C., Sarı, İ. U., Onar, S. C., & Oztaysi, B. (2017). Fuzzy Economic analysis methods for environmental economics. In Intelligence Systems in Environmental Management: Theory and Applications (pp. 315–346). Berlin: Springer.Google Scholar
  22. Kitzing, L., Juul, N., Drud, M., & Boomsma, T. K. (2017). A real options approach to analyse wind energy investments under different support schemes. Applied Energy, 188, 83–96.CrossRefGoogle Scholar
  23. Kucukali, S. (2016). Risk scorecard concept in wind energy projects: An integrated approach. Renewable and Sustainable Energy Reviews, 56, 975–987.CrossRefGoogle Scholar
  24. Kumar, P. S., & Hussain, R. J. (2014). A method for solving balanced intuitionistic fuzzy assignment problem. International Journal of Engineering Research and Applications, 4(3), 897–903.Google Scholar
  25. Kuo-Ping, C. (2011). Multiple criteria group decision making with triangular interval type-2 fuzzy sets. In Proceedings of 2011 IEEE International Conference on Fuzzy Systems (FUZZ), Tapei (pp. 1098-7584), June 27–30, 2011.Google Scholar
  26. Lee, A. H., Chen, H. H., & Kang, H. Y. (2009). Multi-criteria decision making on strategic selection of wind farms. Renewable Energy, 34(1), 120–126.CrossRefGoogle Scholar
  27. Lee, S. C. (2011). Using real option analysis for highly uncertain technology investments: The case of wind energy technology. Renewable and Sustainable Energy Reviews, 15(9), 4443–4450.CrossRefGoogle Scholar
  28. Liu, X., & Zeng, M. (2017). Renewable energy investment risk evaluation model based on system dynamics. Renewable and Sustainable Energy Reviews, 73, 782–788.CrossRefGoogle Scholar
  29. Madlener, R., Glensk, B., & Weber, V. (2011). Fuzzy portfolio optimization of onshore wind power plants. FCN Working Papers 10/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), Revised Jul 2014.Google Scholar
  30. Mahapatra, G. S., & Roy, T. K. (2009). Reliability evaluation using triangular intuitionistic fuzzy numbers arithmetic operations. World Academy of Science, Engineering and Technology, 3(2), 422–429.MathSciNetGoogle Scholar
  31. Morshedizadeh, M., Kordestani, M., Carriveau, R., Ting, D. S.-K., & Saif, M. (2017). Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production. Energy, 138(C), 394–404. Elsevier.Google Scholar
  32. Niewiadomski, A., Ochelska, J., & Szczepaniak, P. S. (2006). Interval-valued linguistic summaries of databases. Control and Cybernetics, 35(2), 415–443.zbMATHGoogle Scholar
  33. Onar, S. C., & Kilavuz, T. N. (2015). Risk analysis of wind energy investments in Turkey. Human and Ecological Risk Assessment, 21, 1230–1245.Google Scholar
  34. Onar, S. C., Oztaysi, B., Otay, İ., & Kahraman, C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90, 274–285.CrossRefGoogle Scholar
  35. Onat, N., & Ersoz, S. (2011). Analysis of wind climate and wind energy potential of regions in Turkey. Energy, 36(1), 148–156.CrossRefGoogle Scholar
  36. Panduru, K. K., Riordan, D., & Walsh, J. (2014). Fuzzy logic based intelligent energy monitoring and control for renewable energy. In Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014). Google Scholar
  37. Petković, D. (2015). Adaptive neuro-fuzzy optimization of the net present value and internal rate of return of a wind farm project under wake effect. Journal of CENTRUM Cathedra: The Business and Economics Research Journal, 8(1), 11–28.Google Scholar
  38. Petković, D., Shamshirband, S., Kamsin, A., Lee, M., Anicic, O., & Nikolić, V. (2016). Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach. Renewable and Sustainable Energy Reviews, 57, 1270–1278.CrossRefGoogle Scholar
  39. Pinson, P., & Kariniotakis, G. N. (2003, June). Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment. In Power Tech Conference Proceedings, 2003 IEEE Bologna (Vol. 2, 8 pp). IEEE.Google Scholar
  40. Şen, Z., & Şahin, A. D. (1997). Regional assessment of wind power in western Turkey by the cumulative semivariogram method. Renewable Energy, 12(2), 169–177.CrossRefGoogle Scholar
  41. Shafiee, M. (2015). A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms. Expert Systems with Applications, 42(4), 2143–2152.MathSciNetCrossRefGoogle Scholar
  42. Shamshirband, S., Petković, D., Ćojbašić, Ž., Nikolić, V., Anuar, N. B., Shuib, N. L. M., et al. (2014). Adaptive neuro-fuzzy optimization of wind farm project net profit. Energy Conversion and Management, 80, 229–237.Google Scholar
  43. Sheen, J. N. (2009). Applying fuzzy engineering economics to evaluate project investment feasibility of wind generation. WSEAS Transactions on Systems, 8(4), 501–510.Google Scholar
  44. Sheen, J. N. (2014). Real option analysis for renewable energy investment under uncertainty. In Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES2013) (pp. 283–289). Cham: Springer.Google Scholar
  45. Soroudi, A. (2012). Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment. IEEE Transactions on Power Systems, 27(3), 1283–1293.CrossRefGoogle Scholar
  46. Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.zbMATHGoogle Scholar
  47. Ucal Sari, I., & Kahraman, C. (2015). Interval Type-2 fuzzy capital budgeting. International Journal of Fuzzy Systems, 17(4), 635–646.MathSciNetCrossRefGoogle Scholar
  48. Wu, C. B., Huang, G. H., Li, W., Zhen, J. L., & Ji, L. (2016). An inexact fixed-mix fuzzy-stochastic programming model for heat supply management in wind power heating system under uncertainty. Journal of Cleaner Production, 112, 1717–1728.CrossRefGoogle Scholar
  49. Wu, Y., Geng, S., Xu, H., & Zhang, H. (2014). Study of decision framework of wind farm project plan selection under intuitionistic fuzzy set and fuzzy measure environment. Energy Conversion and Management, 87, 274–284.CrossRefGoogle Scholar
  50. Yeh, T. M., & Huang, Y. L. (2014). Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP. Renewable Energy, 66, 159–169.CrossRefGoogle Scholar
  51. Yu, D. (2013). Triangular hesitant fuzzy set and its application to teaching quality evaluation. Journal of Information & Computational Science, 10(7), 1925–1934.CrossRefGoogle Scholar
  52. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.MathSciNetCrossRefzbMATHGoogle Scholar
  53. Zadeh, L. A. (1974). Fuzzy logic and its application to approximate reasoning. Information Processing, 74, 591–594.MathSciNetzbMATHGoogle Scholar
  54. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cengiz Kahraman
    • 1
  • Sezi Çevik Onar
    • 1
  • Başar Öztayşi
    • 1
  • İrem Uçal Sarı
    • 1
  • Esra İlbahar
    • 1
  1. 1.Industrial Engineering DepartmentIstanbul Technical UniversityMacka, IstanbulTurkey

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