Fuzzy Sets Applications in Complex Energy Systems: A Literature Review

  • Cengiz Kahraman
  • Başar Oztaysi
  • Sezi Çevik Onar
  • Sultan Ceren Öner
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)

Abstract

With the emergence of new energy-related technologies and new energy sources, energy planning has become even more vital and complex. Decision making and optimization are very important for complex energy systems. Efficient decision making requires the involvement of various stakeholders which makes the decision problem even more difficult. Fuzzy sets provide tools for mathematically representing vagueness and imprecision in the data or the linguistic stakeholder evaluations. In this chapter an extended literature on fuzzy sets application of complex energy systems. The main issues emphasized in the literature review can be summarized as prediction and modelling the energy configuration conditions, interactions among the various critical design parameters, and solving power systems challenges under uncertainty. The fuzzy application on complex energy systems is presented for different energy types, such as bioenergy, wave energy, photovoltaic systems, hydrogen energy, nuclear energy, wind and thermal energy.

References

  1. Abed-Elmdoust, A., & Kerachian, R. (2012). Wave height prediction using the rough set theory. Ocean Engineering, 54, 244–250.CrossRefGoogle Scholar
  2. Afgan, N. H., & Carvalho, M. G. (2004). Sustainability assessment of hydrogen energy systems. International Journal of Hydrogen Energy, 29(13), 1327–1342.CrossRefGoogle Scholar
  3. Afgan, N. H., Veziroglu, A., & Carvalho, M. G. (2007). Multi-criteria evaluation of hydrogen system options. International Journal of Hydrogen Energy, 32(15), 3183–3193.CrossRefGoogle Scholar
  4. Ahn, K. K., Truong, D. Q., Tien, H. H., & Yoon, J. I. (2012). An innovative design of wave energy converter. Renewable Energy, 42, 186–194.CrossRefGoogle Scholar
  5. Akpınar, A., Özger, M., Kömürcü, M. İ. (2014). Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea. Journal of Marine Science and Technology, 19, 1–14.Google Scholar
  6. Amarkarthik, A., & Sivakumar, K. (2016). Investigation on modeling of non-buoyant body typed point absorbing wave energy converter using adaptive network-based fuzzy inference system. International Journal of Marine Energy, 13, 157–168.CrossRefGoogle Scholar
  7. Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.Google Scholar
  8. Atanassov, K.T. (1989). On intuitionistic fuzzy sets and their applications. In Actual Problems of Sciences, Bulgarian Academy of Sciences (Vol. 1, pp. 1–53) (in Bulgarian).Google Scholar
  9. Azizipanah-Abarghooee, R., Niknam, T., Roosta, A., Malekpour, A. R., & Zare, M. (2012). Probabilistic multi objective wind-thermal economic emission dispatch based on point estimated method. Energy, 37(1), 322–335.CrossRefGoogle Scholar
  10. Bonsignore, L., Davarifar, M., Rabhi, A., Tina, G. M., & Elhajjaji, A. (2014). Neuro-fuzzy fault detection method for photovoltaic systems. Energy Procedia, 62, 431–441.CrossRefGoogle Scholar
  11. Buchholz, T., Rametsteiner, E., Volk, T. A., & Luzadis, V. A. (2009). Multi criteria analysis for bioenergy systems assessments. Energy Policy, 37, 484–495.CrossRefGoogle Scholar
  12. Carmona, C. J., González, P., García-Domingo, B., del Jesus, M. J., & Aguilera, J. (2013). MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to concentrating photovoltaic technology. Knowledge-Based Systems, 54, 73–85.CrossRefGoogle Scholar
  13. Castiglia, F., & Giardina, M. (2013). Analysis of operator human errors in hydrogen refuelling stations: Comparison between human rate assessment techniques. International Journal of Hydrogen Energy, 38(2), 1166–1176.CrossRefGoogle Scholar
  14. Cebi, S., Ilbahar, E., & Atasoy, A. (2016). A fuzzy information axiom based method to determine the optimal location for a biomass power plant: A case study in Aegean Region of Turkey. Energy, 116, 894–907.CrossRefGoogle Scholar
  15. Chang, E. (2017). Rapid-convergent sliding mode proportional-integral technology with fuzzy gain scheduling for hydrogen energy applications. International Journal of Hydrogen Energy, 42(29), 18216–18222.CrossRefGoogle Scholar
  16. Chang, P. L., Hsu, C. W., & Lin, C. Y. (2012). Assessment of hydrogen fuel cell applications using fuzzy multiple-criteria decision making method. Applied Energy, 100, 93–99.CrossRefGoogle Scholar
  17. Chekired, F., Mellit, A., Kalogirou, S., & Larbes, C. (2014). Intelligent maximum power point trackers for photovoltaic applications using FPGA chip: A comparative study. Solar Energy, 101, 83–99.CrossRefGoogle Scholar
  18. Chiu, C. S., & Ouyang, R. Y. L. (2011). Maximum power tracking control of uncertain photovoltaic systems: A unified T-S fuzzy model-based approach. IEEE Transactions on Control Systems Technology, 19, 1516–1526.CrossRefGoogle Scholar
  19. Coteli, R., Acikgoz, H., Ucar, F., & Dandil, B. (2017). Design and implementation of type-2 fuzzy neural system controller for PWM rectifiers. International Journal of Hydrogen Energy, 42(32), 20759–20771.CrossRefGoogle Scholar
  20. Cutz, L., Haro, P., Santana, D., & Johnsson, F. (2016). Assessment of biomass energy sources and technologies: The case of Central America. Renewable and Sustainable Energy Reviews, 58, 1411–1431.CrossRefGoogle Scholar
  21. Das, S. K., Verma, D., Nema, S., & Nema, R. K. (2017). Shading mitigation techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 78, 369–390.CrossRefGoogle Scholar
  22. Demirbas, A. H., & Demirbas, I. (2007). Importance of rural bioenergy for developing countries. Energy Conversion and Management, 48(8), 2386–2398.CrossRefGoogle Scholar
  23. Dos Santos Grecco, C. H., Vidal, M. C. R., Cosenza, C. A. N., Dos Santos, I. J. A., & De Carvalho, P. V. R. (2014). Safety culture assessment: A fuzzy model for improving safety performance in a radioactive installation. Progress in Nuclear Energy, 70, 71–83.CrossRefGoogle Scholar
  24. Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84–93.CrossRefGoogle Scholar
  25. Erol, İ., Sencer, S., Özmen, A., & Searcy, C. (2014). Fuzzy MCDM framework for locating a nuclear power plant in Turkey. Energy Policy, 67, 186–197.CrossRefGoogle Scholar
  26. Falsafi, H., Zakariazadeh, A., & Jadid, S. (2014). The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming. Energy, 64, 853–867.CrossRefGoogle Scholar
  27. Franco, C., Bojesen, M., Hougaard, J. L., & Nielsen, K. (2015). A fuzzy approach to a multiple criteria and geographical information system for decision support on suitable locations for biogas plants. Applied Energy, 140, 304–315.CrossRefGoogle Scholar
  28. Gim, B., & Kim, J. W. (2014). Multi-criteria evaluation of hydrogen storage systems for automobiles in Korea using the fuzzy analytic hierarchy process. International Journal of Hydrogen Energy, 39(15), 7852–7858.CrossRefGoogle Scholar
  29. Guimarães, A. C. F., & Lapa, C. M. F. (2004). Nuclear transient phase ranking table using fuzzy inference system. Annals of Nuclear Energy, 31(15), 1803–1812.CrossRefGoogle Scholar
  30. Kahraman, C., & Kaya, İ. (2010). Fuzzy acceptance sampling plans. In C. Kahraman & M. Yavuz (Eds.), Production engineering and management under fuzziness (pp. 457–481). Berlin: Springer.CrossRefGoogle Scholar
  31. Kahraman, C., Ruan, D., & Dogan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135–153.CrossRefMATHGoogle Scholar
  32. Kazeminezhad, M. H., Etemad-Shahidi, A., & Mousavi, S. J. (2005). Application of fuzzy inference system in the prediction of wave parameters. Ocean Engineering, 32(14–15), 1709–1725.CrossRefGoogle Scholar
  33. Khaehintung, N., Kunakorn, A., & Sirisuk, P. (2010). A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current mode boost converter with bifurcation control. International Journal of Control, Automation and Systems, 8, 289–300.CrossRefGoogle Scholar
  34. Khishtandar, S., Zandieh, M., & Dorri, B. (2017). A multi-criteria decision-making framework for sustainability assessment of bioenergy production technologies with hesitant fuzzy linguistic term sets: The case of Iran. Renewable and Sustainable Energy Reviews, 77, 1130–1145.CrossRefGoogle Scholar
  35. Kottas, T. L., Boutalis, Y. S., & Karlis, A. D. (2006). New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks. IEEE Transactions on Energy Conversion, 21, 793–803.Google Scholar
  36. Kuhmaier, M., Kanzian, C., & Stampfer, K. (2014). Identification of potential energy wood terminal locations using a spatial multi criteria decision analysis. Biomass and Bioenergy, 66, 337–347.CrossRefGoogle Scholar
  37. Lee, S. K., Mogi, G., Lee, S. K., Hui, K. S., & Kim, J. W. (2010). Econometric analysis of the R&D performance in the national hydrogen energy technology development for measuring relative efficiency: The fuzzy AHP/DEA integrated model approach. International Journal of Hydrogen Energy, 35(6), 2236–2246.CrossRefGoogle Scholar
  38. Lee, S. K., Mogi, G., Lee, S. K., & Kim, J. W. (2011a). Prioritizing the weights of hydrogen energy technologies in the sector of the hydrogen economy by using a fuzzy AHP approach. International Journal of Hydrogen Energy, 36(2), 1897–1902.CrossRefGoogle Scholar
  39. Lee, S. K., Mogi, G., Li, Z., Hui, K. S., Lee, S. K., Hui, K. N., et al. (2011b). Measuring the relative efficiency of hydrogen energy technologies for implementing the hydrogen economy: An integrated fuzzy AHP/DEA approach. International Journal of Hydrogen Energy, 36(20), 12655–12663.CrossRefGoogle Scholar
  40. Ligtvoet, A., & Chappin, E. J. L. (2012). Experience-based exploration of complex energy systems. Journal of Futures Studies, 17(1), 57–70.Google Scholar
  41. Luan, X., Young, A. G., Han, W. S., & Zhai, Y. (2011). Load-following control of nuclear reactors based on Takagi-Sugeno fuzzy model. IFAC Proceedings Volumes, 44(1), 8253–8258.CrossRefGoogle Scholar
  42. Moon, J. H., & Kang, C. S. (1999). Use of fuzzy set theory in the aggregation of expert judgments. Annals of Nuclear Energy, 26(6), 461–469.CrossRefGoogle Scholar
  43. Mostashari, A. (2011). Collaborative modeling and decision-making for complex energy systems. World Scientific.Google Scholar
  44. Muench, S. (2015). Greenhouse gas mitigation potential of electricity from biomass. Journal of Cleaner Production, 103, 483–490.CrossRefGoogle Scholar
  45. Ng, R. T. L., Ng, D. K. S., Tan, R. R., & El-Halwagi, M. M. (2014). Disjunctive fuzzy optimisation for planning and synthesis of bioenergy-based industrial symbiosis system. Journal of Environmental Chemical Engineering, 2(2014), 652–664.CrossRefGoogle Scholar
  46. Oluwamayowa, O. A., Shearing, P. R., & Fraga, E. S. (2017). On the design of complex energy systems: Accounting for renewables variability in systems sizing. Computers & Chemical Engineering, 103, 103–115.CrossRefGoogle Scholar
  47. Özger, M. (2010). Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Engineering, 37(16), 1443–1451.CrossRefGoogle Scholar
  48. Özger, M. (2011). Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling. Expert Systems with Applications, 38(5), 6269–6274.CrossRefGoogle Scholar
  49. Özger, M., & Şen, Z. (2007). Prediction of wave parameters by using fuzzy logic approach. Ocean Engineering, 34(3), 460–469.CrossRefGoogle Scholar
  50. Palaniswamy, A. M., & Srinivasan, K. (2016). Takagi-Sugeno fuzzy approach for power optimization in standalone photovoltaic systems. Solar Energy, 139, 213–220.CrossRefGoogle Scholar
  51. Purba, J. H. (2014). A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment. Annals of Nuclear Energy, 70, 21–29.CrossRefGoogle Scholar
  52. Rahrah, K., Rekioua, D., Rekioua, T., & Bacha, S. (2015). Photovoltaic pumping system in Bejaia climate with battery storage. International Journal of Hydrogen Energy, 40(39), 13665–13675.CrossRefGoogle Scholar
  53. Rajesh, R., & Mabel, M. C. (2015). A comprehensive review of photovoltaic systems. Renewable and Sustainable Energy Reviews, 51, 231–248.CrossRefGoogle Scholar
  54. Reddy, S. S., Bijwe, P. R., & Abhyankar, A. R. (2013). Multi-objective market clearing of electrical energy, spinning reserves and emission for wind-thermal power system. International Journal of Electrical Power & Energy Systems, 53, 782–794.CrossRefGoogle Scholar
  55. Ren, J., Manzardo, A., Toniolo, S., & Scipioni, A. (2013). Sustainability of hydrogen supply chain. Part II: Prioritizing and classifying the sustainability of hydrogen supply chains based on the combination of extension theory and AHP. International Journal of Hydrogen Energy, 38(32), 13845–13855.CrossRefGoogle Scholar
  56. Safari, S., Ardehali, M. M., & Sirizi, M. J. (2013). Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage. Energy Conversion and Management, 65, 41–49.CrossRefGoogle Scholar
  57. Serdio Fernández, F., Muñoz-García, M. A., & Saminger-Platz, S. (2016). Detecting clipping in photovoltaic solar plants using fuzzy systems on the feature space. Solar Energy, 132, 345–356.CrossRefGoogle Scholar
  58. Singh, S., & Agrawal, S. (2015). Parameter identification of the glazed photovoltaic thermal system using genetic algorithm-fuzzy system (GA–FS) approach and its comparative study. Energy Conversion and Management, 105, 763–771.CrossRefGoogle Scholar
  59. Singh, S., & Agrawal, S. (2016). Efficiency maximization and performance evaluation of hybrid dual channel semi-transparent photovoltaic thermal module using fuzzyfied genetic algorithm. Energy Conversion and Management, 122, 449–461.CrossRefGoogle Scholar
  60. Smarandache, F. (1999). A unifying field in logics: neutrosophic logic, philosophy, 1–141.Google Scholar
  61. Stefanakos, C. (2016). Fuzzy time series forecasting of nonstationary wind and wave data. Ocean Engineering, 121, 1–12.CrossRefGoogle Scholar
  62. Subiyanto, S., Mohamed, A., Hannan, M. A. (2012). Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller. Energy and Buildings, 51, 29–38.Google Scholar
  63. Sussman, J. (2003). Collected views on complexity in systems. In Engineering Systems Division Working Paper Series ESD-WP-2003–01.06-ESD Internal Symposium, Massachusetts Institute of Technology.Google Scholar
  64. Sylaios, G., Bouchette, F., Tsihrintzis Vassilios, A., & Denamiel, C. (2009). A fuzzy inference system for wind-wave modeling. Ocean Engineering, 36(17), 1358–1365.CrossRefGoogle Scholar
  65. Tabanjat, A., Becherif, M., Hissel, D., & Ramadan, H. S. (2017). Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques. International Journal of Hydrogen Energy.  https://doi.org/10.1016/j.ijhydene.2017.06.085. Available online July 6, 2017.
  66. Toffolo, A., & Lazzaretto, A. (2008). Energy system diagnosis by a fuzzy expert system with genetically evolved rules. International Journal of Thermodynamics, 11(3), 115–121.Google Scholar
  67. Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.Google Scholar
  68. Truong, D. Q., & Ahn, K. K. (2014). Development of a novel point absorber in heave for wave energy conversion. Renewable Energy, 65, 183–191.Google Scholar
  69. Ubando, A. T., Culaba, A. B., Aviso, K. B., Tan, R. R., Cuello, J. L., Ng, D. K. S., et al. (2016). Fuzzy mixed integer non-linear programming model for the design of an algae-based eco-industrial park with prospective selection of support tenants under product price variability. Journal of Cleaner Production, 136, 183–196.CrossRefGoogle Scholar
  70. Woo, T. H. (2014). Modified fuzzy algorithm based safety analysis of nuclear energy for sustainable hydrogen production in climate change prevention. International Journal of Electrical Power & Energy Systems, 61, 192–196.CrossRefGoogle Scholar
  71. Woo, T. H., & Lee, U. C. (2010). The statistical analysis of the passive system reliability in the Nuclear Power Plants (NPPs). Progress in Nuclear Energy, 52(5), 456–461.CrossRefGoogle Scholar
  72. Wright, D. G., Dey, P. K., & Brammer, J. G. (2013). A fuzzy levelised energy cost method for renewable energy technology assessment. Energy Policy, 62, 315–323.CrossRefGoogle Scholar
  73. Wu, Y., Chen, K., Zeng, B., Xu, H., & Yang, Y. (2016). Supplier selection in nuclear power industry with extended VIKOR method under linguistic information. Applied Soft Computing, 48, 444–457.CrossRefGoogle Scholar
  74. Yager, R. R. (2013). Pythagorean fuzzy subsets. In Proceedings of the Joint IFSA Congress and NAFIPS Meeting, Edmonton, Canada (pp. 57–61).Google Scholar
  75. Yılmaz Balaman, S., & Selim, H. (2014). A fuzzy multi objective linear programming model for design and management of anaerobic digestion based bioenergy supply chains. Energy, 74, 928–940.CrossRefGoogle Scholar
  76. Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering, 66, 36–56.CrossRefGoogle Scholar
  77. Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–356.Google Scholar
  78. Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8, 199–249.Google Scholar
  79. Ziolkowska, J. R. (2014). Optimizing biofuels production in an uncertain decision environment: Conventional vs. advanced technologies. Applied Energy, 114, 366–376.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cengiz Kahraman
    • 1
  • Başar Oztaysi
    • 1
  • Sezi Çevik Onar
    • 1
  • Sultan Ceren Öner
    • 1
  1. 1.Istanbul Technical UniversityMaçka, IstanbulTurkey

Personalised recommendations