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Fuzzy Sets Applications in Complex Energy Systems: A Literature Review

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Energy Management—Collective and Computational Intelligence with Theory and Applications

Part of the book series: Studies in Systems, Decision and Control ((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.

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References

  • Abed-Elmdoust, A., & Kerachian, R. (2012). Wave height prediction using the rough set theory. Ocean Engineering, 54, 244–250.

    Article  Google Scholar 

  • Afgan, N. H., & Carvalho, M. G. (2004). Sustainability assessment of hydrogen energy systems. International Journal of Hydrogen Energy, 29(13), 1327–1342.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Buchholz, T., Rametsteiner, E., Volk, T. A., & Luzadis, V. A. (2009). Multi criteria analysis for bioenergy systems assessments. Energy Policy, 37, 484–495.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Demirbas, A. H., & Demirbas, I. (2007). Importance of rural bioenergy for developing countries. Energy Conversion and Management, 48(8), 2386–2398.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Kahraman, C., Ruan, D., & Dogan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135–153.

    Article  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ligtvoet, A., & Chappin, E. J. L. (2012). Experience-based exploration of complex energy systems. Journal of Futures Studies, 17(1), 57–70.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mostashari, A. (2011). Collaborative modeling and decision-making for complex energy systems. World Scientific.

    Google Scholar 

  • Muench, S. (2015). Greenhouse gas mitigation potential of electricity from biomass. Journal of Cleaner Production, 103, 483–490.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Özger, M. (2010). Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Engineering, 37(16), 1443–1451.

    Article  Google Scholar 

  • Özger, M. (2011). Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling. Expert Systems with Applications, 38(5), 6269–6274.

    Article  Google Scholar 

  • Özger, M., & Şen, Z. (2007). Prediction of wave parameters by using fuzzy logic approach. Ocean Engineering, 34(3), 460–469.

    Article  Google Scholar 

  • Palaniswamy, A. M., & Srinivasan, K. (2016). Takagi-Sugeno fuzzy approach for power optimization in standalone photovoltaic systems. Solar Energy, 139, 213–220.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Rajesh, R., & Mabel, M. C. (2015). A comprehensive review of photovoltaic systems. Renewable and Sustainable Energy Reviews, 51, 231–248.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Smarandache, F. (1999). A unifying field in logics: neutrosophic logic, philosophy, 1–141.

    Google Scholar 

  • Stefanakos, C. (2016). Fuzzy time series forecasting of nonstationary wind and wave data. Ocean Engineering, 121, 1–12.

    Article  Google Scholar 

  • 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 

  • 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 

  • Sylaios, G., Bouchette, F., Tsihrintzis Vassilios, A., & Denamiel, C. (2009). A fuzzy inference system for wind-wave modeling. Ocean Engineering, 36(17), 1358–1365.

    Article  Google Scholar 

  • 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.

  • 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 

  • Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yager, R. R. (2013). Pythagorean fuzzy subsets. In Proceedings of the Joint IFSA Congress and NAFIPS Meeting, Edmonton, Canada (pp. 57–61).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–356.

    Google Scholar 

  • Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8, 199–249.

    Google Scholar 

  • Ziolkowska, J. R. (2014). Optimizing biofuels production in an uncertain decision environment: Conventional vs. advanced technologies. Applied Energy, 114, 366–376.

    Google Scholar 

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Authors and Affiliations

  1. Istanbul Technical University, Maçka, Istanbul, Turkey

    Cengiz Kahraman, Başar Oztaysi, Sezi Çevik Onar & Sultan Ceren Öner

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  1. Cengiz Kahraman
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  2. Başar Oztaysi
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  3. Sezi Çevik Onar
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  4. Sultan Ceren Öner
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Correspondence to Cengiz Kahraman .

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  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Energy Institute, Istanbul Technical University, Istanbul, Turkey

    Gülgün Kayakutlu

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Kahraman, C., Oztaysi, B., Çevik Onar, S., Öner, S.C. (2018). Fuzzy Sets Applications in Complex Energy Systems: A Literature Review. In: Kahraman, C., Kayakutlu, G. (eds) Energy Management—Collective and Computational Intelligence with Theory and Applications. Studies in Systems, Decision and Control, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-319-75690-5_2

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