Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS

Sustainability (Switzerland)

Volumn 8, Issue 6, 2016, Pages

  Cavallaro, Fausto ^a   Zavadskas, Edmundas Kazimieras ^b   Raslanas, Saulius ^b  

^a UNIVERSITY OF MOLISE   (Italy)

^b VILNIUS GEDIMINAS TECHNICAL UNIVERSITY   (Lithuania)

Author keywords

Combined heat and power (CHP); Fuzzy multicriteria; Fuzzy TOPSIS; Shannon entropy; Sustainability

Indexed keywords

ATMOSPHERIC POLLUTION; CLIMATE CHANGE; COMBINED HEAT AND POWER; DECISION MAKING; EMISSION CONTROL; ENERGY CONSERVATION; ENVIRONMENTAL ASSESSMENT; ENVIRONMENTAL ECONOMICS; ENVIRONMENTAL ISSUE; FUEL; FUZZY MATHEMATICS; GREENHOUSE GAS; METHODOLOGY; MULTICRITERIA ANALYSIS; SUSTAINABILITY;

CHANDIPURA VIRUS;

EID: 84976309734     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su8060556     Document Type: Article

References (97)

1. Commission of the European Communities. Communication from the Commission to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions; Commission of the European Communities: Brussels, Belgium, 2008.
2. European Parliament; Council of the European Union. Directive 2004/8/EC of the European Parliament and of the Council of 11 February 2004 on the Promotion of Cogeneration Based on a Useful Heat Demand in the Internal Energy Market and Amending Directive 92/42/EEC; Official Journal of the European Union: Brussels, Belgium, 2004.
3. Balli, O.; Aras, H. Energetic and exergetic performance evaluation of a combined heat and power system with the micro gas turbine (MGTCHP). Int. J. Energy Res. 2007, 31, 1425-1440.
4. Ertesvag, I.S. Exergetic comparison of efficiency indicators for combined heat and power (CHP). Energy 2007, 32, 2038-2050.
5. Biezma, M.V.; San Cristobal, J.R. Investment criteria for the selection of cogeneration plants-A state of the art review. App. Therm. Eng. 2006, 26, 583-588.
6. Kupecki, J. Off-design analysis of a micro-CHP unit with solid oxide fuel cells fed by DME. Int. J. Hydrog. Energy 2015, 40, 12009-12022.
7. Seijo, S.; Del Campo, I.; Echanobe, J.; García-Sedano, J. Modeling and multi-objective optimization of a complex CHP process. Appl. Energy 2016, 161, 309-319.
8. Wang, H.; Yin, W.; Abdollahi, E.; Lahdelma, R.; Jiao, W. Modelling and optimization of CHP based district heating system with renewable energy production and energy storage. Appl. Energy 2015, 159, 401-421.
9. Rossi, F.; Velázquez, D.; Monedero, I.; Biscarri, F. Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants. Expert Syst. Appl. 2014, 41, 4658-4669.
10. Kortela, J.; Jämsä-Jounela, S.-L. Modeling and model predictive control of the BioPower combined heat and power (CHP) plant. Int. J. Electr. Power 2015, 65, 453-462.
11. Sanaye, S.; Nasab, A.M. Modeling and optimizing a CHP system for natural gas pressure reduction plant. Energy 2012, 40, 358-369.
12. Cavallaro, F.; Ciraolo, L. Sustainability Assessment of Solar Technologies Based on Linguistic Information. In Assessment and Simulation Tools for Sustainable Energy Systems; Cavallaro, F., Ed.; Springer-Verlag: London, UK, 2013; Volume 129, pp. 3-25.
13. Cavallaro, F. Electricity from renewable energy sources: A multi-criteria evaluation framework of technologies. In Leading-Edge Electric Power Research; O'Sullivan, C.M., Ed.; Nova Science Publishers, Inc. Hauppauge: New York, NY, USA, 2008; pp. 173-204.
14. Cavallaro, F. A comparative assessment of thin-film photovoltaic production processes using the ELECTRE III method. Energy Policy 2010, 38, 463-474.
15. Wang, J.J.; Jing, Y.Y.; Zhang, C.F.; Shi, G.H.; Zhang, X.T. A fuzzy multi-criteria decision-making model for trigeneration system. Energy Policy 2008, 36, 3823-3832.
16. Nieto-Morote, A.; Ruz-Vila, F.; Cánovas-Rodríguez, F.J. Selection of a trigeneration system using a fuzzy AHP multi-criteria decision-making approach. Int. J. Energy Res. 2011, 35, 781-794.
17. Pilavachi, P.A.; Roumpeas, C.P.; Minett, S.; Afgan, N.H. Multi-criteria evaluation for CHP system options. Energy Convers. Manag. 2006, 47, 3519-3529.
18. Wang, J.J.; Jing, Y.Y.; Zhang, C.F.Weighting methodologies in multi-criteria evaluations of combined heat and power systems. Int. J. Energy Res. 2009, 33, 1023-1039.
19. Ebrahimi, M.; Keshavarz, A. Sizing the prime mover of a residential micro-combined cooling heating and power (CCHP) system by multi-criteria sizing method for different climates. Energy 2013, 54, 291-301.
20. Carvalho, M.; Lozano, M.A.; Serra, L.M. Multicriteria synthesis of trigeneration systems considering economic and environmental aspects. Appl. Energy 2012, 91, 245-254.
21. Hyde, K.; Maier, H.R.; Colby, C. Incorporating uncertainty in the PROMETHEE MCDA method. J. Multi- Criteria Decis. Anal. 2003, 12, 245-259.
22. Troldborg, M.; Heslop, S.; Hough, R.L. Assessing the sustainability of renewable energy technologies using multi-criteria analysis: Suitability of approach for national-scale assessments and associated uncertainties. Renew. Sustain. Energy Rev. 2014, 39, 1173-1184.
23. Wang, H.; Jiao, W.; Lahdelma, R.; Zhu, C.; Zou, P. Stochastic Multicriteria Acceptability Analysis for Evaluation of Combined Heat and Power Units. Energies 2015, 8, 59-78.
24. Zadeh, L. Fuzzy sets. Inf. Control 1965, 8, 338-353.
25. Ross, T.J. Fuzzy Logic with Engineering Applications, 3rd ed.; John Wiley & Sons, Ltd: Chichester, UK, 2010.
26. Bojadziev, B.; Bojadziev, M. Fuzzy Logic for Business, Finance, and Management;World Scientific Publishing: Singapore, 2007.
27. Zimmermann, H.J. Fuzzy set theory. WIREs Compt. Stat. 2010, 2, 317-332.
28. Cavallaro, F. A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass. Sustainability 2015, 7, 12359-12371.
29. Hosseinzadeh Lotfi, F.; Fallahnejad, R. Imprecise Shannon's Entropy and Multi Attribute Decision Making. Entropy 2010, 12, 53-62.
30. Cavallaro, F. Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Appl. Energy 2010, 87, 496-503.
31. Dawes, R.M.; Corrigan, B. Linear models in decision making. Psychol. Bull. 1974, 81, 95-106.
32. Jia, J.; Fischer, G.W.; Dyer, J.S. AttributeWeighting Methods and Decision Quality in the Presence of Response Error: A Simulation Study. J. Behav. Decis. Mak. 1998, 11, 85-105.
33. Chen, M.F.; Tseng, G.H.; Ding, C.G. Fuzzy MCDM approach to select service provider. In Proceedings of the 12th IEEE International Conference on Fuzzy System, St Louis, MO, USA, 25-28 May 2003; pp. 572-577.
34. Barron, F.H.; Barrett, B.E. Decision Quality Using Ranked Attribute weights. Manag. Sci. 1996, 42, 1515-1525.
35. Stillwell, W.G.; Seaver, D.A.; Edwards, W.A. Comparison of Weight Approximation Techniques in Multiattribute Utility Decision Making. Organ. Behav. Hum. Perform. 1981, 28, 62-77.
36. Eckenrode, R.T.Weighting multiple criteria. Manag. Sci. 1965, 12, 180-192.
37. Kirkwood, C.W.; Sarin, R.K. Ranking with partial information: A method and an application. Oper. Res. 1985, 33, 38-48.
38. Wang, T.C.; Chang, T.H. Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Syst. Appl. 2007, 33, 870-880.
39. Hobbs, B.F. A Comparison of Weighting Methods in Power Plant Siting. Decis. Sci. 1980, 11, 725-737.
40. Schoemaker, P.J.H.; Waid, C.C. An experimental comparison of different approaches to determining weights in additive utility models. Manag. Sci. 1982, 28, 185-196.
41. Deng, H.; Yeh, C.H.; Willis, R.J. Inter-company comparison using modified TOPSIS with objective weights. Comput. Oper. Res. 2000, 27, 963-973.
42. Mareschal, B. Weight stability intervals in multicriteria decision aid. Eur. J. Oper. Res. 1988, 33, 54-64.
43. Triantaphyllou, E.; Sanchez, A. A sensitivity analysis approach for some deterministic multi-criteria decision making methods. Decis. Sci. 1997, 28, 151-194.
44. Shannon, C.E. Mathematical Theory of Communication. Bell Sys. Tech. J. 1948, 27, 379-423, 623-656.
45. Wang, T.C.; Lee, H.D. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst. Appl. 2009, 36, 8980-8985.
46. Hwang, C.L.; Yoon, K. Multiple Attribute Decision Making. Methods and Applications; Springer-Verlag Berlin: Heidelberg, Germany, 1981.
47. Kapur, J.N. Measures of Uncertainty Mathematical Programming and Physics; Department of Mathematical Sciences Mellon College of Science: Pittsburgh, PA, USA, 1970.
48. Güneralp, B.; Gertner, G.; Mendoza, G.; Anderson, A. Evaluating probabilistic data with a possibilistic criterion in land-restoration decision-making: Effects on the precision of results. Fuzzy Set Syst. 2007, 158, 1546-1560.
49. De Luca, A.; Termini, S. A definition of non-probabilistic entropy in setting of fuzzy set theory. Inf. Control 1972, 20, 301-312.
50. Singpurwalla, N.D.; Booker, J.M. Membership functions and probability measures of fuzzy sets. J. Am. Stat. Assoc. 2004, 99, 867-889.
51. Emptoz, H. Non-probabilistic entropies and indetermination process in the setting of fuzzy set theory. Fuzzy Sets Syst. 1981, 5, 307-317.
52. Burg, J.P. Maximum Entropy Spectral Analysis. In Proceedings of the 37th Meeting of the Society of Exploration Geophysicists, Oklahoma City, OK, USA, 31 October 1967.
53. Rosenfeld, R. Adaptive Statistical Language Modeling: A Maximum Entropy Approach. Ph.D. Thesis, School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA, April 1994.
54. Golan, A.; Judge, G.G.; Miller, D. Maximum Entropy Econometrics: Robust Estimation with Limited Data; John Wiley and Sons: New York, NY, USA, 1996.
55. Esmaeili, A.; Kahnali, R.A.; Rostamzadeh, R.; Zavadskas, E.K.; Ghoddami, B. An Application of Fuzzy Logic to Assess Service Quality Attributes in Logistics Industry. Transport 2015, 30, 172-181.
56. Kildiene, S.; Kaklauskas, A.; Zavadskas, E.K. COPRAS Based Comparative Analysis of The European Country Management CapabilitiesWithin The Construction Sector in The Time of Crisis. J. Bus. Econ. Manag. 2011, 12, 417-434.
57. Saparauskas, J.; Zavadskas, E.K.; Turskis, Z. Selection of Facade's Alternatives of Commercial and Public Buildings Based on Multiple Criteria. Int. J. Strateg. Prop. Manag. 2011, 15, 189-203.
58. Susinskas, S.; Zavadskas, E.K.; Turskis, Z. Multiple Criteria Assessment of Pile-Columns Alternatives. Balt. J. Road Bridge Eng. 2011, 6, 145-152.
59. Son, C. Similarity measuring strategy of image patterns based on fuzzy entropy and energy variations in intelligent robot's manipulative task. Appl. Intell. 2013, 38, 131-145.
60. Zhao, H.; Guo, S. Selecting Green Supplier of Thermal Power Equipment by Using a Hybrid MCDM Method for Sustainability. Sustainability 2014, 6, 217-235.
61. Won, K.; Chung, E.-S.; Choi, S.-U. Parametric Assessment of Water Use Vulnerability Variations Using SWAT and Fuzzy TOPSIS Coupled with Entropy. Sustainability 2015, 7, 12052-12070.
62. Erol, I.; Sencer, S.; Ozmen, A.; Searcy, C. Fuzzy MCDM framework for locating a nuclear power plant in Turkey. Energy Policy 2014, 67, 186-197.
63. Erol, I.; Sencer, S.; Sari, R. A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain. Ecol. Econ. 2011, 70, 1088-1100.
64. Jahanshahloo, G.R.; Hosseinzadeh Lotfi, F.; Izadikhah, M. Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl. Math. Comput. 2006, 181, 1544-1551.
65. Liang, G.S. Fuzzy MCDM based on ideal and anti-ideal concepts. Eur. J. Oper. Res. 1999, 112, 682-691.
66. Li, R.J. Fuzzy method in group decision making. Comput. Math. Appl. 1999, 38, 91-101.
67. Kahraman, C.; Cevik, S.; Ates, N.Y.; Gulbay, M. Fuzzy multi-criteria evaluation of industrial robotic systems. Comput. Ind. Eng. 2007, 52, 414-433.
68. Yong, D. Plant selection based on fuzzy TOPSIS. Int. J. Adv. Manuf. 2006, 28, 839-844.
69. Chen, C.T. Extension of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 2000, 114, 1-9.
70. Yazdani-Chamzini, A.; Shariati, S.; Yakhchali, S.H.; Zavadskas, E.K. Proposing a new methodology for prioritising the investment strategies in the private sector of Iran. Ekon. Istraz. 2014, 27, 320-345.
71. Fouladgar, M.M.; Yazdani-Chamzini, A.; Zavadskas, E.K. Risk evaluation of tunneling projects. Arch. Civ. Mech. Eng. 2012, 12, 1-12.
72. Kaya, T.; Kahraman, C. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst. Appl. 2011, 38, 6577-6585.
73. Doukas, H.; Karakosta, C.; Psarras, J. Computing with words to assess the sustainability of renewable energy options. Expert Syst. Appl. 2010, 37, 5491-5497.
74. Chamodrakas, I.; Martakos, D.A. Utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks. Appl. Soft Comput. 2011, 11, 3734-3743.
75. Boran, F.E.; Boran, K.; Menlik, T. The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sour. Part B 2012, 7, 81-90.
76. Yazdani-Chamzini, A.; Fouladgar, M.M.; Zavadskas, E.K.; Moini, H.H. Selecting the Optimal Renewable Energy Using Multi Criteria Decision Making. J. Bus. Econ. Manag. 2013, 14, 957-978.
77. Şengül, Ü.; Eren, M.; Eslamian Shiraz, S.; Gezder, V.; Sengül, A.B. Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renew. Energy 2015, 75, 617-625.
78. Sianaki, O.A.; Masoum, M.A.S. A multi-agent intelligent decision making support system for home energy management in smart grid: A fuzzy TOPSIS approach. Multiagent Grid Syst. 2013, 9, 181-195.
79. Hawkes, A.D.; Leach, M.A. Cost-effective operating strategy for residential micro-combined heat and power. Energy 2007, 32, 711-723.
80. EPA-United States Environmental Protection Agency. Available online: http://www.epa.gov/chp/index.html (accessed on 16 October 2014).
81. European Commission-SETIS section on Cogeneration, Cogeneration, or Combined Heat and Power (CHP). Available online: http://setis.ec.europa.eu/technologies/chp (accessed on 16 October 2014).
82. UNEP-DTIE. Technical Report on Biomass fired Fluidised Bed Combustion Boiler Technology for Cogeneration; UNEP-DTIE: Paris, France, 2007.
83. Vatopoulos, K.; Andrews, D.; Carlsson, J.; Papaioannou, I.; Zubi, G. Study on the State of Play of Energy Efficiency of Heat and Electricity Production Technologies; European Commission: Brussels, Belgium, 2007.
84. International Energy Agency (IEA). Combined Heat and Power. Evaluating the Benefits of Greater Global Investment; International Energy Agency (IEA): Paris, France, 2008.
85. EPA-Combined Heat and Power Partnership, Catalog of CHP technologies. Available online: http://www.epa.gov/chp/documents/catalog_chptech_full.pdf (accessed on 8 September 2014).
86. EPA-Combined Heat and Power Partnership, Catalog of CHP technologies-Section 4. Technology Characterization-SteamTurbines. Available online: http://www.epa.gov/chp/documents/catalog_ chptech_steam_turbines.pdf (accessed on 8 September 2014).
87. EPA-Combined Heat and Power Partnership, Catalog of CHP technologies-Section 4. Technology Characterization-Gas Turbines. Available online: http://www.epa.gov/chp/documents/catalog_chptech_3.pdf (accessed on 8 September 2014).
88. EPA-Combined Heat and Power Partnership, Catalog of CHP technologies-Section 4. Technology Characterization-Microturbines. Available online: http://www.epa.gov/chp/documents/catalog_chptech_5.pdf (accessed on 8 September 2014).
89. Pilavachi, P.A. Power generation with gas turbine systems and combined heat and power. Appl. Therm. Eng. 2000, 20, 1421-1429.
90. Pilavachi, P.A. Mini and Micro-Gas Turbine for Combined Heat and Power. Appl. Therm. Eng. 2002, 22, 2003-2014.
91. Morse, J.D. Micro-fuel cell power sources. Int. J. Energy Res. 2007, 31, 576-602.
92. Steele Brian, C.H.; Heinzel, A. Materials for fuel-cell technologies. Nature 2001, 414, 345-352.
93. Karpenko-Jereb, L.; Sternig, C.; Fink, C.; Hacker, V.; Theiler, A.; Tatschl, R. Theoretical study of the influence of material parameters on the performance of a polymer electrolyte fuel cell. J. Power Sour. 2015, 297, 329-343.
94. Awang, N.; Ismail, A.F.; Jaafar, J.; Matsuura, T.; Junoh, H.; Othman, M.H.D.; Rahman, M.A. Functionalization of polymeric materials as a high performance membrane for direct methanol fuel cell: A review. React. Funct. Polym. 2015, 86, 248-258.
95. Sahai, Y.; Ma, J. High Performance Polymer Hydrogel Based Materials for Fuel Cells. In Polymers for Energy Storage and Conversion; Mittal, V., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2013.
96. Otomo, J.;Waki, K.; Yamada, K. Multicriteria Assessment of the Performance of Solid Oxide Fuel Cells by Cell Design and Materials Development: Design and Modeling Approach. J. Fuel Cell Sci. Technol. 2013, 10, 1-11.
97. Liso, V.; Zhao, Y.; Yang, W.; Nielsen, M.P. Modelling of a Solid Oxide Fuel Cell CHP System Coupled with a Hot Water Storage Tank for a Single Household. Energies 2015, 8, 2211-2229.