Advertisement

Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 17866–17874 | Cite as

Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making

Environmental Pollution and Energy Management
  • 241 Downloads

Abstract

The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker’s risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.

Keywords

Integrated energy system Multi-attribute decision making Decision-maker risk preference Efficiency evaluation model Information fusion Linguistic value 

Notes

Acknowledgements

(1) Project supported by the humanities and social sciences research youth fund of the China Ministry education, the project number: 16YJC790052

(2) Hunan province “The 13th Five-year” education planning project” School-enterprise cooperation in personnel training Virtual Alliance: organizational structure, resource sharing and performance assessment study” (project number: XJK015BGD053).

(3) 2015 Hunan Province Philosophy and Social Science Fund Project “ Research on the Internet Media Effect China’s Stock Market and Network Public Opinion Monitor Index of Listed Companies” (project number: 14YBA306).

(4) Supported by the constructing program of the key discipline of Finance in Huai-hua University.

References

  1. Ahmadi P, Dincer I, Rosen MA (2014) Thermo economic multi-objective optimization of a novel biomass-based integrated energy system. Energy 68:958–970. doi: 10.1016/j.energy.2014.01.085 CrossRefGoogle Scholar
  2. Amin SM, Wollenberg BF (2005) Toward a smart grid: power delivery for the 21st century. IEEE Power Energ Mag 3:34–41. doi: 10.1109/MPAE.2005.1507024 CrossRefGoogle Scholar
  3. Bertsch V, Treitz M, Geldermann J, Rentz O (2007) Sensitivity analyses in multi-attribute decision support for off-site nuclear emergency and recovery management. Int J Energy Sect Manage 1(4):342–365. doi: 10.1108/17506220710836075 CrossRefGoogle Scholar
  4. Colak I, Sagiroglu S, Fulli G et al (2016) A survey on the critical issues in smart grid technologies. Renew Sust Energ Rev 54:396–405. doi: 10.1016/j.rser.2015.10.036 CrossRefGoogle Scholar
  5. Coroama VC, Hilty LM (2014) Assessing internet energy intensity: a review of methods and results. Environ Impact Assess Rev 45:63–68. doi: 10.1016/j.eiar.2013.12.004 CrossRefGoogle Scholar
  6. Galbusera L, Theodoridis G, Giannopoulos G (2015) Intelligent energy systems: introducing power-ICT interdependency in modelling and control design. IEEE Trans Ind Electron 62:2468–2477. doi: 10.1109/TIE.2014.2364546 CrossRefGoogle Scholar
  7. Geidl M, Andersson G (2007) Optimal power flow of multiple energy carriers. IEEE Trans Power Syst 22:145–155. doi: 10.1109/TPWRS.2006.888988
  8. Grandhi S, Wibowo S (2015) The selection of renewable energy alternative using the fuzzy multiattribute decision making method, The 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2015):195–200. doi: 10.1109/FSKD.2015.7381939
  9. Heeks R (2010) Do information and communication technologies (ICTs) contribute to development. J Int Dev 22:625–640. doi: 10.1002/jid.1716 CrossRefGoogle Scholar
  10. Hideharu S, Jun K, Kiichiro T (2003) A multi-objective optimization model for determining urban energy systems under integrated energy service in a specific area. Electr Eng Jpn 123:20–31. doi: 10.1541/ieejpes.123.151 Google Scholar
  11. Kahraman C, Kaya İ, Cebi S (2009) A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 34(10):1603–1616. doi: 10.1016/j.energy.2009.07.008 CrossRefGoogle Scholar
  12. Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sust Energ Rev 69:596–609. doi: 10.1016/j.rser.2016.11.191 CrossRefGoogle Scholar
  13. Lee AHI, Hung MC, Kang HY, Pearn WL (2012) A wind turbine evaluation model under a multi-criteria decision making environment. Energy Convers Manag 64:289–300. doi: 10.1016/j.Enconman.2012.03.029 CrossRefGoogle Scholar
  14. Lemar P (2002) Integrated energy systems (IES) for buildings: a market assessment. Office of Scientific and Technical Information 1–70. doi: 10.2172/814088
  15. Lund H, Münster E (2006) Integrated energy systems and local energy markets. Energy Policy 34:1152–1160. doi: 10.1016/j.enpol.2004.10.004 CrossRefGoogle Scholar
  16. Mattiuss A, Rosano M, Simeoni P (2014) A decision support system for sustainable energy supply combining multi-objective and multi-attribute analysis: an Australian case study. Decis Support Syst 57(1):150–159. doi: 10.1016/j.dss.2013.08.013 CrossRefGoogle Scholar
  17. Pohekar SD, Ramachandran M (2004) Application of multi-criteria decision making to sustainable energy planning—a review. Renew Sust Energ Rev 8(4):365–381. doi: 10.1016/j.rser.2003.12.007 CrossRefGoogle Scholar
  18. Rao CJ, Liu JE, Dong JH, Jentsch PC (2014) Hybrid multi-attribute decision making method of electric coal procurement in industry. Fuzzy Inf Eng 16:451–462. doi: 10.1016/j.fiae.2015.01.004 CrossRefGoogle Scholar
  19. Singh S, Dasgupta MS (2016) Evaluation of research on CO 2 trans-critical work recovery expander using multi attribute decision making methods. Renew Sust Energ Rev 59:119–129. doi: 10.1016/j.rser.2016.01.013 CrossRefGoogle Scholar
  20. Singh D, Rao RV (2011) A hybrid multiple attribute decision making method for solving problems of industrial environment. Int J Ind Eng Comput 2:1–20. doi: 10.5267/j.ijiec.2011.02.001 Google Scholar
  21. Taylan O, Kaya D, Demirbas A (2016) An integrated multi attribute decision model for energy efficiency processes in petrochemical industry applying fuzzy set theory. Energy Convers Manag 117:501–512. doi: 10.1016/j.enconman.2016.03.048 CrossRefGoogle Scholar
  22. Wang JJ, Jing YY, Zhang CF, Zhao JH (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13(9):2263–2278. doi: 10.1016/j.rser.2009.06.021 CrossRefGoogle Scholar
  23. Werner A, Werner A, Wieland R, Kersebaum K-C, Mirschel W, Ende H-P, Wiggering H (2014) Ex ante assessment of crop rotations focusing on energy crops using a multi-attribute decision-making method. Ecol Indic 45:110–122Google Scholar
  24. Wu J (2014) Drivers and state-of-the-art of integrated energy systems in Europe. Autom Electr Power Syst 40:1–7. doi: 10.7500/AEPS20150512001 Google Scholar
  25. Wu J, Yan J, Jia H, Hatziargyriou N, Djilali N (2016) Integrated energy systems. Appl Energy 167:155–157. doi: 10.1016/j.apenergy.2016.02.075 CrossRefGoogle Scholar
  26. Xu L, Zhang Y, Zhang B, Chen L (2014) Based on hybrid multi-attribute group decision making method of evaluation of integrated energy system efficiency. J Ind Technol Econ 245:52–57. doi: 10.3969/j.Issn.1004-910X.2014.03.007 Google Scholar
  27. Xue Y (2015) Energy internet or comprehensive energy network? J Mod Power Syst Clean Energ 3:297–301. doi: 10.1007/s40565-015-0111-5 CrossRefGoogle Scholar
  28. Yang T, Pen H, Wang D, Wang Z (2016) Harmonic analysis in integrated energy system based on compressed sensing. Appl Energy 165:583–591. doi: 10.1016/j.apenergy.2015.12.058 CrossRefGoogle Scholar
  29. Zavadskas EK, Antucheviciene J, Turskis Z, Adeli H (2016) Hybrid multiple-criteria decision-making methods: a review of applications in engineering. Scientia Iranica 23:1–20CrossRefGoogle Scholar
  30. Zhang L, Zhang B (2015) Evaluation of the integrated energy system effectiveness based on the normal distribution interval number method. South Energy Construct 2:41–45. doi: 10.16516/j.gedi.issn2095-8676.2015.02.007 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Information Management & EngineeringShanghai University of Finance and EconomicsShanghaiChina
  2. 2.Department of EconomicsHuai-hua CollegeHuai-huaChina

Personalised recommendations