Sustainability Indicator of Renewable Energy System Based on Fuzzy Multi-Criteria Decision Making Methods

Abstract:

Sustainability measurement of renewable energy system is complex, as is the system itself. The study proposes a general indicator to evaluate sustainability of a renewable energy system. In order to comprehensively assess the environmental, economic and social sustainability, eleven indicators with different dimensions are quantified and aggregated into a general indicator of sustainability (GIS) based on fuzzy decision making methods, such as fuzzy analytic hierarchy processing and fuzzy comprehensive evaluation. The developed GIS is able to provide numerical results of sustainability for a renewable energy system. A case of a renewable energy system in Australia are presented to explain the application of the sustainability indicator. In terms of the four different combinations of grid, solar PV and wind renewable energy, the developed GIS is used to assess their sustainability.

• Periodical: Advanced Materials Research (Volumes 361-363)
• Pages: 1263-1273
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.361-363.1263
• Online since: October 2011
• Authors: Gang Liu, Mohammad Rasul, M. T. O. Amanullah, Masud M. K. Khan
• Keywords: Fuzzy Analytical Hierarchy Process, Fuzzy Multi-Criteria Decision Making, Renewable Energy System, Sustainability Indicator

References

[1] N. Afgan, M. Carvalho, N. Hovanov, Energy system assessment with sustainability indicators, Energy Policy 28 (9) (2000) 603–612.

DOI: 10.1016/s0301-4215(00)00045-8

[2] J. Dewulf, H. Van Langenhove, J. Mulder, M. Van Den Berg, H. Van Der Kooi, J. De Swaan Arons, Illustrations towards quantifying the sustainability of technology, Green Chemistry 2 (3) (2000) 108–114.

DOI: 10.1039/b000015i

[3] A. Kumar, T. Kandpal, co2 emissions mitigation potential of some renewable energy technologies in india, Energy Sources, Part A: Recovery, Utilization and Environmental Effects 29 (13) (2007) 1203–1214.

DOI: 10.1080/009083190965343

[4] W. Norde, Energy and entropy: A thermodynamic approach to sustainability, Environmentalist 17 (1) (1997) 57–62.

[5] G. Liu, M. G. Rasul, M. T. O. Amannuallah, M. M. K. Khan, Environmental and economic analysis for grid-connected hybrid photovoltaic-wind power system in a hot arid environment, in: International Engineering Conference on Hot Arid Region (IECHAR 2010) Kingdom of Saudi Arabia, 2010.

DOI: 10.5383/ijtee.01.01.003

[6] G. H. Brundtland, Our common Fture, Oxford University Press, Oxford, 1987.

[7] N. H. Afgan, M. D. G. Carvalho, Quality, sustainability and indicators of energy systems, Begell House Inc., New York, 2010.

[8] M. Brenna, F. Foiadelli, M. Roscia, Sustainability energy indicators by means of fuzzy logic, in: 2007 IEEE Power Engineering Society General Meeting, PES, Tampa, FL, United states, 2010.

DOI: 10.1109/pes.2007.385585

[9] J. Elkington, Towards the sustainable corporation: Win-win-win business strategies for sustainable development, California Management Review 36 (2) (1994) 90–100.

DOI: 10.2307/41165746

[10] A. Evans, V. Strezov, T. Evans, Assessment of sustainability indicators for renewable energy technologies, Renewable and Sustainable Energy Reviews 13 (5) (2009) 1082–1088.

DOI: 10.1016/j.rser.2008.03.008

[11] T. Manzi, K. Lucas, T. Jones, J. Allen, Social sustainability in urban areas: Communities, connectivity and the urban fabric, Earthscan, London and Washington, DC, 2009.

DOI: 10.1080/17535069.2010.528604

[12] N. H. Afgan, M. G. Carvalho, Sustainability assessment of a hybrid energy system, Energy Policy 36 (8) (2008) 2893–2900.

[13] C. Kahraman (Ed.), Fuzzy multi-criteria decision making:Theory and applications with recent developments, Vol. 16, Springer, 2008.

[14] T. L. Saaty, The analytic hierarchy process: Planning, priority setting, resource allocation, McGraw-Hill, New York, 1980.

[15] C.-C. Sun, A performance evaluation model by integrating fuzzy ahp and fuzzy topsis methods, Expert Systems with Applications 37 (12) (2010) 7745–7754.

DOI: 10.1016/j.eswa.2010.04.066

[16] C.-C. Yang, B.-S. Chen, Key quality performance evaluation using fuzzy ahp, Journal of the Chinese Institute of Industrial Engineers 21 (6) (2004) 543–550.

DOI: 10.1080/10170660409509433

[17] D.-Y. Chang, Applications of the extent analysis method on fuzzy ahp, European Journal of Operational Research 95 (3) (1996) 649–655.

DOI: 10.1016/0377-2217(95)00300-2

[18] B. S. Richards, Quantifying Environmental Impact Assessments Using Fuzzy Logic, Springer, Troutdale, 2005.

[19] N. H. Afgan, M. G. Carvalho, Sustainability assessment of hydrogen energy systems, international Journal of Hydrogen Energy 29 (13) (2004) 1327–1342.

DOI: 10.1016/j.ijhydene.2004.01.005

[20] L. Zadeh, Fuzzy sets, Information Control 8 (1965) 338–353.

[21] J. Lu, G. Zhang, D. Ruan, F.Wu, Multi-objective group decision making: Method, software, and applications with fuzzy set techniques, Vol. 6, Imperial College Press, Chicago, 2007.

DOI: 10.1142/p505

[22] Z. Ayag, R. Ozdemir, A fuzzy ahp approach to evaluating machine tool alternatives, Journal of Intelligent Manufacturing 17 (2) (2006) 179–190.

DOI: 10.1007/s10845-005-6635-1

[23] E. Heo, J. Kim, K.-J. Boo, Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy ahp, Renewable and Sustainable Energy Reviews 14 (8) (2010) 2214–2220.

DOI: 10.1016/j.rser.2010.01.020

[24] D. Du, P. Q. Hua, Y.Wu, Modern comprehensive assessment methods and cases study, Tsinghua University Press, Beijing, 2008.

[25] N. Afgan, M. Darwish, Multi-criteria sustainability assessment of water desalination and energy systems - kuwait case, Desalination and Water Treatment 25 (1-3) (2011) 241–250.

DOI: 10.5004/dwt.2011.1764