China’s Energy Portfolio Optimization Study Based on Multi-Attribute Utility Theory and Genetic Algorithm Method

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Abstract:

China’s energy planning problem was concluded to the energy portfolio optimization problem which was solved by multi-attribute utility theory and genetic algorithm methods. A fundamental objectives hierarchy was established to structure the energy technology alternatives. Based on this hierarchy model, weights and utilities of energy resources were calculated by the multi-attribute utility theory. The Evolver decision tool was used to find an optimal energy portfolio by the genetic algorithm. The results indicated China should reduce coal and nuclear power in the future energy portfolio.

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Advanced Materials Research (Volumes 524-527)

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3027-3035

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May 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] J P Huang, K L Poh, B Wang. Decision analysis of energy and environmental modeling. Energy 20 (1995), 843–855.

DOI: 10.1016/0360-5442(95)00036-g

Google Scholar

[2] Bing W, Dundar F K, TugrulU.D, JitingY. A decision model for energy resource selection in China. Energy Policy. 2010; 38: 7130–7141.

Google Scholar

[3] Tolga Kaya, Cengiz Kahraman. Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications. 38 (2011) 6577-6585.

DOI: 10.1016/j.eswa.2010.11.081

Google Scholar

[4] Espen L. Use of multicriteria decision analysis methods for energy planning problems. Renewable & Sustainable Energy Reviews. 11(2007)1584-1595.

DOI: 10.1016/j.rser.2005.11.005

Google Scholar

[5] Jiangjiang Wang, Youyin Jing, Chunfa Zhang, Junhong Zhao. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews. 13 (2009) 2263–2278.

DOI: 10.1016/j.rser.2009.06.021

Google Scholar

[6] G.A. Mendoza, H. Martins. Multi-criteria decision analysis in natural resource management:A critical review of methods and new modelling paradigms. Forest Ecology and Management. 230 (2006) 1–22.

DOI: 10.1016/j.foreco.2006.03.023

Google Scholar

[7] Danae Diakoulaki, MCDA and energy planning. Multiple criteria decision analysis, chapter 21

Google Scholar

[8] Lan Gan, Xuehu Wang, Rong Li. Research and Implementation of AHP-based Method Base. 2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering

DOI: 10.1109/kese.2009.36

Google Scholar

[9] Seong Kon Lee, Yong Jin Yoon, Jong Wook Kim. A study on making a long-term improvement in the national energy efficiency and GHG control plans by the AHP approach. Energy Policy. 35 (2007) 2862–2868.

DOI: 10.1016/j.enpol.2006.09.019

Google Scholar

[10] S K Lee, G Mogi, S C Shin, J.W. Kim, An AHP/DEA hybrid model for measuring the relative efficiency of energy efficiency technologies. Proceedings of the 2007 IEEE IEEM.

DOI: 10.1109/ieem.2007.4419150

Google Scholar

[11] Jaap Spronk, Ralph E. Steuer, Constantin Zopounidis. Multicriteria decision aid/analysis in finance. Chapter 1.

Google Scholar

[12] G. Heinrich, L. Basson, B. Cohen, M. Howells, J. Petriea. Ranking and selection of power expansion alternatives for multiple objectives under uncertainty. Energy. 32 (2007) 2350–2369.

DOI: 10.1016/j.energy.2007.06.001

Google Scholar

[13] Paul Kailiponi. Analyzing evacuation decisions using multi-attribute utility theory (MAUT). Procedia Engineering. 3 (2010) 163–174.

DOI: 10.1016/j.proeng.2010.07.016

Google Scholar

[14] Espen L, Audun B, Arne T H. Use of the equivalent attribute technique in multi-criteria planning of local energy systems. European Journal of Operational Research. 197 (2009) 1075–1083.

DOI: 10.1016/j.ejor.2007.12.050

Google Scholar

[15] Tugrul U. Daim, Willy Schweinfort, Gulgun Kayakutlu, Noah Third. Identification of energy policy priorities from existing energy portfolios using hierarchical decision model and goal programming case of Germany and France. International Journal of Energy Sector Management. 2010 (4) 24-43.

DOI: 10.1108/17506221011033080

Google Scholar

[16] Pankaj Gupta, Mukesh Kumar Mehlawat, Garima Mittal. Asset portfolio optimization using support vector machines and real-coded genetic algorithm. J Glob Optim.

DOI: 10.1007/s10898-011-9692-3

Google Scholar

[17] Liu Xiaoli et al. China national energy strategy and policy 2020 subtitle 3: adjustment and optimization of energy supply structure in China. Energy Research Institute of NDRC.

Google Scholar

[18] Robert T.C, Terence R. Making hard decisons with decision tools. Duxbury Thomson Learning, 2001.

Google Scholar

[19] Wei Chen, Ling Yang, Wei-jun Xu, Yong-Ming Cai. Genetic Algorithm with an Application to Complex Portfolio Selection. Fourth International Conference on Natural Computation

DOI: 10.1109/icnc.2008.323

Google Scholar

[20] http://www.palisade.com/evolver/

Google Scholar