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Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model

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  • Lin, Q.G.
  • Huang, G.H.

Abstract

Concerns over increasing energy price, exacerbating power shortage and changing climatic conditions are emerging associated with municipal energy management systems. Most of the previous studies could hardly address multiple uncertainties that exist in such systems; this may lead to incomplete, simplified or even false solutions, and could jeopardize the robustness of management decisions. This study is to develop a dynamic inexact stochastic energy systems planning model (DITS-MEM) for managing municipal energy systems and greenhouse-gas (GHG) emissions under uncertainty. Through integrating mixed-integer, interval-parameter and two-stage stochastic programming techniques, DITS-MEM can handle not only the uncertainties expressed as interval values and probabilistic distributions associated with GHG-emission reduction targets, but also the dynamics of capacity-expansion issues. The developed model is then applied to the Beijing Municipality. The results suggest that the DITS-MEM model is applicable in reflecting complexities of multi-uncertainty, dynamic and interactive municipal energy management systems, and capable of addressing two-stage stochastic problem of GHG-emission reduction. The obtained solutions can provide decision bases for formulating GHG-reduction policies and assessing the associated economic implications in purchasing emission credits or bearing economic penalties.

Suggested Citation

  • Lin, Q.G. & Huang, G.H., 2009. "Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model," Energy Policy, Elsevier, vol. 37(11), pages 4463-4473, November.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:11:p:4463-4473
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    1. Lin, Q.G. & Huang, G.H., 2009. "A dynamic inexact energy systems planning model for supporting greenhouse-gas emission management and sustainable renewable energy development under uncertainty--A case study for the City of Waterloo,," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1836-1853, October.
    2. Frei, Christoph W. & Haldi, Pierre-Andre & Sarlos, Gerard, 2003. "Dynamic formulation of a top-down and bottom-up merging energy policy model," Energy Policy, Elsevier, vol. 31(10), pages 1017-1031, August.
    3. Chang-Qing Li & Paul Suding & Chang Qing & Zheng Yan, 2004. "A study on integrated programming model of urban energy supplying based on sustainable development," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 22(2/3/4), pages 99-118.
    4. Lin, Q.G. & Huang, G.H. & Bass, B. & Qin, X.S., 2009. "IFTEM: An interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty," Energy Policy, Elsevier, vol. 37(3), pages 868-878, March.
    5. Muela, E. & Schweickardt, G. & Garces, F., 2007. "Fuzzy possibilistic model for medium-term power generation planning with environmental criteria," Energy Policy, Elsevier, vol. 35(11), pages 5643-5655, November.
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    21. Dong, C. & Huang, G.H. & Cai, Y.P. & Liu, Y., 2012. "An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city," Energy, Elsevier, vol. 37(1), pages 673-688.
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