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Planning carbon emission trading for Beijing's electric power systems under dual uncertainties


  • Zhu, Y.
  • Li, Y.P.
  • Huang, G.H.


In this study, a full-infinite interval-stochastic mixed-integer programming (FIMP) method is developed for planning carbon emission trading (CET) under dual uncertainties. FIMP has advantages in uncertainty reflection and policy analysis, particularly when the input parameters are provided as crisp and functional intervals as well as probabilistic distributions. The developed FIMP is applied to a real case study for managing carbon dioxide (CO2) emissions with trading scheme of Beijing's electric power system (EPS). Electric power industry is one of the major sources of CO2 emission in China. It is essential to accumulate relevant experience to provide a reliable basis for establishing a regional or national CET market, so as to prepare for docking with the international market. This is the first attempt to introduce CET scheme into Beijing's EPS to mitigate CO2 emissions. The solutions for energy supply, electricity generation, carbon-quota allocation, and capacity expansion are obtained. They cannot only be used for formulating CO2-reduction policies and assessing the associated economic implications in purchasing emission permits or bearing economic penalties, but also facilitate analyzing various policies when pre-regulated electricity-generation plans and pre-defined CO2-emission schemes are violated.

Suggested Citation

  • Zhu, Y. & Li, Y.P. & Huang, G.H., 2013. "Planning carbon emission trading for Beijing's electric power systems under dual uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 113-128.
  • Handle: RePEc:eee:rensus:v:23:y:2013:i:c:p:113-128
    DOI: 10.1016/j.rser.2013.02.033

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    Cited by:

    1. Zhou, Y. & Li, Y.P. & Huang, G.H., 2015. "Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty," Applied Energy, Elsevier, vol. 140(C), pages 350-364.
    2. repec:eee:rensus:v:74:y:2017:i:c:p:1314-1322 is not listed on IDEAS
    3. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
    4. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2016. "Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings," Omega, Elsevier, vol. 63(C), pages 48-59.
    5. Zhu, Y. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2015. "A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty," Energy, Elsevier, vol. 88(C), pages 636-649.
    6. repec:eee:appene:v:210:y:2018:i:c:p:60-74 is not listed on IDEAS
    7. Chen, C. & Li, Y.P. & Huang, G.H., 2016. "Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China," Renewable Energy, Elsevier, vol. 86(C), pages 1161-1177.
    8. Li, Y.P. & Huang, G.H. & Li, M.W., 2014. "An integrated optimization modeling approach for planning emission trading and clean-energy development under uncertainty," Renewable Energy, Elsevier, vol. 62(C), pages 31-46.
    9. Xu, Jiuping & Yang, Xin & Tao, Zhimiao, 2015. "A tripartite equilibrium for carbon emission allowance allocation in the power-supply industry," Energy Policy, Elsevier, vol. 82(C), pages 62-80.
    10. Jiang, Jingjing & Xie, Dejun & Ye, Bin & Shen, Bo & Chen, Zhanming, 2016. "Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook," Applied Energy, Elsevier, vol. 178(C), pages 902-917.


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