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A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty

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  • Zhu, Y.
  • Li, Y.P.
  • Huang, G.H.
  • Fan, Y.R.
  • Nie, S.

Abstract

In this study, a FFSP (full-infinite fuzzy stochastic programming) method is developed for planning MEPS (municipal electric power systems) associated with GHG (greenhouse gas) control under uncertainty. FFSP can deal with multiple uncertainties presented in terms of fuzzy sets, functional intervals, and random variables. FFSP is also applied to a case study of Beijing for managing MEPS, and reducing the GHG emission through introducing the EU ETS (European Union greenhouse gas emission trading scheme). The results indicate that reasonable solutions have been generated, which can be used for generating schemes of energy resources, electricity production/allocation, and capacity expansion under various economic costs and GHG reduction requirements. The case study demonstrates that FFSP can increase the abilities of reflecting complexities for dynamics of capacity expansion and interaction of multiple uncertainties in MEPS. The results allow in-depth analyses of trade-offs between GHG mitigation and economic objective as well as those between system cost and decision makers' satisfaction degree. Besides, this study can also provide an example to help China construct domestic carbon trading market at municipal scale for addressing the challenges of global climate change.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:88:y:2015:i:c:p:636-649
    DOI: 10.1016/j.energy.2015.05.106
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    3. Barukčić, M. & Hederić, Ž. & Hadžiselimović, M. & Seme, S., 2018. "A simple stochastic method for modelling the uncertainty of photovoltaic power production based on measured data," Energy, Elsevier, vol. 165(PB), pages 246-256.
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    5. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.
    6. Nie, S. & Huang, Charley Z. & Huang, G.H. & Li, Y.P. & Chen, J.P. & Fan, Y.R. & Cheng, G.H., 2016. "Planning renewable energy in electric power system for sustainable development under uncertainty – A case study of Beijing," Applied Energy, Elsevier, vol. 162(C), pages 772-786.
    7. Lu, W.T. & Dai, C. & Fu, Z.H. & Liang, Z.Y. & Guo, H.C., 2018. "An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint," Energy, Elsevier, vol. 142(C), pages 1023-1039.
    8. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
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    11. 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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