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An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty

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  • Li, Y.P.
  • Huang, G.H.
  • Chen, X.

Abstract

In this study, an interval-valued minimax regret analysis (IMRA) method is proposed for planning greenhouse gas (GHG) abatement under uncertainty. The IMRA method is a hybrid of interval-parameter programming (IPP) and minimax regret analysis (MMR) techniques. The developed method is applied to support long-term planning of GHG mitigation in an energy system under uncertainty. Mixed integer linear programming (MILP) technique with fixed-charge cost function is introduced into the IMRA framework to facilitate dynamic analysis for decisions of timing, sizing and siting in planning capacity expansions for power-generation facilities. The results obtained indicate that replacing fossil fuels with renewable energy sources (i.e. hydro, wind and solar power) can effectively facilitate reducing the GHG emissions. They can help decision makers identify an optimal strategy that can facilitate reducing the worst regret level incurred under any outcome of the uncertain GHG-abatement target.

Suggested Citation

  • Li, Y.P. & Huang, G.H. & Chen, X., 2011. "An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty," Energy Policy, Elsevier, vol. 39(7), pages 4313-4324, July.
    • Handle: RePEc:eee:enepol:v:39:y:2011:i:7:p:4313-4324
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    4. Ma, Y. & Li, Y.P. & Huang, G.H., 2023. "Planning China’s non-deterministic energy system (2021–2060) to achieve carbon neutrality," Applied Energy, Elsevier, vol. 334(C).
    5. Anastasia Soukhov & Ahmed Foda & Moataz Mohamed, 2022. "Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach," Energies, MDPI, vol. 15(19), pages 1-21, September.
    6. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    7. Li, M.W. & Li, Y.P. & Huang, G.H., 2011. "An interval-fuzzy two-stage stochastic programming model for planning carbon dioxide trading under uncertainty," Energy, Elsevier, vol. 36(9), pages 5677-5689.
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    9. 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.
    10. Georgios P. Trachanas & Aikaterini Forouli & Nikolaos Gkonis & Haris Doukas, 2020. "Hedging uncertainty in energy efficiency strategies: a minimax regret analysis," Operational Research, Springer, vol. 20(4), pages 2229-2244, December.
    11. 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.
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