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• W2035182483 abstract "In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements." @default.
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• W2035182483 date "2010-12-01" @default.
• W2035182483 modified "2023-09-24" @default.
• W2035182483 title "An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty" @default.
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• W2035182483 doi "https://doi.org/10.1016/j.energy.2010.09.045" @default.
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