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A stochastic‐fuzzy programming model with soften constraints for electricity generation planning with greenhouse‐gas abatement
Author(s) -
Li Y. P.,
Huang G. H.
Publication year - 2012
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.2885
Subject(s) - greenhouse gas , electricity , electricity generation , fossil fuel , environmental science , stochastic programming , context (archaeology) , environmental economics , climate change , mathematical optimization , environmental engineering , natural resource economics , economics , engineering , waste management , mathematics , power (physics) , physics , quantum mechanics , electrical engineering , ecology , paleontology , biology
SUMMARY Increased atmospheric CO 2 concentration is widely being considered as the main driving factor that causes the phenomenon of global warming, due to the ever‐boosting use of fossil fuels. In this study, a fuzzy‐stochastic programming model with soft constraints (FSP‐SC) is developed for electricity generation planning and greenhouse gas (GHG) abatement in an environment with imprecise and probabilistic information. The developed FSP‐SC is applied to a case study of long‐term planning of a regional electricity generation system, where integer programming technique is employed to facilitate dynamic analysis for capacity expansion within a multi‐period context to satisfy increasing electricity demand. The results indicate different relaxation levels can lead to changed electricity generation options, capacity expansion schemes, system costs, and GHG emissions. Several sensitivity analyses are also conducted to demonstrate that relaxation of different constraints have different effects on system cost and GHG emission. Tradeoffs among system costs, resource availabilities, GHG emissions, and electricity‐shortage risks can also be tackled with the relaxation levels for the objective and constraints. Copyright © 2012 John Wiley & Sons, Ltd.