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• W3202981196 endingPage "107710" @default.
• W3202981196 startingPage "107710" @default.
• W3202981196 abstract "• A chance-constrained programming-based energy system model is proposed. • The proposed model quantifies the risk of not meeting emission targets. • A solution approach that combines two sampling-based methods is proposed. • A newly devised heuristic algorithm is augmented in the solution approach. • Our proposed model and solution approach are verified via a numerical study. In order to achieve carbon neutrality, several countries and regions have set specific targets for the reduction of carbon emissions. Although many policy makers agree on the uncertainty of the several factors which are involved in the planning of future energy systems, they have yet to consider the risks that their policies will incur, should they fail to meet current carbon emission targets. In this study, we propose a stochastic programming-based energy system model which takes into account various economic and technical uncertainty factors in addition to a chance-constraint, in order to assess the feasibility of meeting carbon emission targets. To determine the chance-constrained programming model efficiently, we propose a solution-based approach which adopts a sample average approximation approach. In addition, we combine the sampling-and-discarding approach with a newly devised heuristic algorithm that is suitable for our proposed model. Furthermore, as it is a numerical study, we apply our model and solution approach to evaluate the long-term capacity expansion plan under the carbon emission target within the Korean electricity sector. Through this numerical study, we verify both the solution quality and computational time of our solution approach, as well as the usability of our model for the trade-off analysis between costs and risks." @default.
• W3202981196 created "2021-10-11" @default.
• W3202981196 creator A5033840544 @default.
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• W3202981196 date "2021-12-01" @default.
• W3202981196 modified "2023-09-24" @default.
• W3202981196 title "A sampling-based solution approach for electricity capacity expansion planning with chance constraint" @default.
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• W3202981196 doi "https://doi.org/10.1016/j.cie.2021.107710" @default.
• W3202981196 hasPublicationYear "2021" @default.
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