An Inexact Programming Model for Regional Energy Systems Planning and GHG-Emission Control

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dc.contributor.author Li, Gongchen
dc.contributor.author Chen, Yumin
dc.date.accessioned 2011-04-13T20:55:28Z
dc.date.available 2011-04-13T20:55:28Z
dc.date.issued 2011-04-01
dc.identifier.uri http://hdl.handle.net/10294/3283
dc.description.abstract A regional energy system consists of diverse forms of energy. Energy-related issues such as utilization of renewable energy and reduction of green house gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi-stage stochastic programming regional energy systems planning model (IMSPREM) was developed for supporting regional energy systems management and green house gas control under uncertainty. The IMSP-REM is a hybrid methodology of inexact optimization and multi-stage stochastic programming. It can not only handle uncertainties presented as intervals and probability density functions, but also reflect dynamics of system conditions over multiple planning stages. The developed IMSPREM was applied to a hypothetical regional energy system. The results indicate that the IMSP-REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy systems planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. en_US
dc.language.iso en en_US
dc.publisher University of Regina Graduate Students' Association en_US
dc.relation.ispartofseries Session 2.3 en_US
dc.subject Greenhouse gas en_US
dc.subject Mitigation en_US
dc.subject Energy model en_US
dc.subject Uncertainty en_US
dc.title An Inexact Programming Model for Regional Energy Systems Planning and GHG-Emission Control en_US
dc.type Presentation en_US
dc.description.authorstatus Student en_US
dc.description.peerreview yes en_US


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