Genetic algorithm model to control peak demand to defer capacity investment

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dc.contributor.authorAlamos Guzmán, Oscar Marcelo.
dc.contributor.authorRudnick Van de Wyngard, Hugh
dc.date.accessioned2022-05-16T13:00:32Z
dc.date.available2022-05-16T13:00:32Z
dc.date.issued2012
dc.description.abstractThis paper formulates and develops a peak demand control tool for electric systems within the framework of direct load control plans. This tool allows defining a load dispatch centre for central air conditioning systems in commercial buildings, hence allowing a measured control of peak demand for such pieces of equipment, which are known for their important influence in the end customers' consumption and for the correlation their demand curve has with the system demand curve during summer months. Traditionally, this type of application has been developed in the field of demand management; however, the high energy consumption growth rates have taken electric firms to analyze their application on the system expansion planning, hence deferring, or even preventing, the need to invest in capacity to supply the demand during peak periods. The generic model presented herein is evaluated in an actual urban substation, characterized by a predominant commercial consumption, by the contribution of the air conditioning systems in the substation loads, and by the problems present in its capacity to expand; model that is solved through advanced genetic algorithm techniques.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/PESGM.2012.6344706
dc.identifier.eisbn9781467327299
dc.identifier.eissn1944-9925
dc.identifier.isbn9781467327275
dc.identifier.issn1932-5517
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6344706
dc.identifier.urihttps://doi.org/10.1109/PESGM.2012.6344706
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63945
dc.information.autorucEscuela de ingeniería ; Alamos Guzmán, Oscar Marcelo. ; S/I ; 121628
dc.information.autorucEscuela de ingeniería ; Rudnick van de Wyngard, Hugh ; S/I ; 99167
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofIEEE Power and Energy Society General Meeting (2012 : San Diego, Estados Unidos)
dc.rightsacceso restringido
dc.subjectBuildings
dc.subjectLoad modeling
dc.subjectOptimization
dc.subjectAtmospheric modeling
dc.subjectGenetic algorithms
dc.subjectTemperature
dc.subjectMathematical model
dc.titleGenetic algorithm model to control peak demand to defer capacity investmentes_ES
dc.typecomunicación de congreso
sipa.codpersvinculados121628
sipa.codpersvinculados99167
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