Real-time demand response in smart homes through direct lookahead approximation

dc.contributorTabares Pozos, Alejandra
dc.contributorSabillon, Carlos Francisco
dc.contributorMedina Sánchez, Pablo Alexander
dc.contributorCentro para la Optimizacion y Probabilidad Aplicada (COPA)
dc.creatorPico Garrido, Juan Camilo
dc.date.accessioned2023-05-30T19:13:35Z
dc.date.accessioned2023-09-06T23:34:52Z
dc.date.available2023-05-30T19:13:35Z
dc.date.available2023-09-06T23:34:52Z
dc.date.created2023-05-30T19:13:35Z
dc.date.issued2023-05-24
dc.identifierhttp://hdl.handle.net/1992/66990
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8726643
dc.description.abstractDemand Response (DR) programs play a pivotal role in managing the balance of energy supply and demand, particularly in the era of expanding Renewable Energy Resources (RES) integration into the smart grid. By encouraging consumers to shift their power usage to off-peak periods or times of high RES output, these programs alleviate grid pressure. Recent technological advancements have facilitated the deployment of DR programs in residential contexts, leveraging Home Energy Management Systems (HEMS) for controlling household appliances. However, challenges arise due to the stochastic nature of RES, fluctuating intra-day electricity prices, and varying consumer demand, making appliance scheduling a complex, dynamic problem. To navigate this issue, we introduce a direct lookahead approximation approach for HEMS. This strategy manages shiftable appliance usage and energy storage, aiming to minimize user discomfort and electricity costs. The algorithm takes into account the dynamic nature of the system, incorporating uncertain information about prices, energy production, and demand. The proposed method has demonstrated significant effectiveness, reducing electricity costs and grid consumption in a smart house by 145% and 25% respectively. Our research underscores the efficacy of sequential decision-making techniques in managing domestic energy consumption, particularly within the context of smart grids and RES integration.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Ingeniería Industrial
dc.publisherFacultad de Ingeniería
dc.publisherDepartamento de Ingeniería Industrial
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleReal-time demand response in smart homes through direct lookahead approximation
dc.typeTrabajo de grado - Maestría


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