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Adaptive model predictive control for electricity management in the household sector

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dc.contributor.authorMuñoz Gómez, Antonio Miguel
dc.contributor.authorRosales Asensio, Enrique
dc.contributor.authorFernández Aznar, Gregorio
dc.contributor.authorGalán Hernández, Noemi
dc.contributor.authorColmenar Santos, Antonio
dc.date.accessioned2024-05-20T11:40:26Z
dc.date.available2024-05-20T11:40:26Z
dc.date.issued2022-05
dc.description.abstractThis paper focuses on the optimisation of electricity consumption in residential buildings. To deal with the increase in electricity consumption, the intermittency of renewable energy generation and grid contingencies, a greater effort is required towards residential management optimisation. A novel adaptive model predictive control algorithm is proposed to achieve this objective. The challenges for this research included recognising and modelling the economic and technical constraints of the sources and appliances and addressing the uncertainties concerning the weather and user behaviour. Data-driven models are developed and trained to predict the user behaviour and buildings. Artificial neural networks and statistical models based on the weighted moving average are proposed to capture the patterns of deferrable and non-deferrable appliances, battery storage, electric vehicles, photovoltaic modules, buildings and grid connections. A dual optimisation method is devised to minimise the electricity bill and achieve thermal comfort. The proposed optimisation solver is a two-step optimisation method based on genetic algorithm and mixed integer linear programming. A comprehensive simulation study was carried out to reveal the effectiveness of the proposed method through a set of simulation scenarios. The results of the quantitative analysis undertaken as part of this study show the effectiveness of the proposed algorithm towards reducing electricity charges and improving grid elasticity.en
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1016/j.ijepes.2021.107831
dc.identifier.issn0142-0615
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12399
dc.journal.titleInternational Journal of Electrical Power & Energy Systems
dc.journal.volume137
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingenieros Industriales
dc.relation.departmentIngeniería Eléctrica, Electrónica, Control, Telemática y Química Aplicada a la Ingeniería
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsAdaptive Model Predictive Control
dc.subject.keywordsHome Energy Management System
dc.subject.keywordsSmart Grid
dc.subject.keywordsDemand Response
dc.subject.keywordsDistributed Energy Resources
dc.subject.keywordsArtificial Neural Network
dc.subject.keywordsGenetic Algorithm
dc.titleAdaptive model predictive control for electricity management in the household sectores
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublication46632e74-cbcb-4861-8ffb-b8b7ba55284f
relation.isAuthorOfPublication.latestForDiscovery46632e74-cbcb-4861-8ffb-b8b7ba55284f
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