Adaptive model predictive control for electricity management in the household sector

Colmenar-Santos, Antonio, Muñoz-Gómez, Antonio-Miguel, Rosales-Asensio, Enrique, Fernandez Aznar, Gregorio y Galan-Hernandez, Noemi . (2022) Adaptive model predictive control for electricity management in the household sector. International Journal of Electrical Power & Energy Systems

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Título Adaptive model predictive control for electricity management in the household sector
Autor(es) Colmenar-Santos, Antonio
Muñoz-Gómez, Antonio-Miguel
Rosales-Asensio, Enrique
Fernandez Aznar, Gregorio
Galan-Hernandez, Noemi
Materia(s) Ingeniería Industrial
Abstract This 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.
Palabras clave Adaptive Model Predictive Control
Home Energy Management System
Smart Grid
Demand Response
Distributed Energy Resources
Artificial Neural Network
Genetic Algorithm
Editor(es) Elsevier
Fecha 2022-05
Formato application/pdf
Identificador bibliuned:DptoIEEC-ETSI-Articulos-Acolmenar-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoIEEC-ETSI-Articulos-Acolmenar-0001
DOI - identifier 10.1016/j.ijepes.2021.107831
ISSN - identifier 0142-0615
Nombre de la revista International Journal of Electrical Power & Energy Systems
Número de Volumen 137
Publicado en la Revista International Journal of Electrical Power & Energy Systems
Idioma eng
Versión de la publicación acceptedVersion
Tipo de recurso Article
Derechos de acceso y licencia info:eu-repo/semantics/embargoedAccess
Tipo de acceso Acceso embargado
Notas adicionales The registered version of this article, first published in International Journal of Electrical Power & Energy Systems, is available online at the publisher's website: Elsevier https://doi.org/10.1016/j.ijepes.2021.107831
Notas adicionales La versión registrada de este artículo, publicado por primera vez en International Journal of Electrical Power & Energy Systems, está disponible en línea en el sitio web del editor: Elsevier https://doi.org/10.1016/j.ijepes.2021.107831

 
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Creado: Sat, 03 Feb 2024, 03:56:00 CET