Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/112856
Título: Adaptive model predictive control for electricity management in the household sector
Autores/as: Colmenar-Santos, Antonio
Muñoz-Gómez, Antonio-Miguel
Rosales Asensio, Enrique 
Fernandez Aznar, Gregorio
Galan-Hernandez, Noemi
Clasificación UNESCO: 332205 Fuentes no convencionales de energía
332201 Distribución de la energía
Fecha de publicación: 2022
Publicación seriada: International Journal of Electrical Power and Energy Systems 
Resumen: 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.
URI: http://hdl.handle.net/10553/112856
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2021.107831
Fuente: International Journal of Electrical Power and Energy Systems [ISSN 0142-0615], n. 137, (Mayo 2022)
Colección:Artículos
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