Reference : Greenhouse gas emission reduction in residential buildings: A lightweight model to be...
Scientific journals : Article
Engineering, computing & technology : Computer science
Sustainable Development
Greenhouse gas emission reduction in residential buildings: A lightweight model to be deployed on edge devices
Ortiz, Paul [> >]
Kubler, Sylvain mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal]
Rondeau, Éric [> >]
McConky, Katie [> >]
Shukhobodskiy, Alexander Alexandrovich [> >]
Colantuono, Giuseppe [> >]
Georges, Jean-Philippe [> >]
Journal of cleaner production
Elsevier B.V
[en] Renewable energies ; Energy storage ; Battery
[en] Keywords Greenhouse gas emission; Energy efficiency; Photovoltaics; Battery; Edge computing; Linear programming Electricity produced and used in the residential sector is responsible for approximately 30% of the greenhouse gas emissions (GHGE). Insulating houses and integrating renewable energy and storage resources are key for reducing such emissions. However, it is not only a matter of installing renewable energy technologies but also of optimizing the charging/discharging of the storage units. A number of optimization models have been proposed lately to address this problem. However, they are often limited in several respects: (i) they often focus only on electricity bill reduction, placing GHGE reduction on the backburner; (ii) they rarely propose hybrid-energy storage optimization strategies considering thermal and storage heater units; (iii) they are often designed using Linear Programming (LP) or metaheuristic techniques that are computational intensive, hampering their deployment on edge devices; and (iv) they rarely evaluate how the model impacts on the battery lifespan. Given this state-of-affairs, the present article compares two approaches, the first one proposing an innovative sliding grid carbon intensity threshold algorithm developed as part of a European project named RED WoLF, the second one proposing an algorithm designed based on LP. The comparison analysis is carried out based on two distinct real-life scenarios in France and UK. Results show that both algorithms contribute to reduce GHGE compared to a solution without optimization logic (between 10 to 25%), with a slight advantage for the LP algorithm. However, RED WoLF makes it possible to reduce significantly the computational time ([almost equal to]25 min for LP against [almost equal to]1 ms for RED WoLF) and to extend the battery lifespan (4 years for LP against 12 years for RED WoLF). Author Affiliation: (a) Université de Lorraine, CNRS, CRAN, F-54000, France (b) Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, L-1359 Esch-sur-Alzette, Luxembourg (c) Department of Industrial and Systems Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, NY 14623, United States (d) School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK * Corresponding author. Article History: Received 12 March 2022; Revised 3 June 2022; Accepted 8 July 2022 (miscellaneous) Handling Editor: Panos Seferlis Byline: Paul Ortiz [] (a,*), Sylvain Kubler [] (a,b), Éric Rondeau [] (a), Katie McConky [] (c), Alexander Alexandrovich Shukhobodskiy [] (d), Giuseppe Colantuono [] (d), Jean-Philippe Georges [] (a)
COPYRIGHT 2022 Elsevier B.V.

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