Pattern-driven behaviour for demand-side management: an analysis of appliance use
Authors
Cruz de la Torre, Carlos; Tostado Véliz, Marcos; Palomar González, Esther; Bravo Muñoz, IgnacioIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60887DOI: 10.1016/j.enbuild.2024.113988
ISSN: 0378-7788
Publisher
Elsevier
Date
2024-02-15Funders
Agencia Estatal de Investigación
Bibliographic citation
Cruz de la Torre, C., Tostado Véliz, M., Palomar González, E. & Bravo Muñoz, I. 2024, "Pattern-driven behaviour for demand-side management: an analysis of appliance use", Energy and Buildings, vol. 308, art. no. 113988, pp. 1-14.
Keywords
Energy and behaviour
Energy modelling
Demand profiling
Demand-side management
Load patterns
Controllable appliances
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-132700B-I00
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1016/j.enbuild.2024.113988Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2024 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities.
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