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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International
dc.contributor.authorUnamuno, Eneko
dc.contributor.authorCABEZUELO ROMERO, DAVID
dc.date.accessioned2024-02-02T08:52:59Z
dc.date.available2024-02-02T08:52:59Z
dc.date.issued2024
dc.identifier.issn2169-3536
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=176419
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6188
dc.description.abstractIn this paper, a smart machine-learning-based energy management system (MLBEMS) is developed for a hybrid energy storage system (HESS). This HBESS consists of batteries with high-energy (HE) and high-power (HP) characteristics, to provide grid-supporting services. The aim of the MLBEMS is to improve the overall battery lifetime and achieve state-of-charge (SoC) balancing for two different use cases (UC). UC1 involves enhanced frequency regulation for the Pan-European grid, while UC2 pertains to an electric vehicle (EV) charging station with photovoltaic (PV) generation. The designed MLBEMS is compared with a rule-based energy management system (RBEMS) from the literature with similar use cases. To ensure optimal power sharing between the battery modules, an optimization model is created using real battery aging data. Using a genetic algorithm, optimal power sharing is achieved for various initial SoC conditions. The generated dataset is subsequently utilized to train a machine-learning regression model, and the resulting prediction function is imported into MATLAB/Simulink. In UC1, MLBEMS achieved a 39.3% better SoC balancing compared to RBEMS, along with 36.5% and 22.6% higher battery lifetimes for HE and HP batteries, respectively. Similarly, for UC2, MLBEMS achieved a 68.5% improvement in SoC balancing, along with 53.6% and 45.8% higher battery lifetimes for HE and HP batteries, respectively.
dc.language.isoeng
dc.publisherIEEE
dc.rights© 2024 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEnergy management system
dc.subjecthybrid energy storage system
dc.subjectmachine learning
dc.subjectstationary storage system
dc.titleDevelopment and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
dcterms.sourceIEEE Access
local.contributor.groupSistemas electrónicos de potencia aplicados al control de la energía eléctrica
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1109/ACCESS.2024.3381864
local.contributor.otherinstitutionhttps://ror.org/006e5kg04
local.source.detailsVol. 12
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
oaire.funderNameEuropean Commission
oaire.funderIdentifierhttps://ror.org/00k4n6c32 http://data.crossref.org/fundingdata/funder/10.13039/501100000780
oaire.fundingStreamH2020
oaire.awardNumber963527
oaire.awardTitleInteroperable, modular and smart hybrid energy storage system for stationary application (ISTORMY)
oaire.awardURIhttps://doi.org/10.3030/963527


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