A PV ramp-rate control strategy to extend battery lifespan using forecasting

This study analyses and presents a new ramp-rate control algorithm for smoothing PV power fluctuations, designed to address three fundamental objectives: to reduce battery cycling, to meet minimum storage requirements and to be able to operate, without ramp-rate violations, with real publicly-availa...

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Autores: González Moreno, Alejandro, Marcos Álvarez, Javier, Parra Laita, Íñigo de la, Marroyo Palomo, Luis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/43690
Acceso en línea:https://hdl.handle.net/2454/43690
Access Level:acceso abierto
Palabra clave:PV fluctuations
PV smoothing
Ramp-rate limitation
Energy storage Systems (ESS)
PV integration
Levelized Cost of Energy (LCOE)
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spelling A PV ramp-rate control strategy to extend battery lifespan using forecastingGonzález Moreno, AlejandroMarcos Álvarez, JavierParra Laita, Íñigo de laMarroyo Palomo, LuisPV fluctuationsPV smoothingRamp-rate limitationEnergy storage Systems (ESS)PV integrationLevelized Cost of Energy (LCOE)This study analyses and presents a new ramp-rate control algorithm for smoothing PV power fluctuations, designed to address three fundamental objectives: to reduce battery cycling, to meet minimum storage requirements and to be able to operate, without ramp-rate violations, with real publicly-available forecasting. The algorithm was compared to three benchmark methods and, as a performance limit, also to a hypothetical perfect prediction. Different performance variables were analyzed for all the strategies within a restricted ramp-rate constraint (2%/min): minimum storage requirement, battery power distributions, throughput energy, state of charge (SOC) distributions, degradation (calendar and cycling), expected battery lifespan and levelized cost of energy (LCOE). The proposal proves to be the most cost-effective smoothing technique and the simulation results show that its performance is comparable to the obtained with the use of an assumed perfect prediction.Support of the Spanish State Research Agency (AEI) under grants PID2019111262RB-I00 and PID2019-110816RB-C21. Open access funding provided by Universidad Pública de Navarra.ElsevierIngeniería Eléctrica, Electrónica y de ComunicaciónIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/43690reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111262RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110816RB-C21© 2022 The Authors. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/bync-nd/4.0/https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/436902026-06-17T12:41:47Z
dc.title.none.fl_str_mv A PV ramp-rate control strategy to extend battery lifespan using forecasting
title A PV ramp-rate control strategy to extend battery lifespan using forecasting
spellingShingle A PV ramp-rate control strategy to extend battery lifespan using forecasting
González Moreno, Alejandro
PV fluctuations
PV smoothing
Ramp-rate limitation
Energy storage Systems (ESS)
PV integration
Levelized Cost of Energy (LCOE)
title_short A PV ramp-rate control strategy to extend battery lifespan using forecasting
title_full A PV ramp-rate control strategy to extend battery lifespan using forecasting
title_fullStr A PV ramp-rate control strategy to extend battery lifespan using forecasting
title_full_unstemmed A PV ramp-rate control strategy to extend battery lifespan using forecasting
title_sort A PV ramp-rate control strategy to extend battery lifespan using forecasting
dc.creator.none.fl_str_mv González Moreno, Alejandro
Marcos Álvarez, Javier
Parra Laita, Íñigo de la
Marroyo Palomo, Luis
author González Moreno, Alejandro
author_facet González Moreno, Alejandro
Marcos Álvarez, Javier
Parra Laita, Íñigo de la
Marroyo Palomo, Luis
author_role author
author2 Marcos Álvarez, Javier
Parra Laita, Íñigo de la
Marroyo Palomo, Luis
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica, Electrónica y de Comunicación
Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.subject.none.fl_str_mv PV fluctuations
PV smoothing
Ramp-rate limitation
Energy storage Systems (ESS)
PV integration
Levelized Cost of Energy (LCOE)
topic PV fluctuations
PV smoothing
Ramp-rate limitation
Energy storage Systems (ESS)
PV integration
Levelized Cost of Energy (LCOE)
description This study analyses and presents a new ramp-rate control algorithm for smoothing PV power fluctuations, designed to address three fundamental objectives: to reduce battery cycling, to meet minimum storage requirements and to be able to operate, without ramp-rate violations, with real publicly-available forecasting. The algorithm was compared to three benchmark methods and, as a performance limit, also to a hypothetical perfect prediction. Different performance variables were analyzed for all the strategies within a restricted ramp-rate constraint (2%/min): minimum storage requirement, battery power distributions, throughput energy, state of charge (SOC) distributions, degradation (calendar and cycling), expected battery lifespan and levelized cost of energy (LCOE). The proposal proves to be the most cost-effective smoothing technique and the simulation results show that its performance is comparable to the obtained with the use of an assumed perfect prediction.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/43690
url https://hdl.handle.net/2454/43690
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111262RB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110816RB-C21
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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