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dc.contributor.authorRaveendran Nair, Unnikrishnan
dc.contributor.authorCosta Castelló, Ramon
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2020-03-17T14:27:34Z
dc.date.available2020-03-17T14:27:34Z
dc.date.issued2019
dc.identifier.citationRaveendran, U.; Costa-Castelló, R. An analysis of energy storage system interaction in a multi objective model predictive control based energy management in DC microgrid. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): proceedings: University of Zaragoza, Zaragoza, Spain: 10-13 September, 2019". 2019, p. 739-746.
dc.identifier.isbn978-1-7281-0303-7
dc.identifier.urihttp://hdl.handle.net/2117/180252
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractNon-deterministic generation from renewable sources have resulted in the incorporation energy storage systems in modern grids. Management of energy between different storage elements need to done optimally to ensure efficient operation of the grid. The intraday energy management problem is addressed in this work through an online model predictive control using multi objective optimisation. This work analyses the energy interaction among different storages when penalty weights in a multi objective optimisation problem is varied, in order to find an optimal scenario in terms of weight distribution. Different scenarios are identified and performance indices are proposed to achieve the same. The work also addresses implicitly the objective of minimising rate of degradation batteries. Simulation results are presented to aid in the analysis.
dc.format.extent8 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherModel predictive control
dc.subject.otherEnergy management
dc.subject.otherEnergy storages system
dc.subject.otherEegradation rate
dc.titleAn analysis of energy storage system interaction in a multi objective model predictive control based energy management in DC microgrid
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1109/ETFA.2019.8869474
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Control theory::Predictive control
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8869474
dc.rights.accessOpen Access
local.identifier.drac26580557
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/2PE/MDM-2016-0656
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//DPI2015-69286-C3-2-R/ES/ESTIMACION, DIAGNOSIS Y CONTROL PARA LA MEJORA DE LA EFICIENCIA Y LA VIDA UTIL DE LAS PILAS DE COMBUSTIBLE DE TIPO PEM/
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/675318/EU/Innovative controls for renewable sources Integration into smart energy systems/INCITE
dc.relation.projectidinfo:eu-repo/grantAgreement/ACC10/RIS3CAT/COMRDI15-1-0036-11
local.citation.authorRaveendran, U.; Costa-Castelló, R.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationName2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): proceedings: University of Zaragoza, Zaragoza, Spain: 10-13 September, 2019
local.citation.startingPage739
local.citation.endingPage746


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