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dc.contributor.authorOlivella-Rosell, Pol
dc.contributor.authorRullan, Francesc
dc.contributor.authorLloret-Gallego, Pau
dc.contributor.authorPrieto-Araujo, Eduardo
dc.contributor.authorFerrer-San-José, Ricard
dc.contributor.authorBarja-Martinez, Sara
dc.contributor.authorBjarghov, Sigurd
dc.contributor.authorLakshmanan, Venkatachalam
dc.contributor.authorHentunen, Ari
dc.contributor.authorForsström, Juha
dc.contributor.authorOttesen, Stig Ødegaard
dc.contributor.authorVillafafila-Robles, Roberto
dc.contributor.authorSumper, Andreas
dc.date.accessioned2021-03-02T14:24:56Z
dc.date.available2021-03-02T14:24:56Z
dc.date.created2021-02-10T17:15:59Z
dc.date.issued2020
dc.identifier.citationIEEE Transactions on Smart Grid. 2020, 11 (4), 3257-3269.en_US
dc.identifier.issn1949-3053
dc.identifier.urihttps://hdl.handle.net/11250/2731216
dc.description.abstractThe recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleCentralised and Distributed Optimization for Aggregated Flexibility Services Provisionen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber3257-3269en_US
dc.source.volume11en_US
dc.source.journalIEEE Transactions on Smart Griden_US
dc.source.issue4en_US
dc.identifier.doi10.1109/TSG.2019.2962269
dc.identifier.cristin1888656
dc.description.localcodeThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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