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dc.contributor.authorZaferanlouei, Salman
dc.contributor.authorFarahmand, Hossein
dc.contributor.authorVadlamudi, Vijay Venu
dc.contributor.authorKorpås, Magnus
dc.date.accessioned2021-02-23T09:23:01Z
dc.date.available2021-02-23T09:23:01Z
dc.date.created2021-01-30T21:15:33Z
dc.date.issued2021
dc.identifier.issn0885-8950
dc.identifier.urihttps://hdl.handle.net/11250/2729683
dc.description.abstractIn this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm. Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed Schur-Complement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discusseden_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleBATTPOWER Toolbox: Memory-Efficient and High-Performance Multi-Period AC Optimal Power Flow Solveren_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.journalIEEE Transactions on Power Systemsen_US
dc.identifier.doi10.1109/TPWRS.2021.3055429
dc.identifier.cristin1883463
dc.description.localcode© 2020 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.en_US
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode2


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