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DC poleHodnotaJazyk
dc.contributor.authorFaraji, Jamal
dc.contributor.authorHashemi Dezaki, Hamed
dc.contributor.authorKetabi, Abbas
dc.date.accessioned2021-01-18T11:00:22Z-
dc.date.available2021-01-18T11:00:22Z-
dc.date.issued2020
dc.identifier.citationFARAJI, J., HASHEMI DEZAKI, H., KETABI, A. Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran. Sustainable Energy Technologies and Assessments, 2020, roč. 42, č. December 2020, s. 1-19. ISSN 2213-1388.cs
dc.identifier.issn2213-1388
dc.identifier.uri2-s2.0-85091339316
dc.identifier.urihttp://hdl.handle.net/11025/42496
dc.format19 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofseriesSustainable Energy Technologies and Assessmentsen
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© Elsevieren
dc.titleMulti-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iranen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedAlthough much efforts have been devoted to the optimal design of the energy systems, there is a research gap about the multi-year load growth-based optimal planning of microgrids. This paper tries to fill such a research gap by developing a novel method for the optimal design of the grid-connected microgrids based on the longterm load demand forecasting. The multilayer perceptron artificial neural network is used for time-series load prediction. The impacts of the annual load growth are analyzed under various cases based on the consideration and determination methods of yearly load growth. The proposed method is applied to an actual microgrid in Tehran, Iran, using HOMER (Hybrid Optimization of Multiple Energy Resources) software. The load modeling’s capabilities of HOMER software, as a well-known software for the optimal design of energy systems, are used, which have received less attention. Since most existing research works in Iran focused on the off-grid operating mode, the study of an actual microgrid under grid-connected operating mode is one of the most contributions of this paper. The comparison of the obtained results and other available methods illustrate the impacts of the adequately precise estimation of annual load growth in the design of energy systems.en
dc.subject.translatedoptimal planningen
dc.subject.translatedmicrogrids (MGs)en
dc.subject.translatedmulti-year load growth-based method artificial neural networks (ANNs)en
dc.subject.translatedload forecasting (LF)en
dc.subject.translatedHOMER (Hybrid Optimization of Multiple Energy Resources) softwareen
dc.identifier.doi10.1016/j.seta.2020.100827
dc.type.statusPeer-revieweden
dc.identifier.document-number595926700005
dc.identifier.obd43931458
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