Assessment of load and generation modelling on the quasi-static analysis of distribution networks

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dc.authoridKryonidis, Georgios/0000-0002-7593-1761
dc.authoridChrysochos, Andreas/0000-0002-5712-3859
dc.authoridYetkin, E. Fatih/0000-0003-1115-4454
dc.authoridPippi, Kalliopi/0000-0002-0931-284X
dc.authoridPapadopoulos, Theofilos/0000-0001-6384-1964
dc.authorwosidKryonidis, Georgios/E-8497-2016
dc.contributor.authorLamprianidou, I. S.
dc.contributor.authorPapadopoulos, T. A.
dc.contributor.authorKryonidis, G. C.
dc.contributor.authorYetkin, E. Fatih
dc.contributor.authorPippi, K. D.
dc.contributor.authorChrysochos, A., I
dc.date.accessioned2023-10-19T15:11:40Z
dc.date.available2023-10-19T15:11:40Z
dc.date.issued2021
dc.department-temp[Lamprianidou, I. S.; Papadopoulos, T. A.; Pippi, K. D.] Democritus Univ Thrace, Dept Elect & Comp Engn, Xanthi 67100, Greece; [Kryonidis, G. C.] Aristotle Univ Thessaloniki, Sch Elect & Comp Engn, Thessaloniki 54124, Greece; [Yetkin, E. Fatih] Kadir Has Univ, Management Informat Syst Dept, Istanbul, Turkey; [Chrysochos, A., I] Hellen Cables, R&D Dept, Athens 15125, Greeceen_US
dc.description.abstractQuasi-static analysis of power systems can be performed by means of timeseries-based and probability density function-based models. In this paper, the effect of different load and generation modelling approaches on the quasi-static analysis of distribution networks is investigated. Different simplified load and distributed renewable energy sources generation timeseries-based models are considered as well as probabilistic analysis. Moreover, a more sophisticated approach based on cluster analysis is introduced to identify harmonized sets of representative load and generation patterns. To determine the optimum number of clusters, a three-step methodology is proposed. The examined cases include the quasi-static analysis of distribution networks for different operational conditions to identify the simplified modelling approaches that can efficiently predict the network voltages and losses. Finally, the computational efficiency by using the simplified models is evaluated in temperature-dependent power flow analysis of distribution networks. (C) 2021 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipHellenic Foundation for Research and Innovation [HFRI-FM-17229]en_US
dc.description.sponsorshipThe research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) , Greece under the First Call for H.F.R.I. Research Projects to support Faculty members and Re-searchers and the procurement of high-cost research equipment grant (Project Number: HFRI-FM-17229) .en_US
dc.identifier.citation3
dc.identifier.doi10.1016/j.segan.2021.100509en_US
dc.identifier.issn2352-4677
dc.identifier.scopus2-s2.0-85111263267en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.segan.2021.100509
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5163
dc.identifier.volume27en_US
dc.identifier.wosWOS:000687444900007en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Energy Grids & Networksen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPattern-RecognitionEn_Us
dc.subjectTime-SeriesEn_Us
dc.subjectPower-FlowEn_Us
dc.subjectReanalysisEn_Us
dc.subjectEnergyEn_Us
dc.subjectClusteringen_US
dc.subjectPattern-Recognition
dc.subjectDistributed generation modellingen_US
dc.subjectTime-Series
dc.subjectLoad modellingen_US
dc.subjectPower-Flow
dc.subjectLoad timeseriesen_US
dc.subjectReanalysis
dc.subjectPhotovoltaic systemsen_US
dc.subjectEnergy
dc.subjectWind turbinesen_US
dc.titleAssessment of load and generation modelling on the quasi-static analysis of distribution networksen_US
dc.typeArticleen_US
dspace.entity.typePublication

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