Assessment of Load and Generation Modelling on the Quasi-Static Analysis of Distribution Networks

dc.authorid Kryonidis, Georgios/0000-0002-7593-1761
dc.authorid Chrysochos, Andreas/0000-0002-5712-3859
dc.authorid Yetkin, E. Fatih/0000-0003-1115-4454
dc.authorid Pippi, Kalliopi/0000-0002-0931-284X
dc.authorid Papadopoulos, Theofilos/0000-0001-6384-1964
dc.authorwosid Kryonidis, Georgios/E-8497-2016
dc.contributor.author Lamprianidou, I. S.
dc.contributor.author Yetkin, Emrullah Fatih
dc.contributor.author Papadopoulos, T. A.
dc.contributor.author Kryonidis, G. C.
dc.contributor.author Yetkin, E. Fatih
dc.contributor.author Pippi, K. D.
dc.contributor.author Chrysochos, A., I
dc.contributor.other Business Administration
dc.date.accessioned 2023-10-19T15:11:40Z
dc.date.available 2023-10-19T15:11:40Z
dc.date.issued 2021
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, Greece en_US
dc.description.abstract Quasi-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.sponsorship Hellenic Foundation for Research and Innovation [HFRI-FM-17229] en_US
dc.description.sponsorship The 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.citationcount 3
dc.identifier.doi 10.1016/j.segan.2021.100509 en_US
dc.identifier.issn 2352-4677
dc.identifier.scopus 2-s2.0-85111263267 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.segan.2021.100509
dc.identifier.uri https://hdl.handle.net/20.500.12469/5163
dc.identifier.volume 27 en_US
dc.identifier.wos WOS:000687444900007 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Sustainable Energy Grids & Networks en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 9
dc.subject Pattern-Recognition En_Us
dc.subject Time-Series En_Us
dc.subject Power-Flow En_Us
dc.subject Reanalysis En_Us
dc.subject Energy En_Us
dc.subject Clustering en_US
dc.subject Pattern-Recognition
dc.subject Distributed generation modelling en_US
dc.subject Time-Series
dc.subject Load modelling en_US
dc.subject Power-Flow
dc.subject Load timeseries en_US
dc.subject Reanalysis
dc.subject Photovoltaic systems en_US
dc.subject Energy
dc.subject Wind turbines en_US
dc.title Assessment of Load and Generation Modelling on the Quasi-Static Analysis of Distribution Networks en_US
dc.type Article en_US
dc.wos.citedbyCount 6
dspace.entity.type Publication
relation.isAuthorOfPublication 81114204-31da-4513-a19f-b5446f8a3a08
relation.isAuthorOfPublication.latestForDiscovery 81114204-31da-4513-a19f-b5446f8a3a08
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