FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367693721> ?p ?o ?g. }

• W4367693721 endingPage "e01683" @default.
• W4367693721 startingPage "e01683" @default.
• W4367693721 abstract "The uptake of low carbon technologies, particularly Electric Vehicles (EVs) and Heat-Pumps (HPs), at the low voltage (LV) distribution network, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand. This development can, as hinted in previous studies, pose a real challenge of capacity overloading to transformers at the LV distribution network of electricity system. Prolonged periods of transformer overloading could lead to premature transformer failure and shortens transformer's life expectancy. A direct solution to addressing transformer overloading is the upgrading of the transformer capacity. However, the number of LV distribution transformers in electricity system to be upgraded and the resources needed for such operation make the solution less desirable to the Distribution Network Operators (DNOs). Therefore, it is important to develop cost-effective solutions for the optimal utilization of the existing transformer capacity. Adaptive thermal loading of transformers is one of such solutions. This paper focusses on the Adaptive Thermal Loading (ATL) of transformers in LV distribution networks with considerable penetration level of EVs and HPs. The thermal model of a 500-kVA, 11/0.415-kV (no load), 50-Hz, Dyn11, ONAN mineral oil filled, free breathing, ground mounted transformer serving a real and typical urban LV network in the United Kingdom (UK) is developed based on IEC 60,076–7:2005 standard and used as the case study. A method of adaptive thermal loading of the transformer is presented to examine its capacity performance when serving the future load of the LV network following the integration of projected uptake figures of EVs and HPs for the years 2020, 2030, 2040 and 2050 into the network. Given the load and temperature forecasts of a day, the method aims at optimizing, considering the real and present conditions of the operating environment, the overall daily transformer capacity utilization that gives maximum daily return on investment without undermining reliability of supply and normal life expectancy of the transformer. Results show improved performances of the transformer when the adaptive thermal loading method is used." @default.
• W4367693721 created "2023-05-03" @default.
• W4367693721 creator A5033609591 @default.
• W4367693721 creator A5058692326 @default.
• W4367693721 date "2023-07-01" @default.
• W4367693721 modified "2023-10-14" @default.
• W4367693721 title "Adaptive thermal model for loading of transformers in low carbon electricity distribution networks" @default.
• W4367693721 cites W2006373187 @default.
• W4367693721 cites W2111944939 @default.
• W4367693721 cites W2139835088 @default.
• W4367693721 cites W2327145679 @default.
• W4367693721 cites W2399228658 @default.
• W4367693721 cites W2588757093 @default.
• W4367693721 cites W4205397488 @default.
• W4367693721 cites W4310656285 @default.
• W4367693721 cites W4310737912 @default.
• W4367693721 cites W4317618814 @default.
• W4367693721 doi "https://doi.org/10.1016/j.sciaf.2023.e01683" @default.
• W4367693721 hasPublicationYear "2023" @default.
• W4367693721 type Work @default.
• W4367693721 citedByCount "0" @default.
• W4367693721 crossrefType "journal-article" @default.
• W4367693721 hasAuthorship W4367693721A5033609591 @default.
• W4367693721 hasAuthorship W4367693721A5058692326 @default.
• W4367693721 hasBestOaLocation W43676937211 @default.
• W4367693721 hasConcept C117323899 @default.
• W4367693721 hasConcept C119599485 @default.
• W4367693721 hasConcept C127413603 @default.
• W4367693721 hasConcept C165801399 @default.
• W4367693721 hasConcept C171146098 @default.
• W4367693721 hasConcept C18903297 @default.
• W4367693721 hasConcept C200601418 @default.
• W4367693721 hasConcept C206658404 @default.
• W4367693721 hasConcept C39432304 @default.
• W4367693721 hasConcept C47737302 @default.
• W4367693721 hasConcept C66322947 @default.
• W4367693721 hasConcept C86803240 @default.
• W4367693721 hasConcept C9121194 @default.
• W4367693721 hasConceptScore W4367693721C117323899 @default.
• W4367693721 hasConceptScore W4367693721C119599485 @default.
• W4367693721 hasConceptScore W4367693721C127413603 @default.
• W4367693721 hasConceptScore W4367693721C165801399 @default.
• W4367693721 hasConceptScore W4367693721C171146098 @default.
• W4367693721 hasConceptScore W4367693721C18903297 @default.
• W4367693721 hasConceptScore W4367693721C200601418 @default.
• W4367693721 hasConceptScore W4367693721C206658404 @default.
• W4367693721 hasConceptScore W4367693721C39432304 @default.
• W4367693721 hasConceptScore W4367693721C47737302 @default.
• W4367693721 hasConceptScore W4367693721C66322947 @default.
• W4367693721 hasConceptScore W4367693721C86803240 @default.
• W4367693721 hasConceptScore W4367693721C9121194 @default.
• W4367693721 hasLocation W43676937211 @default.
• W4367693721 hasOpenAccess W4367693721 @default.
• W4367693721 hasPrimaryLocation W43676937211 @default.
• W4367693721 hasRelatedWork W149558712 @default.
• W4367693721 hasRelatedWork W2064030720 @default.
• W4367693721 hasRelatedWork W2350175534 @default.
• W4367693721 hasRelatedWork W2354065378 @default.
• W4367693721 hasRelatedWork W2373443317 @default.
• W4367693721 hasRelatedWork W2382773045 @default.
• W4367693721 hasRelatedWork W2389342727 @default.
• W4367693721 hasRelatedWork W2804831932 @default.
• W4367693721 hasRelatedWork W3092136568 @default.
• W4367693721 hasRelatedWork W88811473 @default.
• W4367693721 hasVolume "20" @default.
• W4367693721 isParatext "false" @default.
• W4367693721 isRetracted "false" @default.
• W4367693721 workType "article" @default.