FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W8303669> ?p ?o ?g. }

• W8303669 abstract "Low voltage network design and planning in the UK operates in accordance with the Electricity Regulations and the Distribution code and has utilised the same general principles for many years. The approach is primarily concerned with providing a secure, good quality supply and relies on the techniques of After Diversity Maximum Demand and the ‘statistical method’ for estimating load demand. In recent years the prospect of increased levels of low carbon technology such as micro-generation, electric vehicles, electric space/water heating and demand side management has been the focus of studies addressing the impact on existing LV networks and also regulations for new LV network developments. These studies have shown varying levels of voltage and reverse power flow problems arising on case study networks and have used a variety of approaches to LV network modelling and load estimation. The use of feed-in tariffs for micro-generation, plus policy to shift energy demand to electricity from sectors such as transport and heating, increases the likelihood of significant changes in the LV network operating conditions in the near future and will create a situation that current LV planning approaches are ill-equipped to deal with. In addition, aggregation (Virtual Power Plants) and active control of demand are areas of significant research with the aim of harnessing the potential contribution of distributed energy resources connected to the LV network. As such, the design and planning of future networks will be required to consider the influence of these schemes. This paper presents functionality likely to be required by network planners and discusses the key components of a probabilistic planning framework that allows a DNO to analyse the need for, operation of, and value provided by DNO led Demand Side Management. LV modelling techniques are reviewed and the requirements of an LV modelling tool in the planning framework are examined. Probabilistic approaches to evaluating LV network scenarios are discussed and the required load and generation information are considered, including the use of smart meter data, forecasting, scenarios, stochastic data and time-series profiles. The requirements for data collection, analysis and processing for varying degrees of functionality are weighed against the benefit provided. Possible LV control schemes are considered and methods of presenting the potential network services to planning engineers are investigated. Modelling that allows the planner to assess the impact of these control schemes and include the proposed services in a planning design is also explored. A probabilistic analysis of a case study LV network with varying penetrations of electric vehicles, electric space and water heating, and supplier-led DSM schemes is used to develop components of the planning framework and inform the discussion areas described above." @default.
• W8303669 created "2016-06-24" @default.
• W8303669 creator A5060536874 @default.
• W8303669 creator A5079541661 @default.
• W8303669 date "2012-01-01" @default.
• W8303669 modified "2023-10-05" @default.
• W8303669 title "A framework for low voltage network planning in the era of low carbon technology and active consumers" @default.
• W8303669 cites W1541786028 @default.
• W8303669 cites W1901949613 @default.
• W8303669 cites W2013376613 @default.
• W8303669 cites W2014223833 @default.
• W8303669 cites W2042569829 @default.
• W8303669 cites W2047674532 @default.
• W8303669 cites W2071267648 @default.
• W8303669 cites W2098738097 @default.
• W8303669 cites W2099517607 @default.
• W8303669 cites W2119440292 @default.
• W8303669 cites W2148879682 @default.
• W8303669 cites W2171758655 @default.
• W8303669 cites W632036464 @default.
• W8303669 doi "https://doi.org/10.1049/cp.2012.0761" @default.
• W8303669 hasPublicationYear "2012" @default.
• W8303669 type Work @default.
• W8303669 sameAs 8303669 @default.
• W8303669 citedByCount "1" @default.
• W8303669 countsByYear W83036692013 @default.
• W8303669 crossrefType "proceedings-article" @default.
• W8303669 hasAuthorship W8303669A5060536874 @default.
• W8303669 hasAuthorship W8303669A5079541661 @default.
• W8303669 hasConcept C104779481 @default.
• W8303669 hasConcept C11413529 @default.
• W8303669 hasConcept C119599485 @default.
• W8303669 hasConcept C127413603 @default.
• W8303669 hasConcept C128624480 @default.
• W8303669 hasConcept C140205800 @default.
• W8303669 hasConcept C144133560 @default.
• W8303669 hasConcept C165801399 @default.
• W8303669 hasConcept C41008148 @default.
• W8303669 hasConceptScore W8303669C104779481 @default.
• W8303669 hasConceptScore W8303669C11413529 @default.
• W8303669 hasConceptScore W8303669C119599485 @default.
• W8303669 hasConceptScore W8303669C127413603 @default.
• W8303669 hasConceptScore W8303669C128624480 @default.
• W8303669 hasConceptScore W8303669C140205800 @default.
• W8303669 hasConceptScore W8303669C144133560 @default.
• W8303669 hasConceptScore W8303669C165801399 @default.
• W8303669 hasConceptScore W8303669C41008148 @default.
• W8303669 hasLocation W83036691 @default.
• W8303669 hasOpenAccess W8303669 @default.
• W8303669 hasPrimaryLocation W83036691 @default.
• W8303669 hasRelatedWork W1544841211 @default.
• W8303669 hasRelatedWork W1570156794 @default.
• W8303669 hasRelatedWork W1571481168 @default.
• W8303669 hasRelatedWork W1859921603 @default.
• W8303669 hasRelatedWork W2014743873 @default.
• W8303669 hasRelatedWork W2611828605 @default.
• W8303669 hasRelatedWork W2902259768 @default.
• W8303669 hasRelatedWork W2965947166 @default.
• W8303669 hasRelatedWork W3107541594 @default.
• W8303669 hasRelatedWork W3178732595 @default.
• W8303669 isParatext "false" @default.
• W8303669 isRetracted "false" @default.
• W8303669 magId "8303669" @default.
• W8303669 workType "article" @default.