Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/16220

Full metadata record

DC FieldValueLanguage
dc.contributor.authorQuiggin, Daniel-
dc.date.accessioned2014-11-13T09:11:36Z-
dc.date.available2014-11-13T09:11:36Z-
dc.date.issued2014-
dc.identifier.urihttps://dspace.lboro.ac.uk/2134/16220-
dc.descriptionA Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.en_GB
dc.description.abstractThe 2050 national energy scenarios as planned by the DECC, academia and industry specify a range of different decarbonised supply side technologies combined with the electrification of transportation and heating. Little attention is paid to the household demand side; indeed within many scenarios a high degree of domestic Demand Side Management (DSM) is implicit if the National Grid is to maintain supply-demand balance. A top-down, bottom-up hybrid model named Shed-able Household Energy Demand (SHED) has been developed and the results of which presented within this thesis. SHED models six published national energy scenarios, including three from the Department for Energy and Climate Change, in order to provide a broad coverage of the possible energy scenario landscape. The objective of which is to quantify the required changes in current household energy demand patterns via DSM, as are implicit under these highly electricity dominated scenarios, in order to maintain electrical supply-demand balance at the national level. The frequency and magnitude of these required household DSM responses is quantified. SHED performs this by modelling eleven years of supply-demand dynamics on the hourly time step, based on the assumptions of the published energy scenarios as well as weather data from around 150 weather stations around the UK and National Grid historic electricity demand data. The bottom-up component of SHED is populated by 1,000 households hourly gas and electricity demand data from a recently released dataset from a smart metering trial in Ireland. This aggregate pool of households enables national domestic DSM dynamics to be disaggregated to the aggregate household level. Using household classifications developed by the Office for National Statistics three typical ' households are identified within the aggregate pool and algorithms developed to investigate the possible required responses from these three households. SHED is the first model of its kind to connect national energy scenarios to the implications these scenarios may have on households consumption of energy at a high temporal resolution. The analysis of the top-down scenario modelling shows significant periods where electrical demand exceeds supply within all scenarios, within many scenarios instances exist where the deficit is unserviceable due to lack of sufficient spare capacity either side of the deficit period. Considering the level of participation required within the modelled scenarios in order to balance the electricity system and the current lack in understanding of smart metering and Time-Of-Use (TOU) tariffs within households, it would seem there is a disconnect between the electricity system being planned, the role this system expects of households and the role households are willing to play.en_GB
dc.description.sponsorshipEPSRCen_GB
dc.language.isoenen_GB
dc.publisher© Daniel Quigginen_GB
dc.rightsThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dc.subjectEnergy scenarioen_GB
dc.subjectSupply-demand grid balancingen_GB
dc.subjectHeat electrificationen_GB
dc.subjectClimate changeen_GB
dc.subjectDemand Side Managementen_GB
dc.subjectEnergy system modellingen_GB
dc.subjectSmart gridsen_GB
dc.subjectRenewable energyen_GB
dc.titleModelling the expected participation of future smart households in demand side management, within published energy scenariosen_GB
dc.typeThesisen_GB
dc.administration.instructionName: Quiggin, Daniel Email: D.Quiggin@lboro.ac.uk Department: CBE Access: open Comment:en_GB
dc.identifier.ethospersistentiduk.bl.ethos.631644-
Appears in Collections:PhD Theses (Architecture, Building and Civil Engineering)

Files associated with this item:

File Description SizeFormat
Form-2014-Quiggin.pdf806.9 kBAdobe PDFView/Open
Thesis-2014-Quiggin.pdf21.21 MBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.