Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745667
Title: Investigating the ability of smart electricity meters to provide accurate low voltage network information to the UK distribution network operators
Author: Poursharif, Goudarz
ISNI:       0000 0004 7226 6772
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Abstract:
This research presents a picture of the current status and the future developments of the LV electricity grid and the capabilities of the smart metering programme in the UK as well as investigating the major research trends and priorities in the field of Smart Grid. This work also extensively examines the literature on the crucial LV network performance indicators such as losses, voltage levels, and cable capacity percentages and the ways in which DNOs have been acquiring this knowledge as well the ways in which various LV network applications are carried out and rely on various sources of data. This work combines 2 new smart meter data sets with 5 established methods to predict a proportion of consumer’s data is not available using historical smart meter data from neighbouring smart meters. Our work shows that half-hourly smart meter data can successfully predict the missing general load shapes, but the prediction of peak demands proves to be a more challenging task. This work then investigates the impact of smart meter time resolution intervals and data aggregation levels in balanced and unbalanced three phase LV network models on the accuracy of critical LV network performance indicators and the way in which these inaccuracies affect major smart LV network application of the DNOs in the UK. This is a novel work that has not been carried out before and shows that using low time resolution and aggregated smart meter data in load flow analysis models can negatively affect the accuracy of critical low voltage network estimates.
Supervisor: Brint, Andrew ; Cantarelli, Chantal Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.745667  DOI: Not available
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