Poursharif, G., Brint, A.T. orcid.org/0000-0002-8863-407X, Black, M. et al. (1 more author) (2018) Using smart meters to estimate low voltage losses. IET Generation, Transmission and Distribution, 12 (5). pp. 1206-1212. ISSN 1751-8687
Abstract
Losses on low voltage networks are often substantial. For example, in the UK they have been estimated as being 4% of the energy supplied by low voltage networks. However, the breakdown of the losses to individual conductors and their split over time are poorly understood as generally only the peak demands and average loads over several months have been recorded. The introduction of domestic smart meters has the potential to change this. How domestic smart meter readings can be used to estimate the actual losses is analysed. In particular, the accuracy of using 30 minute readings compared with 1 minute readings, and how this accuracy could be improved, were investigated. This was achieved by assigning the data recorded by 100 smart meters with a time resolution of 1 minute to three test networks. Smart meter data from three sources were used in the investigation. It was found that 30 minute resolution data underestimated the losses by between 9% and 24%. By fitting an appropriate model to the data, it was possible to reduce the inaccuracy by approximately 50%. Having a smart meter time resolution of 10 minutes rather than 30 gave little improvement to the accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Institution of Engineering and Technology. This is an author produced version of a paper subsequently published in IET Generation, Transmission and Distribution. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | smart meters; conductors (electric); data recording; loss measurement; voltage measurement; low-voltage loss estimation; low-voltage network; UK; conductor loss breakdown; domestic smart meter reading; data recording; time 30 min; time 1 min |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Nov 2017 13:44 |
Last Modified: | 15 Dec 2023 09:33 |
Status: | Published |
Publisher: | Institution of Engineering and Technology |
Refereed: | Yes |
Identification Number: | 10.1049/iet-gtd.2017.1300 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124207 |