Analysing the ability of smart meter data to provide accurate information to the UK DNOs

Goudarz Poursharif; Andrew Brint; Mary Black; Mark Marshall

doi:10.1049/oap-cired.2017.0654

Analysing the ability of smart meter data to provide accurate information to the UK DNOs

Goudarz Poursharif^*
, Andrew Brint
, Mary Black
, Mark Marshall

^*Corresponding author for this work

Sheffield University Management School
Northern Powergrid Holdings Company

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

By 2020, smart meters will potentially provide the UK's distribution network operators (DNOs) with more detailed information about the real-time status of the low-voltage (LV) network. However, the smart meter data that the DNOs will receive has a number of limitations including the unavailability of some real-time smart meter data, aggregation of smart meter readings to preserve customer privacy, half-hourly averaging of customer demand/ generation readings, and the inability of smart meters to identify the connection phases. This research investigates how these limitations of the smart meter data can affect the estimation accuracy of technical losses and voltage levels in the LV network and the ways in which 1 min losses and correct phasing patterns can be determined despite the limitations in smart data.

Original language	English
Pages (from-to)	2078-2081
Number of pages	4
Journal	CIRED - Open Access Proceedings Journal
Volume	2017
Issue number	1
DOIs	https://doi.org/10.1049/oap-cired.2017.0654
Publication status	Published - 1 Oct 2017
Event	24th International Conference and Exhibition on Electricity Distribution, CIRED 2017 - Glasgow, United Kingdom Duration: 12 Jun 2017 → 15 Jun 2017

Bibliographical note

Publisher Copyright:
© 2017 Institution of Engineering and Technology. All rights reserved.

Funding

We would like to thank Northern Powergrid Ltd. for sponsoring and supporting this research project. We would also like to thank the CLNR project team for providing us with the Smart Meter datasets necessary to carry out this research. From 2011 to 2014 CLNR was carried out by British Gas, Northern Powergrid, Durham Energy Institute, the Newcastle University, EA Technology, and Low Carbon Network fund.

Funders
CLNR
Northern Powergrid Ltd.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1049/oap-cired.2017.0654Licence: CC BY 3.0

Cite this

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abstract = "By 2020, smart meters will potentially provide the UK's distribution network operators (DNOs) with more detailed information about the real-time status of the low-voltage (LV) network. However, the smart meter data that the DNOs will receive has a number of limitations including the unavailability of some real-time smart meter data, aggregation of smart meter readings to preserve customer privacy, half-hourly averaging of customer demand/ generation readings, and the inability of smart meters to identify the connection phases. This research investigates how these limitations of the smart meter data can affect the estimation accuracy of technical losses and voltage levels in the LV network and the ways in which 1 min losses and correct phasing patterns can be determined despite the limitations in smart data.",

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AB - By 2020, smart meters will potentially provide the UK's distribution network operators (DNOs) with more detailed information about the real-time status of the low-voltage (LV) network. However, the smart meter data that the DNOs will receive has a number of limitations including the unavailability of some real-time smart meter data, aggregation of smart meter readings to preserve customer privacy, half-hourly averaging of customer demand/ generation readings, and the inability of smart meters to identify the connection phases. This research investigates how these limitations of the smart meter data can affect the estimation accuracy of technical losses and voltage levels in the LV network and the ways in which 1 min losses and correct phasing patterns can be determined despite the limitations in smart data.

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Analysing the ability of smart meter data to provide accurate information to the UK DNOs

Abstract

Bibliographical note

Funding

UN SDGs

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