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.
Highlights
The Low Voltage (LV) side of the electricity distribution grid is relatively invisible to the Distribution Network Operators (DNOs), compared to the high and medium voltage parts of the electricity network which have traditionally been designed to accommodate generation and various monitoring points
High resolution smart meter data can enhance various DNO applications such as network planning and design, asset management, fault location and restoration, power quality management, active network management, Demand Side Management (DSM), and Distributed Generation (DG) integration by providing more accurate power flow information which in turn can lead to more accurate estimations of network losses, voltage variations, cable loading capacity, and phasing arrangements
Two versions were analyzed with data from different trials, one using data collected by Loughborough University in 2008 and 2009 [3] and using data collected by the Customer-Led Network Revolution (CLNR) project from 2011 to 2014
Summary
The Low Voltage (LV) side of the electricity distribution grid is relatively invisible to the DNOs, compared to the high and medium voltage parts of the electricity network which have traditionally been designed to accommodate generation and various monitoring points. The introduction of smart meters in the UK has the potential to dramatically change this by providing detailed consumption/generation information from every household, at node points along the network, and downstream of LV substations to the network operators. High resolution smart meter data can enhance various DNO applications such as network planning and design, asset management, fault location and restoration, power quality management, active network management, Demand Side Management (DSM), and Distributed Generation (DG) integration by providing more accurate power flow information which in turn can lead to more accurate estimations of network losses, voltage variations, cable loading capacity, and phasing arrangements. These issues are investigated of this paper
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