IEEE Electrification - December 2022 - 64

represents a massive increase in
data ingestion beyond traditional
supervisory control and data acquisition
(SCADA) measurements,
which are usually present only for
the few voltage control assets. Traditionally,
VVO typically uses primary
voltage measurements, and it both
flattens the voltage profile and
brings voltages down to within the
lower range of the character of service/American
National Standards
Institute C84.1 range A. A typical
strategy of VVO is conservation voltage
reduction. It aims to lower voltages to achieve benefits
in terms of reduced load consumption. An assumed
headroom is usually reserved to account for the voltage
drop between the primary and secondary distribution
system. AMI provides VVO with the actual voltage drop
to the grid edge, potentially unlocking unused headroom
or, alternatively, requiring increased headroom in
the case of overestimation.
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45
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5
Utilities have a huge
opportunity to
leverage AMI to help
facilitate DER
integration and
improve overall
network health.
One challenge of using AMI data
may be visibility over the potentially
large spread of AMI voltages (Figure 8).
If voltages are widely distributed
across the full character-of-service
range, there may be little room for
changing control for VVO. How might
VVO, with full visibility of grid-edge
voltages, react when simultaneously
having meters across the circuit above
and below character-of-service ranges?
An immediate tradeoff for voltage
operation in these circumstances may
be to bound the percentile of customers
for whom to improve the power quality, allowing for
the voltage to be optimized for the majority of customers.
Long-term solutions, in these cases, would be to tighten
the spread of voltages across the feeder through upgrades.
Solar Hours (9 a.m.-4 p.m.)
Nonsolar Hours (12 a.m.-9 a.m. and 4 p.m. to 12 p.m.)
Duration of Consecutive Violation (minutes)
Figure 7. The duration of voltage events outside of the normal operation. As expected, most
events are of low duration, and the frequency decreases for high-duration events. Another aspect
to examine is the repetition of excursions on a daily basis and the characterization of persistent
period issues versus infrequent one-off issues.
1.02
1.04
1.06
0.9
0.92
0.94
0.96
0.98
1
Range of the Maximum
and Minimum
Range of the 25th
and 75th Percentiles
AMI: Other Applications
Beyond real-time operation, AMI has other potential
applications that are more specialized. For example, it has
been demonstrated that AMI data
can be used to identify the phase
allocation of customers (and, by definition,
secondaries) across a utility
distribution system. Many utilities
have struggled or neglected to keep
secondary information in their GISs,
meaning that such an application
may help distribution planners and
GIS teams update their networks
using a data-driven approach.
Another use case for AMI is helping
to validate and improve the quality
of distribution power-flow models.
As power-flow models are increasingly
being used to assess the
impacts of DERs, identify potential
upgrades, and examine new control
schemes, validating these models
with AMI data, in combination with
substation SCADA data, enables utilities
to have a realistic voltage drop
profile across the entire network
that can be used to improve powerflow
models.
Median Mean
Figure 8. The distribution of voltages for a selection of AMI meters examining the maximum, minimum,
75th and 25th percentile, mean, and median values. A challenge for integrating AMI into
DMSs for voltage control will be making decisions on potentially disparate voltages, but the benefit
will be unlocking knowledge about the voltage drop right to the grid edge.
64
IEEE Electrification Magazine / DECEMBER 2022
Establishing the
Infrastructure for
AMI Analysis
A major barrier to the use cases discussed
here so far is having sufficient
infrastructure to deploy the
analysis. AMI data are not useful in
isolation, and they must be tied to
Per-Unit Voltage
Percentage of All Events
Outside of Normal
Operation (%)
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