IEEE Power & Energy Magazine - May/June 2014 - 62

62

ieee power & energy magazine

MCU

Residential Load-H

Smart Meter-H

PLC
Modem

Capacitor Banks
Unit

Sub. Capacitor
Banks Unit

Cap. Bank
Switch

AFE

Line Voltage
Regulator

Cap. Bank Switch

Distribution Transformer
12.5 kV/0.4 kv

..
..
Voltage Regulator
Switch

MCU

Smart Meter-B
MV Breaker

Residential Load-B

PLC
Modem
Substation MV Bus

AFE

PLC
Modem

IA-IED

MCU

Substation Transformer
138-kv/12.5-kV
Transformer On-Load
Tap Changer

MV Breaker

Load Bank

MV
Distributed
Breaker Generation Source
12.5 kV

Capacitor Banks
Unit

MCU

AFE

Smart Meter-A

Residential Load-A

PLC
Modem

AFE
VVO/CVR
Opt. Engine

HV Breaker

Substation HV Bus

To HV Substation

figure 8. Substation-based VVO and CVR.

the advent of ami changed that
situation. engineers in charge of planning for new VVo and CVr functions
saw the opportunity to use real-time
voltage, current, and power factor
(V/i/PF) sample values from smart
meters at each customer node to build
a realistic and accurate real-time view
of the load profile across any given
feeder and thus to optimize VVo and
CVr settings based on an accurate
voltage gradient across the feeder, climatic conditions, and toU. Such an
approach has been named adaptive
real-time VVO/CVR.
to implement adaptive real-time
VVo/CVr, two approaches have
been considered. one relies on capturing smart meters' sensory data
through an interface with the mdm
system in the back office, then running VVo and CVr algorithms on
powerful enterprise servers using
the network model of the distribution system, and finally transferring
the new settings to the field VVo
and CVr assets through the SCada
system. in other words, the VVo and
CVr functions are split into a clientserver configuration, with the server
operating in the back office and relying on mdm system databases to
continuously calculate new settings
for VVo and CVr clients in the field
and transfer the new configurations
to such assets through the SCada
system. this approach is called centralized VVO/CVR control.
Centralized VVo/CVr control,
depicted in Figure 7, seemed quite
attractive at first. the availability of
accurate network models, combined
with adequate processing power on the
enterprise bus and access to the dmS
system, could indeed result in highly
effective settings for VVo and CVr
assets. but further studies at bCit (see
Figure 8 and the suggested readings
that follow this article) indicated that
the VVo and CVr functions could be
performed a lot more efficiently (and
at lower cost) if on-demand sample
values of V/i/PF from bellwether smart
meters could be made available much
more frequently than they can be using
may/june 2014



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2014

IEEE Power & Energy Magazine - May/June 2014 - Cover1
IEEE Power & Energy Magazine - May/June 2014 - Cover2
IEEE Power & Energy Magazine - May/June 2014 - 1
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IEEE Power & Energy Magazine - May/June 2014 - 128
IEEE Power & Energy Magazine - May/June 2014 - Cover3
IEEE Power & Energy Magazine - May/June 2014 - Cover4
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