IEEE Power & Energy Magazine - May/June 2014 - 63

may/june 2014

N5-2 Smart
Meters

∗D2

T2

Rooftop

B2

Smart Inverters

Net
Meter
N=3

PVs

Var Injection
Point-2

∗Bi

Var Injection
Point-3
Feeder
Capacitor
Bank

N5-1 Smart
Meters

T3

N=4

N=5

Net
Meter

T4

DK

EV
Net Meter
D1

DG Source

Var Injection
Point-4
Smart Inverter
Cj
C2
∗C1

N3 Smart Meters

N=1

Var Injection
Point-1

T1

N=2

B1

A8
∗A2

Communication Flow
Dist. Network Flow

Substation
Capacitor
Bank

Substation Transformer
with Tap-Changer

CVR
Server
IEC 61850

VVO
Engine
EMS
Server

HV/MV Substation

N2 Smart Meters

Feeder
Capacitor
Bank

A1

current ami systems and their
mdm system interfaces. to achieve
this, VVo and CVr algorithms have
to be processed locally within each
substation, using the local sensory
data associated with each individual feeder. this approach is called
decentralized VVO/CVR control.
although research in decentralized VVo/CVr control algorithms
is ongoing, early results indicate
that a decentralized approach is
more efficient and cost-effective for
such applications. the difficulty in
realizing a decentralized VVo/CVr
control strategy is that although it
requires the ami system to supply
it more data more frequently, as a
substation-based function it has no
direct access to the ami system.
this means that such data must be
extracted from the mdm system on
the enterprise bus and transported
down to the substation through the
SCada system. the issue there is
that current ami systems (which
interface directly with an mdm
system in the back office) are not
typically designed to supply such
large quantities of real-time data
from the field to the mdm system
without the risk of network congestion. Second, most SCada systems
are incapable of transferring such
massive amounts of time-sensitive
information from the back office to
field devices without depriving other
critical functions of access to their
allocated bandwidth. third, given
the fact that the VVo and CVr
functions are feeder-bound (i.e., the
required inputs and outputs are all
local), there is very little rationale
for involving upper-layer enterprise
functions in their operation.
this example is a clear indication of how critical a smart grid
integration map can be to the
realization of the smart grid. if a
utility's integration map fails to
accommodate access to time-sensitive data for upper-layer-based
smart grid functions, it will either
have to give up implementing
future smart grid functionalities

figure 9. A substation-based EMS.
ieee power & energy magazine

63



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

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IEEE Power & Energy Magazine - May/June 2014 - Cover3
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