IEEE Power & Energy Magazine - May/June 2014 - 61

jurisdictions in the world assumes a disjointed utility network
(assuming such networks exist at lower layers of the distribution
system, which is often not the case), parts of which can be conveniently bypassed. what typically happens in such installations
is that smart meters are grouped into a local-area network (lan)
type of association (meshed or otherwise) and exchange their
data and commands through radio frequency (rF) or power line
carrier (PlC) links with a data aggregator unit (daU) installed
on a pole in their vicinity. the daU then employs a dedicated
communication link (often a proprietary wireless protocol) to
exchange the aggregated data and commands with an mdm
system within the utility's enterprise bus.
as shown in Figure 6, the specification of the constituent
components of the system is therefore optimized to ensure
data exchange between smart meters and the associated
mdm system residing within the utility's core network. the
ami system as such is not only oblivious to anything that
happens in between, but it also has no provisions for handling or carrying any other information or data, however
critical or important such data may be for other smart grid
functions to be rolled out in the future.
this simply means that critical sensory data and information produced at the downstream side of the network, which
may be critically required by middle layers of the system,
bypass such layers and end up in the upper layers of the
system for a specific function and/or purpose (e.g., billing).
they therefore do not contribute and/or enable future smart
grid functions that may benefit from access to such data. to
elaborate this issue further, the next section of this article
examines typical examples of such applications that are
deprived of access to these critical data as a result of inaccurate smart grid integration maps.

Transformer

WAN
Interface

Field Area
Network
Interface
Feeder A

Recloser
Volt/Var and CVR
Optimization Engine
DMZ

Timing and
Syncronization

LAN
Interface
AMI Headend

DAU

Substation Bus

Substation
Area
Network

one would certainly hope that utilities' quest for infrastructure modernization will not come to a screeching halt. Utilities will no doubt attempt to integrate additional smart grid
functionalities based on their particular priorities and road
maps. two such functions-ones many utilities intend to
implement next-are VVo and CVr. the U.S. department
of energy's recent studies in energy conservation across the
United States indicated that CVr functions, if integrated
across less than half of U.S. feeders, could potentially yield
more than a 2% reduction in demand on the U.S. electrical system. in fact, it is public knowledge that many utilities
regard VVo and CVr as high priorities for implementation
on critically congested feeders. it has been claimed that the
roi ration for VVo and CVr integration is six to one, with
a payback period of two to three years. that is certainly a
great incentive to regard investment in advanced VVo and
CVr as the next item on the integration map of many utilities after ami.
Prior to ami, the VVo and CVr functions in distribution substations (if they existed at all) used a statistically
aggregated profile of feeder load to determine the settings
and configurations of capacitor banks, tab changers, voltage regulators, and other devices used to correct the feeder's power factor and to ensure compliance of the voltage
gradient across the entire feeder, from substation to the last
customer, with anSi and ieee requirements. given the
fact that no real-time information about the actual voltage
samples across the feeder was available, the settings and
configurations for such VVo and CVr assets were either
ineffective or inefficient.

VR

Caps

VR

Caps

RF/PLC Interface

Power
SCADA

Data

Typical Issues with Smart
Grid Integration Maps

Substation EMS
Distribution Substation

EMS
HAN

EMS
HAN

EMS
HAN

EMS
HAN

figure 7. Client-server based VVO and CVR.
may/june 2014

ieee power & energy magazine

61



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

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