Situation Overview

The biggest threat today to the
transmission and distribution
(T&D) business’ cybersecurity
is not necessarily a virus, worm or terrorist.
While these can all be significant
threats, the culprit is often the company
itself. Particularly as utilities open more
doors to their network automation
and control systems – such as by deploying
more intelligent devices – they can
make themselves more vulnerable to any
cybersecurity threat. Although utilities
can mitigate vulnerabilities, these actions
come at an additional cost.

This paper examines recent developments
in cybersecurity for network
automation and control systems. First,
we review how recent cost-cutting
initiatives and new technologies are
creating cybersecurity problems. We
then look at how the North American
Electric Reliability Corporation’s (NERC)
new cybersecurity standards, CIP 002-
009, may help utilities address these
problems but also create additional
costs. Lastly, the report discusses how
utilities can cost-effectively strengthen
their cybersecurity.

More Cybersecurity Problems

Today most energy delivery businesses
have moved beyond worrying about
specific cyberattacks, which could be
launched by almost anyone from “kids in
a basement” to a company insider. Instead
many are now concerned about how
cost-cutting efforts and the introduction
of new technologies could increase their
vulnerability to any attack, no matter
where it originates. There are two key
corporate efforts that can raise significant
cyber-security issues:

• Obtaining greater real-time visibility of
  and access to remote assets; and
• Implementing more open, standardized
  real-time systems.

Greater Visibility and More A ccess Points

From intelligent grid initiatives for better
visualizing and reacting to real-time
events to cost-cutting efforts, utilities continue
to open more doors to their control
networks (see Figure 1).

Specific efforts that could create cybersecurity
problems include:

• More points of access to the grid: Many
  utilities are using more intelligent electronic
  devices (IEDs), smart meters and
  sensors to connect with the field better
  – an important part of a fast-reacting
  intelligent network. Furthermore, utilities
  are allowing more remote access
  to their delivery system networks by
  telecommuters and third-party contractors.
  These efforts don’t just open more
  connections to system; they also let in
  remote parties that may not have sufficient
  cybersecurity systems of their
  own in place.
• More standard communication networks:
  Although IP-enabled SCADA and
  wireless networks offer many benefits,
  they also create problems. On the positive
  side, an IP-enabled SCADA system
  provides a low-cost alternative to a proprietary
  SCADA system. It also allows
  for better deployment of intelligent grid
  technologies through easier integration.
  Intelligent grid efforts are also pushing
  utilities to use more wireless communications
  (e.g., WiFi, WiMAX and GPRS).
  The problem with these communications
  methods is that they spread out
  standard communication channels into
  the service area, creating more ways
  for cyberthreats to enter the system.
• More integration with corporate networks:
  To support data-sharing and
  decision support and, in some cases,
  to reduce costs, more utilities are integrating
  their control networks with the
  corporate network. This is troublesome
  because it allows even more traffic on
  and accessibility to the corporate network,
  which means a greater possibility
  of cyberthreats entering the network
  automation and control system.
• Greater reliance on commonly used
  platforms: It is becoming increasingly
  difficult for IT departments to find
  professionals competent on platforms
  such as Unix, the traditional operating
  system for network automation and
  control systems. These platforms have
  substantially longer learning curves and
  require a costlier workforce. As a result,
  many utilities are turning to more commonplace
  platforms instead. Standardizing
  on platforms such as Microsoft
  Windows offers many benefits, but such
  platforms are also common targets for
  cyberattacks.
• Vendors have not made cybersecurity
  a top priority: Today many utilities rely
  on standardized commercial applications
  rather than pay for expensive
  customized systems. This creates
  vulnerabilities because many network
  automation and control vendors have
  not built adequate cybersecurity measures
  to protect those applications and
  attackers can readily obtain information
  about how to crack them.

New Regulations Will Now Force Utilities to Take Action

Although the initiatives discussed above
create cybersecurity problems, utilities
can address them by implementing some
additional procedures. Aside from the
direct operational benefits that come
from greater cybersecurity, today’s utilities
have an even stronger incentive to
take action. NERC and the Federal Energy
Regulatory Commission (FERC) released
the Critical Infrastructure Protection (CIP)
standards for cybersecurity, which went
into effect on June 1, 2006. Because most
utilities are familiar with CIP 002-009, this
paper focuses on a few key problems these
standards will likely create for utilities.

Defining and Interpreting a Critical Cyber Asset

Today’s CIP standards provide a more
detailed critical cyber asset (CCA) definition
than previous versions. The standards
require utilities first to define their critical
assets and then to identify CCAs that are
essential to their function (see Figure 2).
However, according to the NERC, CCAs
can now also include any cyber asset that
“uses a routable protocol to communicate
outside the electronic security perimeter;
or … uses a routable protocol within a
control center; or … is dial-up accessible.”
For example, modems that contractors
use to access the network automation and
control system connected to an otherwise
noncritical cyber asset could now qualify
as a CCA. This brings us to another problem
with the new CCA definition. Although
the definition is now more specific, there
are still varying interpretations of its
meaning. For example, although one utility
recently identified 300 CCAs, another
comparably sized utility found approximately
three times as many.

This more detailed CCA definition opens
up the possibility that a cyber asset not
essential for a critical asset to function
will be subject to these regulations, simply
because it has a routable protocol or
dial-up modem. As discussed earlier, more
and more utilities are implementing IPenabled
technologies, such as IP-enabled
SCADA. Therefore, more of those assets
may be subject to the regulations, which
wouldn’t be the case if utilities went with
older technologies. Furthermore, even
some older technologies, such as dial-up
modems, may now count as CCAs.
Ultimately, more assets subjected to
these standards means more effort and
cost for utilities to secure those assets.

Future Outlook

Some of the greatest challenges for utilities
stem from the fact that demand for
these cost-cutting and new technology
initiatives are unlikely to go away. As the
need increases for better grid visibility
and improved response times to delivery
system events, utilities will demand even
more intelligent grid technologies – such
as IP-enabled SCADA systems, smart
meters and grid-friendly appliances – that
will allow for more and more access to
delivery assets, i.e., more open networks.

Another challenge on the horizon, as
more and more workers retire, is that utilities
will turn to automation to offset the
loss of staff. We’ll see a greater number
of initiatives such as the use of standardized
applications and more commonly
used platforms and corporate networks as
companies use information technology to
reduce costs and increase efficiency and
also to replace their retiring workforces.

At the same time, the new CIP standards
will continue to evolve, and the
broader NERC reliability standards will
further complicate a utility’s ability to
meet cybersecurity standards. Given the
rapidly changing cybersecurity environment
and the age of CIP 002-009 standards,
these regulations will likely change.
Moreover, CIP 002-009 is just a piece
of the NERC reliability standards. These
extensive standards cover a broad range
of issues, from resource and demand balancing
to personnel performance, training
and qualifications. These broader regulations
will put a squeeze on the resources
utilities can devote to cybersecurity.

Future Adoption Patterns

Although some utilities have complied
with voluntary cybersecurity standards,
the CIP standards will force many more to
reconsider seriously their cybersecurity
system. As a result, utilities will:

• Have larger cybersecurity budgets.
  Despite the demands of the broader
  NERC reliability standards, CIP 002-009
  means that many utilities will have to
  step up their cybersecurity efforts – and
  spending – to become compliant.
• Be more aware of cybersecurity costs
  and risks. Now that utilities must comply
  with regulations and deal with the varying
  interpretations of the CCA definition,
  they will take into greater consideration
  the cybersecurity costs associated
  with new projects. But even as utilities
  expand their budgets, they will not be
  able to afford every single cybersecurity
  measure available for their network
  automation and control systems. Instead
  they’ll take a risk management approach
  that weighs the probability and extent
  of risks – events that would cause
  problems for their network automation
  and control systems. They won’t have
  resources to answer every risk, so they’ll
  prioritize their cybersecurity efforts to
  address the most critical risks (e.g., risks
  that may be unlikely to occur but would
  be catastrophic to the system).
• Demand more third-party assistance.
  Utilities will need more products and
  services – such as vulnerability assessments
  and security software packages
  – as they improve their cybersecurity.
  They’ll turn not only to cybersecurity
  vendors but also to industry-specific
  vendors to strengthen one another’s
  solutions.

Essential Guidance

Popping in a security software program
or setting up a firewall is not adequate to
protect a utility’s network automation and
control system. And new technologies and
cost-cutting efforts will continue to expose
the network automation and control system
to cyberthreats. Therefore utilities
must weigh their cost-reduction and intelligent
grid initiatives against the need for
greater security.

Utilities should not necessarily avoid
newer technologies or cost reductions out
of cybersecurity fear. They will, however,
need to determine up front what changes
to network automation and control systems
will cost in terms of cybersecurity
compliance. A new, more efficient technology
isn’t really less expensive if it requires
additional investments in cybersecurity
measures than an older technology would.
More generally, to protect increasingly
vulnerable network automation and control
systems, utilities need to consider:

Ongoing vulnerability assessments.
Before a utility can secure its network
automation and control system, it
needs to understand its system vulnerabilities.
Determining cybersecurity
needs requires an initial evaluation and
then, after developing and implementing
initial cybersecurity strategies,
utilities must continue re-evaluating
their systems.

Vigilant monitoring of the network
automation and control systems.
Utilities should monitor systems on an
ongoing basis to establish a baseline
for normal activities. By knowing its
baseline, a utility will be better able to
identify unusual activity.

Enterprise effort. With growing connections
between business units, cybersecurity
does not just impact network
automation and control systems. Utilities
should be working across business
units to develop a broader, more comprehensive
approach to cybersecurity
that addresses both control networks
and the corporate network itself.

Cybersecurity is more than just software.
Although there are effective
cybersecurity applications on the market,
utilities must research additional
ways of mitigating cyberthreats, from
knowing their users to improving their
physical infrastructure.

Thinking outside the regulatory box.
The new CIP standards provide a good
start for cybersecurity, but they cannot
adequately address all related issues.
Utilities should take the time to evaluate
additional cybersecurity recommendations,
such as the ISO 17799 Security
Standard.

Effective solutions for today and
tomorrow. Because many cybersecurity
measures are narrowly focused, utilities
should implement solutions that work
with their existing technologies and can
also adapt to meet future technological
needs.

Energy & Utilities: From your perspective,
what is the intelligent grid?
Frank Hoss: The intelligent grid focuses
primarily on the efficient, reliable and safe
distribution of electricity. It’s the marriage of
the electrical distribution infrastructure, with
a communications infrastructure. That can
be in the form of a number of communication
protocols such as two-way radio frequency,
broadband over power line, power line carrier,
cellular or WiMax. It’s that combination that
makes the data – which was always available on
the distribution grid – more readily accessible
for operating, maintenance and planning decisions.
It also lets the utility run its distribution
operations in a much more autonomous, automated,
remote fashion.

E&U: What do you see as the catalyst behind
efforts to make the intelligent grid a reality?
FH: A number of events have made it possible
to focus on the intelligent grid and make it
feasible. Data from distribution grid devices
has always been available, but the problem has
been retrieving it and using it remotely on a
wide scale. The needed communications have
been prohibitively expensive up til now. Over
the last several years, communication technology
has developed rapidly, so bandwith and
wide-scale deployment are more economically
feasible now. And many utilities are experiencing
government or regulatory mandates to
implement smart metering, which requires a
very robust, wide-scale communications infrastructure.
So utilities now have a mechanism to
get communications in place affordably.

And to a large extent, the distribution grid
has been operated as a “black box,” with few
actual data points used in grid operations.

But with programs and initiatives like demand
response, dynamic pricing, distributed generation,
renewable and alternative energy sources,
islanding, smart homes, much more needs to
be known about the distribution grid to keep it
operating efficiently and safely. And because
It’s not uncommon for it to take 10 or 12 years
from concept to execution, efficient grid operations
are going to be instrumental in alleviating
either transmission congestion or the generation
capacity problems.

E&U: What are the main challenges utilities
face in making the intelligent grid a reality?
FH: The intelligent grid is going to be an evolution,
starting with mandated smart metering.
For utilities, it’s a huge investment, and they
want to get it right the first time. Communications
is a major part of that. Even with widescale
communication solutions becoming
affordable, it’s still changing quickly, and something
else even better may emerge in the near
future. One good example is WiMax. Now you
have several major telecommunications companies
that are investing billions of dollars in
WiMax over the next several years, which could
make it a preferred solution for utilities.
Also, when utilities are considering intelligent
grid solutions, what they’re implementing
today must position them for the operating and
regulatory environments of 15 to 20 years from
now. That’s why it’s so important that they do
it right the first time, so they can leverage what
they’re doing today for future implementations.

E&U: How do utilities ensure the intelligent
grid solutions they choose for today will be
current in the fast-paced technology development
cycle of the future?
FH: When we talk about intelligent grid
solutions, it’s multiple solutions that will
need to be implemented and integrated.
But any solution must meet three primary
conditions: 1) the solution must be
open, meaning there’s no proprietary
software or code involved, so the utility
can interface with that particular solution
without having to go back to the vendor;
2) the solution has to adhere to a standard
communication protocol, as being IPaddressable
or in line with the IEC 61850
requirements; and 3) the solution must be
scalable. When you talk about building the
intelligent grid over the entire distribution
infrastructure, you’re talking about having
to integrate multiple solutions. The more
they can scale, the better for the utility
in terms of both integration and maintenance
of those solutions going forward.

Finally, utilities must realize the intelligent
grid is an evolution that will take
years to implement. They need to make
sure that what they’re doing today will
prepare them for 15 to 20 years down
the road. And the intelligent grid is not
the same for every utility, because each
one has different drivers that will result
in different solutions. One good way for
the utility to prepare is to start examining
plausible scenarios, maybe in five-year
increments, within which they may find
themselves having to operate at some
point and determining how the company
must respond to be successful. And then
extend these scenarios to 15 or 20 years
out. Each solution should be a building
block or enabler for what they’re going
to need to do over the next five years.
It’s not a perfect crystal ball, but I think
adherence to those three primary solutions
and the use of plausible scenarios
can help the utility develop a realistic
intelligent grid road map.

E&U: Are there any additional challenges
facing a utility that needs to implement an
intelligent grid solution?
FH: I think another challenge is the sheer
magnitude of data that’s going to be available
from implementing intelligent grid
solutions. It’s going to require that the
utilities have an extremely robust data
management capability. Probably the
best way to show this is to talk through an
example. If you take a utility that’s looking
to deploy just 1 million smart meters, that’s
going to be equivalent to about 110 million
records on a daily basis. Of those 110 million,
96 million would be usage records,
with another 10 million records associated
with voltage readings, and then an estimate
of about 4 million records associated
with missed readings/rereads. Add to this,
some of the other programs and initiatives
that are going on in the industry;
for example, distributed generation. If 2
percent of your customers will have distributed
generation, that adds 4 million
records per day for the distributed generation
requirement. And then when you start
looking at load management and demand
side management, that could add up to
another 50 million records per day. So
when you examine this on an annual basis,
all those records represent a combined 59
terabytes of data, which is huge. The other
aspect is the timing of the data. Prior to
intelligent grid solutions, the need for the
frequency of the data ranged anywhere
from months down to days, and in some
cases, maybe hourly readings. With the
intelligent grid solutions operating the distribution
grid in a near-real-time fashion,
we’re now talking about milliseconds in
some cases. This is new territory for many
of the utilities and for the vendors that are
providing solutions – not only to be able to
acquire the data but to process it and provide
it as output to wherever it’s needed
within a-millisecond-or-less time frame.

E&U: What’s the justification for implementing
intelligent grid solutions?
FH: Whether it’s being mandated or
whether the programs or initiatives are
under way to make utilities much more
energy-efficient, intelligent grid solutions
will have to be deployed. Utilities are challenged
to provide a business case any
time they spend money, not only internally
but a justification that will stand up to the
scrutiny of regulators as well as customers
and stockholders. As far as intelligent
grid solutions, whether it be smart metering
as the starting point or other solutions,
utilities have to leverage whatever
piece they’re currently considering to
its maximum value within the company.
For example, if you’re installing smart
metering primarily for automated meterreading
capabilities, consider whether
you can also use that meter information
to identify outages. You’ll detect the outages
much sooner and do a much better
job of deploying field crews to make the
repairs. It would take a very small additional
investment to be able to leverage
initial smart metering capabilities to support
outage management like this, and the
utility would realize some very significant
benefits. There are a lot of opportunities
like that in these intelligent grid solutions.

E&U: How does the GridWise Alliance
support the intelligent grid efforts?
FH: The GridWise Alliance is an organization
that has been together about five
years. It is a collection of utilities as well
as vendors primarily focused on effecting
policy, legislation and regulations
within the utility industry, at federal and
state levels. We want to make sure that,
whether it’s the Department of Energy,
FERC, other regulatory bodies, or Congress
that they’re putting legislation out
there that advances the intelligent grid
and the deployment of energy-efficient
solutions. We’re also interested in having
various companies like DOE provide
the right type of research and programs
to make the intelligent grid solutions a
reality. We want to stay on top of those
drivers and mandates to ensure they’re
in line with where the distribution grid
needs to go.

Energy Conservation. Energy
Efficiency. Go Green. Clean Tech.
Smart Grid. The utility industry
has enacted a number of initiatives with
a common goal – improving the quality,
value and long-term sustainability of
electricity delivery. Utility executives
are being challenged to chart a course
for the next century of utility services
and prepare the grid for changing
and often-unknown demands of their
customers. Utility issues are moving
beyond traditional revenue-cycle
services to embrace energy efficiency,
alternative generation and improve
customer services.

For the past 20 years, the industry
has focused considerable resources on
automated meter reading, primarily
intended to improve the accuracy and
cost of monthly revenue reads. Today
focus has broadened to a number of
related applications leveraging the
same technology assets. Dynamic pricing
programs hold great promise for
flattening the load curve, but require
more sophisticated and granular measurement.
This expansion of demand response creates significant opportunities
in both commodity hedging and
customer services, but may also change
the economics of distributed generation.
Increases in distributed generation will
have untold impact on distribution operations,
expanding the need for distribution
automation beyond the substation. The
convergence of these varied and interrelated
applications is creating exciting
opportunities to reshape the nature of
electric delivery.

Utility leaders are developing comprehensive
strategies to implement and
support a variety of new applications that
move well beyond meter reading. Understanding
the cumulative requirements of
these operational initiatives leads to the
recognition that an advanced networking
infrastructure is required to efficiently
manage the many devices that create a
“smart grid.” The right network brings
smart devices “on line” and allows for
real-time command and control of the
entire distribution system. Figure 1 illustrates
how an advanced utility network
connects the components of
a smart grid.

Utilities’ ability to realize the vision
of a smart grid is largely determined by
their choosing the right network infrastructure:
one that is functionally capable and cost-effective today, yet will support
future (and often unknown) requirements.
Advanced networking from the substation
to – and into – the premise creates
the fundamental platform on which smart
grid initiatives are built. The right network
infrastructure provides secure and seamless
connectivity, supporting any utility
application. Innovative, standards-based
applications can leverage smart grid
assets, turning new concepts into new
products.

The practice of a common network
infrastructure supporting a number of
applications is not new. For example,
consider the Ethernet system installed
in most offices. When the network was
installed, it was intended to support a
variety of applications, including email,
Web browsing, video delivery and more.
However, it was not necessary to decide
up front all of the computers, printers or
applications that would ever run over
that network. By choosing a standardsbased
network with the right performance
characteristics, new technologies
are easily and seamlessly incorporated
onto the network.

Utilities that make the right strategic
decisions regarding the networking platform
to deliver the smart grid will enjoy
similar flexibility and business value creation.
Failure to make the right networking
choice may result in a utility’s future initiatives
being hampered by the limitations of
its network.

Defining Your Smart Grid

The first step in implementing the right
network requires a utility to develop a
strategic plan and define the technical,
performance and price characteristics
needed to support both current and future
applications. Input from all areas of the
utility operation should be included, as
all departments are affected by – and can
benefit from – the smart grid. Operational
use cases that consider both current and
anticipated needs should be reviewed,
including representation from customer
service, metering, distribution operations,
information technology, revenue
protection, regulatory and rate making.
A considerable body of work is available
to assist in this effort, including published
documents from EPRI, GridWise and
UtilityAMI.

Open, Not Closed

Next, technical requirements of the
advanced utility network need to be
defined to support the operational
requirements of the business. Given
the variety of devices and interrelations
between several applications, use of
single-purpose or proprietary networking
should be avoided. Implementing
these technologies increases complexity
and cost, while simultaneously decreasing
long-term flexibility. Standardsbased
networking is the safest, surest
route when specifying the network
infrastructure of the smart grid.

The dominant standard in networking
is Internet protocol, known simply as
IP. Beyond the World Wide Web, IP is the
networking standard used in managing
nearly all telecommunications, cable
and information technology applications.
Hundreds of billions of dollars in collective
research and development make IP
the gold standard for mission-critical
networking around the world.

IP addresses many of the challenges
of running multiple applications and
devices on the same network. The IP
suite delivers proven technologies for
addressing, routing, quality of service
and a host of related networking functions,
all demonstrated at scale. With IP,
vendors can compete to develop best-ofbreed
products for a variety of applications,
yet share a common network infrastructure
to minimize cost and complexity.

Security Via Proven Technology

Historically, security in many remote
utility applications consisted solely of
“obscurity” created through the use of
proprietary products or the use of simple
passwords that were rarely changed. The
move to sophisticated command-and-control
applications mandates significantly
more proven and robust security across
the entire grid.

A number of proven IP security technologies
(for example, IPSEC or SSL) are
available to address this need. These
technologies are widely used in a number
of industries, including securing financial
transactions over the Web, to manage a
range of security concerns. Most importantly,
these technologies are constantly
improving due to the collective efforts
of countless vendors. IP suite security
technologies allow utilities to effectively
address concerns of spoofing, denial of
service and unauthorized access without
the requirement of reinventing new technologies
or relying on the efforts of
a single vendor.

Transport Independence

Application vendors have historically
been defined by the physical medium
of their solution. A vendor might be
“wireless” or “powerline,” but rarely
both. Over the past few years, there has
been a dizzying increase in the number
of available carrier solutions, including
WiFi, Wi-Max, Zigbee, Z-wave, DS2,
Homeplug, a variety of cellular standards
and more. Each of these “standards”
(reflecting Layer 1 and Layer 2 of
the ISO seven-layer stack) offers different
advantages and disadvantages.

A similar set of choices exists in enterprise
IT infrastructure. For example, office
computers may connect to the enterprise
network either via WiFi or wired Ethernet.
In the same way an enterprise can choose
computers that connect using a variety of
transports, it should be possible for a utility
to choose the physical transport that
best achieves the business case for any
particular part of its service territory (for
example, wireless for urban or suburban,
powerline for rural). Such flexibility can be
achieved by utilities in the same way that
it is achieved by enterprises: by deploying
standard, interoperable, IP-based products
rather than proprietary, transportspecific
ones.

Performance: Band width and Latency

Two critical issues when developing the
technical requirements of the smart grid
network are those of available bandwidth
and latency.

Bandwidth is the volume of data per
unit time that can move through the
network. The first step in determining
your requirements is to scope how much
data is required from each device and
application. Add them up on a timesensitive
basis. Where are the peaks?
Where does it break? Does the solution
allow you to add bandwidth if needed in
the future?

Although smart grid applications generate
significantly more data than monthly
meter reading and historic low-density,
one-way demand response applications,
it is not a large volume of data relative to
modern IT systems. Take the very familiar
example of a manual meter read: Manual
meter reading generates approximately
30 bytes per month of data, per customer.
A new smart meter, collecting multichannel,
15-minute interval data with event
logs, security logs, power quality and
other measures might generate over
10,000 times as much – perhaps 50 to
60 KB of data per meter, per day.

In an environment of applicationspecific,
low-bandwidth solutions, it may
seem that this much data could never
be supported, nor would ever be necessary.
The history of networking, however,
suggests that if additional data is made
available, customers will always find a
use for it. For example, over time, public
websites have migrated from serving
small text pages of a few KBs, to rich
graphical pages of hundreds of KBs, to
streaming audio and video at tremendous
data rates. These innovations are made
possible by the availability of economical
but rapidly growing Internet bandwidth.

Solutions from the traditional IP-based
networking world, deployed in utility
networks today, render it possible for
utilities to move and manage much larger
amounts of data than heretofore possible.
It is no longer necessary for utilities to
constrain their business operations –
or for that matter, their imaginations –
based on the limitations of their vendors’
technology.

Latency is equally important. Many
smart grid applications, including distribution
automation, outage alarming and
load control signaling, require very low
latency, while others, such as metering,
are more latency-tolerant. Smart grid networking
needs to support end-to-end and
device-to-device latencies not of minutes
or hours, but of seconds and milliseconds.
To manage traffic appropriately, networking
technology must support message
prioritization, allowing critical, latencyintolerant
messages primacy to other
network traffic. For example, meter- reading
acquisition is generally expected only
within a time window measured in minutes
or hours, while some DA applications
require that remote devices talk “across”
the network (without routing through the
back office) in less than a second.

Cost/Performance Balance Drives Value

Hardware economics historically limited
the deployment of multi-application
networks, forcing utilities to implement
vertically integrated, application-specific
solutions. While these solutions often
solved immediate needs, they also created
significant back-office integration
issues, increased operational complexity
and increased long-term costs. Many utilities
hoped that solutions offering greater
bandwidth, such as broadband over
powerline (BPL) would address this need.
Although BPL delivered strong functional
performance, the infrastructure costs
were measured in hundreds of dollars per
home passed, far exceeding the value to
be gained from utility applications alone.

Current hardware economics now make
it possible to deliver high-bandwidth,
low-latency networking at reduced cost.
It is now possible to deploy a systemwide
networking infrastructure delivering hundreds
of Kbps and sub-second latencies
at a fraction of the cost of broadband.
Specific pricing varies based on utility
specifics, but can typically rival traditional
AM R/AM I network pricing. This results in
the utility realizing significantly greater
benefits from a variety of applications
while simultaneously saving 30 to 40 percent
in operational costs versus operating
separate communications solutions for
each application.

Build It Right – Not Over

Less than 20 years ago, laptops, ubiquitous
cell phones, iPods and Xboxes
were not in existence. Considering the
emergence of new utility applications
and devices, it is hard to imagine what is
to come in the next five to 10 years. Even
today, there is an explosion of new utility
and consumer devices, including remote
controllable thermostats, consumer-based
energy storage appliances, customer displays,
fault indicators, distribution automation
applications and more. Regardless
of the applications or devices that emerge,
a standards-based network ensures that
they can easily be incorporated into the
smart grid. As exhibited in other industries,
including cable, IT and telecom, a
robust and flexible network is the basis
for competitive advantage.

An IP-based network allows a utility
to network devices not yet invented,
if they are built on IP. Product development
cycles for devices are much faster
than the life cycle of the network, so
one must expect new devices will
become available and utilities will need
to connect them.

The Right Network

Utilities around the world are now leading
the drive to capture energy efficiency as
the “fifth fuel.” Smart grid applications
including advanced metering, demand
response, distributed generation and distribution
automation offer utilities all the
tools to capture this value.

By specifying and implementing the
right networking infrastructure, utilities
are building a strategic technology platform
that enables a wide range of policy
and business initiatives for years to come,
avoiding concerns of near-term obsolescence
or functionally bridling technologies.
IP-based networking is really the
only choice when building the network
infrastructure for the future.

Project Introduction and Motivation

Electricity system challenges have
emerged in Washington’s Olympic
Peninsula that make it an ideal
laboratory for advanced intelligent utility
network (IUN) experiments. The peninsula’s
transmission feed is approaching
capacity as a result of population growth,
but there are significant concerns over
siting additional transmission or central
generation assets there due to the ecologically
sensitive nature of the Olympic
National Forest, which comprises much
of the northern portion of the peninsula.
In 2001, in response to that situation,
Bonneville Power Administration (BPA)
began their Non-Wires Solutions program
of demand response (DR) and distributed
generation (DG) experiments and pilots
with the goal of minimizing the environmental
impact of new transmission and
generation construction.

The Pacific Northwest GridWise™ Testbed
program, part of the Department of
Energy’s GridWise™ initiative, was established
in 2005 to coordinate technology
projects supporting the GridWise™ vision
of a transactive electric infrastructure
leveraging advances in information technology
and communications. The goals
of GridWise™ and the challenges faced
by BPA led to the creation of the Olympic
Peninsula Project as part of the Testbed
program. This project combines DR and
DG control, using a real-time retail market
clearing mechanism to determine the
optimal balance of DR and DG dispatch
in response to the current state of the
system.

The yearlong Olympic Peninsula Project,
which concluded in March 2007, was
a partnership involving a number of organizations:
Pacific Northwest National Laboratory
(PNNL), IBM Research, Bonneville
Power Administration, Invensys Controls
and several public and private utilities
in the region. PNNL, working with BPA,
defined the project and acted as the overall
project manager. IBM Research was
responsible for the system integration of
the various components with the market
clearing system developed by PNNL.
Invensys Controls provided the residential
equipment, including communicating
meters, programmable thermostats, load
control modules for water heaters and
broadband gateways for communication.

Design of the Experiment – Participants and Structure

The Olympic Peninsula Project involves
about 120 residential customers and several
commercial/industrial customers.
The residential customers are divided into
several contract types and a small control
group. The contract types are fixed price,
time of use/critical peak price (TOU/CPP)
and real-time price (RTP). One of the goals
of the project is to compare the performance
of the three contract types, with
particular emphasis on the RTP versus
TOU/CPP comparison. The hypothesis
being tested is that a properly designed
and implemented RTP system will be more
effective at managing Distributed Energy
Resources (DER) such as DR and DG than
a TOU/CPP system.

Rather than work through the process
of establishing experimental tariffs for
the residential participants, PNNL implemented
a shadow billing system with
an online payment portal. Participants
receive their normal utility bill based
on actual consumption, and pay it as
they ordinarily would. They receive an
additional online bill for the experiment
that represents their consumption from
a virtual distribution feeder. That bill is
paid via the online portal using funds
seeded into an account at the start of
each quarter, so customers experience no
additional out-of-pocket expense (part of
the project budget was reserved for this
purpose). The amount of funds that are
seeded is based on historical consumption
for each customer.

To give the experiment some level
of real-world motivation and benefit in
exchange for being responsive to critical
situations or high prices, PNNL refunds
any balance remaining at the end of each
quarter back to the residential customer.
Therefore, the more aggressively a customer
responds, the larger his refund
check will be. The experiment has been
designed to allow the most aggressive
participants to receive on the order of
$150 – roughly what it might cost to take
a family out to dinner, for example.

The virtual distribution feeder is implemented
with feeder modeling software at
PNNL using the real consumption information
being collected. This allows all the
participants to appear as if they’re on
the same feeder, and that feeder can be
artificially constrained to create critical
situations as part of the experiment.

For the residential customers, the
DR assets are the heating and cooling
systems and the water heaters. The
physical control components in the
homes have been provided by Invensys
Controls, and, in the case of the TOU/CPP
contract homes, the Invensys GoodWatts
pilot system is being used to implement
the control management. Homeowners
can set up occupancy profiles and timeof-
day schedules based on the predetermined
time-of-use tariffs. Critical peak
price events are scheduled as part of
the experiments based on expected constraint
times.

Fixed-price contract homes do not
have any DR automation, but homeowners
do have access to the same Web
portal as the TOU/CPP and RTP contract
homeowners, showing up-to-date consumption
information. Their equipment
also has the ability to help them manage
reduction of electricity consumption
through mechanisms such as night-time
thermostat set-backs, and part of what’s
being observed is how well those homeowners
reduced their overall load.

The DR assets of the RTP contract
homes, as well as the DG units, are
managed by their participation in an
artificial real-time market that clears
every five minutes. The market incorporates
information from multiple sources,
including the base transmission capacity
and price (provided by a live Dow Jones
feed of the wholesale price), virtual
distribution feeder capacity and actual
distribution cost, the current non-curtailable
load, the current curtailable load
and corresponding buy bids, and the
current dispatchable DG capacity and
corresponding sell bids submitted to the
market on each cycle. From this information,
the market determines a new clearing
price for retail electricity every five
minutes, and that clearing price is communicated
to the DR and DG assets. Each
of those assets then responds based on
its most recent bid and the parameters
defined by its owner.

Implementing the System Using Event-Based Middleware

The most innovative functionality in the
Olympic Peninsula Project is the market-
based control of DR assets in the
RTP contract homes. The experiment
requires each programmable thermostat
to understand how to create a buy
bid into the real-time market based on
homeowner comfort goals, the current
state of the device (e.g., the temperature
in the home) and the current trends of
the market. Note that the water heaters
are not bidding into the market; they are
using an open-loop control scheme in
which they only respond to price signals
from the market. Both the thermostats
and water heaters then have to respond
appropriately to the clearing price generated
every five minutes before the cycle
starts again. (The DG assets are also bidding
and responding but using a different
software and communication approach,
so they are not discussed here.)

The design and implementation of this
part of the system is based on a prototype
event-based programming framework
called Internet-scale Control Systems
(iCS) developed at IBM’s T.J. Watson
Research Center. iCS is an example of
what is currently emerging in the academic
and research community under
the category of cyber-physical systems,
reflecting the increasing importance of
monitoring and managing the physical
world in a much richer and more detailed
fashion. The physical world, in this case,
is the electric grid and its associated enduse
loads (such as heating and air-conditioning
systems).

In practical applications, there is an
additional aspect that needs addressing.
Cyber-physical systems will almost always
operate within the context of some business
environment. In the Olympic Peninsula
Project, the business environment
is reflected in the use of a market-based
control scheme and the need for devices
to be virtually augmented with business
domain awareness – they need to bid into
the market and react to clearing events.
For this reason, IBM Research is focusing on an expanded scope of investigation
referred to as cyber-physical business
(CPB) systems, which includes both
discrete, event-based systems and continuous
data stream-based systems. The
objective is to define a middleware framework
that addresses solutions, such as
intelligent utility networks, which involve
the integration of the physical operations
domain with the business domain, deals
with the challenges of a highly distributed
and heterogeneous environment, and also
reflects the time-dependent nature of
such integrated solutions.

By using a loosely coupled event-programming
approach with a very simple
programming model, iCS enabled all
the components of the system to be
abstracted and represented as simple
sensor, actuator or control objects in
the market-control application – from
the market itself all the way down to the
heating element relays and load-monitoring
sensors on the water heaters. The
market-awareness and bidding/response
algorithms were added in the middleware
framework without modification of the
devices themselves, essentially creating
new, more intelligent virtual devices from
the perspective of the market. This also
allowed the design to be easily adapted
in response to requirement adjustments
or other issues that surfaced during
development. In addition, iCS enabled the
overall market-control application to be
structured as a layered set of hierarchical
control loops linked together through the
event communication model.

Another benefit of using a loosely coupled
event-based approach is the level of
resiliency it affords. The system was implemented
so devices fall into a safe degraded
mode if there is any loss of communication
or failure in the market clearing signal.
Operations continue in a less-than-optimal
mode, but there is no catastrophic failure.
Once the problem is resolved, the system
returns to its optimal state.

Early Results

In spring 2007, the Olympic Peninsula
Project is just completing the region’s
winter electricity-constraint season.
Several cold periods occurred, and the
real-time market system operated as
designed. It limited total load on the virtual
distribution feeder to the configured
capacity through price-based DR management,
and it dispatched DG units when
the market price for electricity met the
sell bids. When no constraints existed,
the market price was stable and in the
expected range. In terms of performance,
it appears there was more optimal shifting
of load with the RTP contracts than with
the TOU/CPP contracts.

Another intermediate observation
was that the fixed price contract homes
sometimes shifted more load than the
TOU/CPP homes. That may have been an
artifact of the small sample size and the
individual homeowners involved, but it
is still an important result in that it indicates
customers will take advantage of
well-designed energy management tools,
and may have further been motivated
by having real-time information sources
about their consumption and cost easily
accessible.

An additional positive indicator surfaced
as a result of extending the project
from its original end date of September
2006 to March 2007: Most of the residential
participants agreed to continue their
involvement, presumably because they
were satisfied with the system’s positive
impact on their electricity consumption
and, to some extent, on their overall electricity
cost.

Conclusion

The Olympic Peninsula Project has
already succeeded in a number of dimensions,
from demonstrating the effectiveness
of a cyber-physical business system
approach in designing such solutions, to
generating a great deal of interest in and
awareness of the optimal management of
combined demand response and distributed
generation in dealing with a variety
of issues in electric grid operations. The
goal of using this project as both an
experiment and an educational tool is
already starting to be realized.

The project also represents an important
milestone in the realization of the
GridWise™ vision of an intelligent utility
network – by integrating utility and customer
assets; by leveraging advanced
information technologies; and by bridging
the operations and business domains of
the utility environment. Further, it has
been an extremely successful collaboration
of industry and public organizations,
including equipment and information
technology vendors, investor-owned
utilities, public utility districts, and the
Department of Energy’s National Laboratory
system.

This paper describes the application
of the modern automation
platform in electrical transmission
substations. The applications described
within are not “bleeding edge” or merely
theoretical. Working, proven examples
of everything described in this paper can
be found in at least one U.S. or Canadian
substation, but few substations include all
platform applications.

The modern automation platform
consolidates solutions previously implemented
in multiple physical boxes from
different suppliers. A single automation
platform can perform the roles of security
gateway, SCADA RTU, communication
processor, port switch, protocol
converter, sequence of events recorder,
alarm annunciator and substation HMI.

Economic, technological and cyber
security forces have driven this functional
consolidation. Figure 1 depicts the
many components of a traditional transmission
substation automation system,
while Figure 2 illustrates the configuration
of the modern automation platform.
Having fewer boxes reduces hardware,
purchasing, installation and maintenance
costs, while having a single configuration
tool reduces training and configuration
costs. Thanks to technological advances,
powerful yet inexpensive processors can
now perform complex data processing
and display tasks in the tough substation
environment without a fan, hard drive
or other moving parts. Modern software
development tools and operating systems
are also ideally suited for the embedded,
real-time environment. Therefore, technology
is no longer a barrier to entry for
niche vendors.

Finally, since substation security functions
are easier to manage through a
single gateway, the centralized topography
enabled by the automation platform
allows a simpler and more secure
approach to cyber security.
Each of the roles of the modern automation
processor is described below:

Security Gateway

The specialized security functions
required in transmission substations
include:

• Firewalls;
• Data encryption and VPNs;
• Local and centralized authentication;
  and
• Event and alarm logging.

Secure physical routers/switches from
traditional IT suppliers and security
software running on PCs have traditionally
provided a solution. However,
transmission substation engineers prefer
not to purchase and maintain separate
IT-oriented boxes, some of which may
not meet all substation environmental
specifications and may require specialized
and extensive training. To resolve
this situation, the automation platform
has taken on these security functions as
resident tasks, with functions optimized
and simplified for substation automation
applications.

Traditional SCADA RTU Functions

The traditional substation remote terminal
unit (RTU) has performed its task
reliably in transmission substations since
the advent of computers, reporting realtime
data to one or more Masters and
executing control commands. The challenge
has been to get the RTU to do more
than it was designed to do. Increasingly,
users need to obtain SCADA data from
IEDs using IED protocols, some of which
are proprietary and require specialized
knowledge to emulate. Users also need to
move data securely from the RTU to nontraditional
users. The modern automation
platform performs all of the traditional
RTU tasks plus these new tasks in a single
package.

Communications Processing

Communications processing includes the
tasks of protocol conversion and media
conversion. These have often been handled
by specialized and separately powered
hardware boxes. Examples include
separate bit-to-byte converters, legacy
protocol to DNP3.0 converters, RS232-to-
RS485 and serial-to-fiber converters and
Ethernet-to-serial converters. However,
all of these options increase cost, complicate
automation system design and
reduce system reliability.

Alternately, the modern automation
platform performs these functions with a
modular software and hardware architecture
that permits any Master or Slave protocol
to be configured on any combination
of built-in bit synchronous, RS232, RS485,
fiber optic or Ethernet ports. The resulting
design is cleaner, more affordable and
more reliable.

The modern automation platform also
enables data to be routed in nonconventional
ways, such as Master-Master (using
the automation platform as a mailbox)
and Slave-Slave (reading data from one
Slave and writing these data to a second
Slave).

Distributed I/O With Accurate Time

Most traditional RTUs have required
all substation inputs and outputs to be
wired back to a centralized location,
particularly if the inputs needed to be
time-stamped to an accuracy of one millisecond.
Today high-performance distributed
I/O modules with IRIG-B timecode
formats can be mounted on substation
breakers and transformers, with a
single fiber optic connection routed back
to the substation RTU or automation platform.
Some distributed I/O designs can
also synchronize I/O module time with
IRIG-B being transmitted over the serial
connection to the module, or using NTP
over Ethernet, to further reduce IRIG-B
wiring costs.

Accurate Time Management

Accurate time-formatting (such as IRIGB)
and accurate time-stamping of events
have been part of transmission substan
tion automation design for at least 15
to 20 years. But there has been little
coordinated and integrated management
of time stamps from multiple IEDs in
multiple protocols and to multiple Masters.
For example, time-stamped events
in proprietary relay protocols need to
be transmitted to the SCADA Master in
the SCADA protocol, typically as DNP3.0
events with time. In addition, IEDs that
communicate using protocols that do not
support time need to have a time stamp
placed on events as they are received.
Multiple SCADA Masters and HMIs may
each require the same events with time
but possibly in a different protocol. The
modern automation platform handles
these time management functions as
standard routines.

Local Logic Processing and Control

Local logic processing and control for
schemes such as breaker-and-one-half,
synchro-check, breaker failure, underfrequency
load shed and black start have
traditionally been implemented in protective
relays and dedicated programmable
controllers. While this is still the case, the
trend is toward less logic implemented in
programmable logic controllers and more
in the automation platform. The math
and logic packages available in the automation
platform are based upon open
and well-supported tools such as Visual
Basic and IEC 61131. Many logic tasks
that require data from different parts of
the substation are handled easily by the
automation platform, as it is the central
repository for all substation data.

Protective Relay Record Management

Traditionally it has been common to apply
a separate communication processor or
port switch to provide access to engineering
data residing in substation protective
relays. The tools to access and process
records have also traditionally resided
on remote PCs. Today the automation
platform serves as a pass-through port
switch, supporting dial-in connections
as well as local and remote Ethernet
connections. The automation platform
can also filter and preprocess relay fault
records, so that only pertinent records
are retrieved for analysis (for example,
filter-based upon fault distance) or to
simplify analysis (for example, pulling
apart separate relay events from one
large flat “file” and integrating with
record viewer packages).

Substation HMI

Substation engineers have always desired
local tabular or graphic visualization of
real-time operating conditions in the
more critical transmission substations.
The challenge has been to justify the
relatively high cost of buying the HMI
software, configuring the displays and
maintaining the PC-based hardware
platform and operating system. Users
report that the highest single cost in
substation automation is the PC-based
HMI. Some of the PC hardware issues
have been addressed with the advent of
rugged PC power supplies and solid-state
hard drives, but other costs remain. The
modern automation platform can serve
up standard preconfigured Web pages
to a hardened PC or a laptop, reducing
software configuration costs. Some automation
platforms can also support video
output of Web pages, allowing the use of
a hardened LCD panel instead of a PC and
further reducing hardware and maintenance
costs.

Alarm Annunciation

Separate hardwired or serially driven
alarm annunciator panels have been
commonly applied in substations to alert
operators of abnormal conditions. These
hardware displays can be replaced with
software-driven displays that are generated
and served out from the automation
platform and viewable as a Web page on
any PC or as video on an LCD monitor.
Displays can be identical to the old hardware-
based displays or can change based
upon real-time operating conditions.

Sequence of Event Recording

Dedicated sequence of event (SOE)
recorders have been applied to determine
the precise sequence of operation of substation
equipment before, during and after
substation events, and to verify proper
operation (to a typical resolution of one
millisecond). The modern automation platform,
combined with high-performance
distributed I/O, can replace the dedicated
recorder with preconfigured Web pages
served out to local or remote PCs. Savings
include reduced wiring costs and reduced
training and configuration time (same
configuration tools and database as RTU,
alarm annunciation and other functions).

The Future

Economic, technological and security
forces will continue to drive the development
and application of the single, powerful
automation platform in transmission
substations. Designs will become easier
to configure and commission, and will
take on new software tasks to convert
data into information, and information
into better operating decisions. These
efficiencies will benefit all utilities, as
they are challenged to contain costs
without sacrificing service.

The business world seems to be all
about “smart” these days. You
have smart cars, smart money,
even smart mobs. The utility industry
is no laggard in this wave of smartness.
“Smart meters” is one of the current
buzz phrases that quickly invoke discussions
about “smart grids” and eventually
“smart customers.” All this is via the
smart technology known as advanced
metering infrastructure (AMI).

But smartness will not come cheap.
The investment in AMI is significant;
north of $130 per customer at current
market costs. The business cases for
such projects focus heavily on the meter
– what type with what functions; the
network required to collect all the data
and what systems; new and current ones
modified that are required to process the
tidal wave of data AMI will generate. The
benefits are no more meter readers, far
more efficient operations and customers
who demand responsive.

Today it is estimated that only 10 percent
of U.S. customers have some form
of automated meter reading device. And
although that number will change by a
healthy level in the next five to seven
years, widespread implementation of AMI
is still in the planning and pilot stages at
many utilities. This is not out of desire to
implement AMI but because the business
case does not balance.

However, the industry has an opportunity
to change the balance of cost-tobenefit
in their business cases. By aggressively
adopting open standards, the cost
of AMI meters can be reduced significantly
and the applications that “harden” some
of the “softer” benefits in business cases
can be effectively developed. The fundamental
shift in thinking that is required is
the realization that AMI is not a meter system
replacement. It is transformational. It
is a classical innovators’ dilemma. AMI is a
computer and communications system. It
is hardware and software. Forget meters.
Think computers. Think applications!

Let’s first take a look at the key components
of AMI – the smart meter, the
smart customer and the smart utility
– and then consider the challenges this
vision presents.

Smart Meter

AMI starts with a smart meter. The frequent
question is, how smart? Is an AMR
meter with some application integration
in the back end sufficient?

AMR automates the collection of meter
reads. No more meter readers doing their
appointed rounds on a monthly cycle.
Instead trucks make the rounds at up
to 35 miles per hour and AMR meters
transmit their reads. Or a fixed network of
varied technologies collect the reads, perhaps
more frequently than monthly, but
all to the same end – replace people with
automated meters.

But consider the AMI vision. Your
electric meter is really “smart”; it has an
embedded computer; it tracks and stores
all the vital information about your energy
use; and it communicates your usage and
other interesting information back to the
utility on demand. It will send out a distress
call to the utility when the power is
out. It can connect to an in-home, IP-based
LAN that communicates with all your
appliances, monitoring them and, in times
of power shortages, can even change the
operations of these appliances.

AMI requires a meter that is functionrich
and able to provide information in
near-real time. It needs to be two-way,
upgradeable, programmable and extensible.
This ups the ante when the decision
as to what type of meter and how smart it
needs to be is considered. Accordingly, it
significantly increases costs.

Most business cases treat the AMI
meter as 20-year utility property. With
over 75 percent of the AMI cost in the
meter, its associated network and its
installation cost, changing out this infrastructure
in a near- or medium-time
horizon will be a financial problem to the
business case. A five-year horizon like
that used for computer equipment would
quickly sink most business cases.

Nonetheless, conceptualizing the meter
as a computer versus a utility meter is the
defi ning change in perspective required
for achieving AMI benefi ts (see Figure 1).
And with this realization enters the opportunity
for the fundamental, aforementioned
shift. The AMI meter is a computing
platform and thus the value comes from
the applications it supports – not just the
functions it replaces or automates. These
are applications that support the smart
customer and the smart utility.

The Smart Customer

For AMI benefi ts to be realized you need
a “smart customer.” A smart customer is
both informed and empowered in regard
to their energy use; informed because
they know their energy use and its associated
cost over time and how that energy
use is derived from all the energy-consuming
devices in their premise.

Moreover, a smart customer is an
empowered customer. It does little good
for a customer to receive hourly or even
sub-hourly energy and cost information
if they have no idea what to do with the
information. They need access to a set
of easy-to-use applications that allow
them to visualize the cost of their energy
behaviors and to make informed decisions
on how to change those behaviors.
These applications need to have the ability
to perform what-if analyses to support
customers’ energy and cost-effi cient
behaviors. And even more ambitiously,
the AMI system needs to deliver real-time
price signals that alert customers of highcost
conditions and that allow utilities
to send signals directly via a meter connected
to a customer-premise LAN that is
able to control customer energy-consuming
devices.

The Smart Grid

Utilities need to be smart for the business
vision to work.

Utility support for operations in today’s
world is basically forensic in nature. Take
outage management, for example. Generally
the utility waits until enough customers
call to report an outage to triangulate
on the source of the problem and begin
restoration operations. In the case of a
grid component failure such as a transformer
explosion, they wait until the
explosion happens, usually unaware of
its impending failure. Why? Because they
have generally no telemetry installed
to give them the required information
before the customer calls or the transformer
blows.

But with AMI meters deployed to all
customers, the utility gains the ability
to continuously monitor the operational
status of the network at the end points.
Network component failures are instantaneously
observable and can be isolated
directly to the limited portion of the grid
impacted. During an outage, the utility
already knows a customer’s power is out
and is able to provide a timely and accurate
estimate for the time of restoration
when a customer calls.

On an ongoing basis, the history of
key metrics such as distribution loading
– from which key operational metrics of
transformer performance can be induced
– are collected and analyzed. Predictive
maintenance and the replacement and
upgrade of network devices are managed
more effectively and effi ciently. The
distribution grid is managed predictably
instead of forensically because the AMI
system can operate as a proxy for many
of the historical functions of OMS and
SCADA.

Challenges

If this compelling vision of the benefi ts of
AMI did not have its challenges, the current
less than 10 percent penetration rate
of AMI in the U.S. would be much higher.
It turns out the challenges are formidable.

The first challenge is the cost for the
function required. AMI meters are very
expensive relative to their predecessor, the
electromechanical meter. Clearly, a meter
that is much “smarter” and more function-
rich than an AMR meter is required
to realize the benefi ts of AMI. Such AMI
meters exist in the market today and are
generally too expensive to be deployed
on a large scale to all customers. A signifi –
cant element of the cost is in its research
and development. As R&D is amortized
and manufacturing capabilities grow and
mature, one can expect the cost to decline,
which has already occurred over the past
few years. But for AMI meters to reach
the near commodity status of today’s
electro mechanical meters, the industry
needs take a page out of the computer and
telecommunications industries’ playbook.
They need to embrace standardization
and openness in this technology. AMI is,
after all, a computer and communications
network, and the recent history in both
industries is a dramatic example of what
open standardization can do to drive down
costs and deepen functionality.

The second challenge focuses on the
set of applications required to realize
both the smart customer and the smart
utility. These are customer applications
that can provide the information that customers
will access to become informed
and empowered. These are also utility
applications and analytics that support
operations, as well as the integration of
these applications into the operational
processes of the utility. Both sets of
applications require a fundamentally different
view within the utility in regard to
the basic systems and business process
supporting customer information and
utility operations.

Implementing these systems both
through new systems and modification
and adaptation of current systems is
complex. Most business case analysis
focuses on the obvious issue of scalability
due to the large increase in raw data
processed within the utilities systems.
But the problem is much more pervasive
than data volume. The integration
required between batch-orientated billing
systems, real-time outage systems and
near-real time customer information analytics
applications – all utilizing the same
data stream from the same AMI system
infrastructure – is as complex and sophisticated
as any system the industry has
yet implemented. The important starting
precept is that the AMI system is a complex
collection of multiple, interrelated
computer and communications systems,
not all of which are operating at the same
temporal cadence. Effective integration
of these systems is the controlling problem
impacting successful implementation.

The process of planning to add a new
meter data management system that connects
to the AMI network, and connecting
it to the current utility systems through
currently employed techniques, will more
than likely deliver sub-optimum results.
AMI systems cannot be glued together
in some gerrymandered architecture. A
much more robust, flexible and extensible
architecture is required. Again, as with
the meter, standardization and openness
is an absolute requirement. Building
systems around Web services and
SOA architectures is already adopted by
industries with the same fundamental
problem of building similarly complex and
extensible systems. Only through such
open and standard state-of-the-art architectural
approaches will AMI applications
be successfully and effectively integrated
to perform all the functions required to
achieve AMI benefits.

Adopting open standards for both the
meter and customer and utility application
development will drive the cost of
AMI implementation down dramatically
and increase the realizable benefits to
both the customer and the utility. It will
also enable the level of application innovation
required to deliver the benefits
of AMI. AMI champions need to focus on
computer systems and applications – and
not on the replacement of electrical measuring
devices – to be successful.

It’s the smart thing to do!

Utilities have long been on a
quest for better ways to handle
and make sense of data. Few
other industries need control over such
huge data volumes simply to handle
the everyday aspects of their business
– locating assets, dealing with customers
and their consumption, directing field
crews, repairing outages and addressing
ever-increasing requirements for
efficiency, cost control and community
contributions.

It is not surprising, then, that utilities
were prime candidates for the earliest
forays into business intelligence.

Initial, Partial Success

In the 1980s, reporters dominated the
scene. Though seemingly straightforward,
they proved difficult to use. IT experts had
to not only translate business requirements
but also to know precisely what
data was stored, where it was stored,
how the value was populated and what
caused it to change. Coding was an IT art
of the highest order, requiring specialized
knowledge of proprietary tools that were
time-intensive and laborious. Thus, information
users could not write or modify reports and had to wait months for IT to
make even high-priority changes. That
essentially guaranteed that any information
gleaned would be out of date.

Utility business intelligence made a
major leap forward with the development
of knowledge warehouses and data
marts. These hubs of mined information
from varying data sources were generally
separated from the production environment
so as not to risk the integrity of live
data or hinder performance of production
systems. They also permitted online
analytical processing (OLAP) – fast, interactive
and, above all, multidimensional
information access that facilitates analysis
via presentations like the Balanced
Scorecard. Data mining technology took
us a step further in helping users to find
patterns in large amounts of data.

As with report writing, however, data
warehouse implementation generally
required lengthy analysis, programming
and setup – complex tasks with which few
in-house IT experts could cope. Options
and compromises were many and decisions
hard to come by. Many companies
became mired in “analysis paralysis,” and
many projects never produced concrete results. As a consequence, most organizations
attempting to implement data warehouses
hired busloads of outside experts
from the major consulting firms – for
months or even years at a time.

Even when experts implemented data
warehouses, users still faced the same
problem they had had with report writers:
They could not make changes or
ask today’s most relevant questions. So,
few of these early projects succeeded in
attaining the business decision improvements
hoped for. Some got partway to
their goal, if only for a short time. Many
declared victory and then sank beneath
the waves of an annual report’s footnotes.
And data remained isolated within its own
individual departmental silos.

New Approaches

In the 21st century, applications vendors
have begun to build a new road to business
intelligence. Its raw materials are:

• New delivery techniques, especially
  Web-based portals and the technology
  that lets users easily customize them.
• Pre-built extracts from common commercial
  packages such as ERP, CIS, etc.,
  that require less specialized expertise.
• Aggregation, bit mapping and other
  OLAP optimization techniques built into
  standard database products, making
  them ubiquitous and easier to use.
• Data warehousing techniques that
  support real-time or near-real-time
  data feeds, effectively supporting
  the analysis of rapidly changing
  operational data.
• Standards that allow simple interaction
  between the OLAP results and business
  processes on the front end, closing the
  loop between measuring results and
  improving processes.
• Perhaps most important, tools that
  bridge the gap between the business
  and IT departments,requiring less
  “translation” between roles and allowing
  each to work in their own specializations
  to get the job done.

Results Out of the Box

The result of this initiative is embedded
business intelligence – solid, quick
and easy analytical capability across an
enterprise. This new approach, generally
designed with a specific industry in
mind, automatically collects, stores and
analyzes data within a live application
environment, hence the common term for
it, “embedded BI.” It permits users – from
the CEO down – to get the data they need
presented in virtually any manner on a
regular basis or in response to on-the-fly
queries. It draws together information
from different databases and presents
results in easily accessible, graphical
forms. As a result, managers and staff
at any level can perform the updates,
historical comparisons and cross-organizational
comparisons that enhance
real-time decision making. And, though
out of the box and easy to upgrade, these
products still have the flexibility to permit
sophisticated customization where
required.

Hesitation

Embedded BI is not a complete replacement
for the large, complex BI projects
custom-built from vendor-provided tools,
which can:

• Draw data from a larger variety of
  sources;
• Respond to an organization’s specific
  needs; and
• Use vocabulary and other approaches
  unique to the organization.

But the cost of these customized solutions
is very high – easily an order of
magnitude higher than the typical cost
of embedded approaches that are linked
to a specific application. And, over time,
even the simplest and most straightforward
custom applications tend to become
burdened with modifications to accommodate
the demands of individual users.
These changes may seem logical, particularly
those that minimize the time users
must work with the application before
getting their results, but as they accumulate,
they lower system performance
and degrade overall processing. The outcome
is frequently the BI “Franken-App”
– complex solutions that become increasingly
difficult to maintain and change
over time.

Why Choose Embedded BI?

Embedded business intelligence is different.
Linked to a specific application or
set of applications, it provides such
benefits as:

• Significantly lower cost.
• The ability to tailor applications to specific
  needs and to retain those changes
  over time – in other words, tailoring
  without loss of upgradability.
• The ability to perform prototyping to
  provide near-term approximate answers
  while accelerating the ultimate solution
  design. With embedded BI, organizations
  do not have to spend weeks or
  months analyzing a specific problem
  before embarking on design. Embedded
  BI permits the setup of a quick
  prototype for testing, which can then
  be improved by going back and refining
  the ETL, the schema definitions, the KPI
  definitions, etc., until the new solution
  produces appropriate results.
  Other benefits include rapid implementation;
  vendor-provided updates,
  maintenance/training; and exposure to
  and learning opportunities via the vendor’s
  community of users and experts.
  Not all embedded approaches are
  created equal, however. Among the elements
  that increase value are:
• A working framework from which
  to pull information and deliver value
  immediately, which will fast-track pertinent
  knowledge based on actual data.
  Look for solutions that are delivered
  with predefined data extracts and
  reporting structures.
• A “starter kit” of common metrics and
  measures that are used throughout
  the industry. Utilities companies should
  look for metrics that focus on such
  concerns as outage duration, preventive
  maintenance, bill accuracy, queries
  resolved during an initial call from the
  customer, and so on.
• Performance protection. While many
  business intelligence applications can
  operate comfortably within a production
  environment, that is not always the
  case. For very high volumes, it’s crucial
  to implement a solution that extracts
  data directly from the production operating
  system and holds it in a more efficient
  vehicle. This will maximize system
  performance and operational flow.
• Flexibility. Business intelligence effectiveness
  depends on users’ ability to
  adapt information presentation to the
  way they work – not the other way
  around. Simple information access
  is not enough. Users must be able to
  filter and sort it in order to highlight
  the exact information they need when
  they need it. Predetermined analytics
  should provide not an end point but,
  instead, a launching pad from which
  users can create the performance indicators
  they need. Additionally, users will
  recognize problems and opportunities
  for improvement far more quickly when
  they can adapt graphical formats for
  fast and easy examination and analysis
  of evolving situations.
• The ability to add and subtract business
  facts and other data quickly and
  easily. Users need to be able to see
  the consequences of, for instance, new
  regulations under consideration, a sudden
  population increase or decrease, or
  a rise or fall in the price of fuel or other
  supplies. In some cases, users may need
  to add an entire new source of data to
  permit effective response to new business
  imperatives; the best BI applications
  make that easy.
• Provider expertise. The most effective
  solutions focus on the needs of a
  specific industry. The solution provider
  must have a broad knowledge of the
  industry’s business applications, regulatory
  environment and unique business
  challenges.
• Appropriate sizing. An application that
  works with a single data source can
  address specific customer or organization
  needs. That will probably be inadequate,
  however, for executives who
  must address issues across multiple
  enterprisewide business processes.

There is no denying, that embedded business
intelligence requires balancing and
trade-offs. Not all of these solutions can
draw information from multiple, complex
databases, and when they can’t, missing
information can degrade the decisionmaking
process. A second contentious
area is the extent to which an application
accommodates unique organizational
needs; accommodations that decrease
user time and effort may also, in the long
run, restrict application upgradability.

Implementing Embedded BI

Implementing an embedded business intelligence
solution is vastly different from
implementing customized BI. While custom
BI solutions generally rest on extensive
and extended analysis, out-of-the-box solutions,
as explained above, rest on a prototyping
process in which you begin with
approximation and move iteratively closer
to the ideal by redefining and redesigning.

Successful prototyping means avoiding
overanalysis. Instead it’s prudent to move
relatively rapidly through initial steps that:

• Define KPIs. Begin with those most
  important to organizational success and
  add refinements later.
• Determine the reporting structures
  (star schemas) that support the KPIs.
• Map data sources to the structures.
• Determine scheduling. Extract-frequency
  depends on how often information
  changes and the consequences of using
  outdated information. For tasks like
  product introductions, managers may be
  able to make sound decisions based on
  trends shown across several months of
  historic data, the most recent of which
  may be a week or two old. For tasks like
  informing managers of progress on a
  current outage resolution against the
  previous year’s trends, extracts every 15
  minutes may be too slow.

These steps are all much easier with a vendor
that provides considerable application
power and guidance during the first implementation.
Use the vendor’s experience
and “conventional wisdom” to develop
reasonable solutions to KPI measurement,
then improve upon them with experience.
Remember that the goal is getting a solution
that starts producing answers almost
immediately. While a vendor should also
deliver long-term plans for upgrades, the
initial solution has to fit today’s needs
along with the configurability and flexibility
to support tomorrow’s unanticipated
conditions and changing goals.

Does Embedded BI Work?

Before they give embedded business
intelligence a try, many organizations will
have already experienced the failure or
only partial success of large-scale, customized
business intelligence projects.
Additionally, because embedded solutions
are almost always connected intimately
to an application or set of applications,
organizations are likely to encounter different
vendors’ versions of embedded
BI as they move across the IT structure.
Therefore, it may be possible to move
beyond traditional measures of application
success like “how many people are
using the product after a year” or “what
is the level of user complaints.” Here are
some metrics with which to compare various
approaches to BI:

• How long did it take to achieve usable
  results, i.e., were the KPIs defined in
  step 1 actually used or did the process
  take so long, the requirements changed
  before the solution was delivered?
• What were the costs in time and money?
• Were investments made in the application
  at the start usable throughout the
  project? In other words, could we add
  new data or change parameters along
  the way without throwing out everything
  accomplished to that point?
• Was rollout quick and reasonably painless?
  Web-based solutions tend to score
  high in this category, since their familiarity
  minimizes user training. They also
  ease the IT burden, since there’s no
  software to roll out.

The Case for Embedded BI

Today’s challenges and tomorrow’s trials
and opportunities necessitate a state of
readiness and business agility that can
only be supported by rapid, ready access
to business-critical information. Managers
and executives need as much help as
technology can offer to condense volumes
of complex, disparate data from multiple,
mission-critical data sources into a cohesive
knowledge base. From this base, they
can identify risks, determine trends, more
accurately forecast, and identify causeand-
effect relationships that might not
have otherwise been apparent.

With this information, organizations can
optimize operations, reduce the cost of
servicing customers and identify opportunities
to sell new products and services.
This level of intelligence promotes a culture
of continuous improvement, which
enhances the ability to predict changing
market conditions, and it offers executives
the ability to highlight and respond to concrete
opportunities for business optimization
and an enhanced bottom line.

Theft of service has been discussed
more in the boardroom in the last
three years than it has in the past
30. Why all the new interest?

In short, the boardroom is responding
to its perception of how some consumers
may react to skyrocketing energy and
resource prices. Unprecedented rates
are pushing more consumers than ever
before to resort to actions like stealing
service. There is even word of an emerging
market for professional “service tamperers,”
who are paid to assist consumers
in bypassing metered service.

Skyrocketing prices aren’t the only factor.
Sarbanes-Oxley, competition created
through deregulation, and the public and
state regulator’s demand for better corporate
governance all contribute to making
theft of service a 20th-floor topic.

No territory is immune and no service
is safe from theft. With a little knowledge,
a little nerve or the right contractor, anyone
can tap into electric, gas and water
services. The majority of theft occurs in
the residential sector, but the majority of
all revenues lost, estimated between 1 and
3 percent of total distribution revenues,
occurs in the commercial account sector.
A utility with $1 billion in revenues potentially
loses between $10 million and $30
million each year to theft, and more than
two-thirds of that loss is within the relatively
small commercial account sector.

We will focus on commercial accounts
to discuss how and where the most significant
theft occurs and what leading North
American utilities can do and are already
doing to curb such illicit activity.

Stealing Around the Meter

The smarter thieves do not steal all of
the service or tamper with the meter
directly. This is an ill-conceived act with
short-term payoff, since it’s likely to generate
a zero-read event at the utility.

Locking and sealing programs, solidstate
meters and automated meter reading
(AMR) tamper flags all help protect
the meter but not the energy. A smart
thief knows this and tampers around the
meter instead.

AMR tamper flags do not detect
bypasses that divert energy around
the meter, while locks and seals do not
prohibit theft outside of the meter box.
Analysis of theft cases among Detectent’s
AMR-enabled customers between
January and December 2006, revealed
that most thefts occur without any corresponding
tamper flag. Moreover, the
majority of tamper flags triggered don’t
even indicate a theft case or any other
spurious activity. Most of them are the
result of normal daily activities, such as
contractor-induced outages and maintenance
service calls, as well as external
factors like vibrations from nearby
machinery. The enormous volume of false
alarms tends to minimize the impact of
those few valid tamper flags.

So the question is, how can a utility
defend against theft of service when
the thieves are getting smarter and the
technology deployed does not address
the major losses that occur outside of the
meter box?

Protect the Service, Not the Meter

Service protection is not achieved through
locks and seals, although a locking and
sealing program is an important component
of meter security.

The service can only truly be protected
by recognizing anomalous consumption.
To do this, however, we first have to
understand what consumers do with the
energy and resources they receive. Then
models can be developed to establish
expected consumption patterns. Erroneous
consumption patterns can then
be detected as deviations from normal
expected behavior. As a result, both theft
around the meter and direct tampering
can be exposed.

Knowing the Consumer

Customer information systems (CIS) were
designed to facilitate customer identification
and business transactions such as
billing. They weren’t designed to store
the variety of data involved with getting
to know the customer better. A great
illustration of this is the classification of
customers by standard industry code. A
standard industry code of “full-service
restaurant” provides no indication of how
a restaurant uses energy and resources
to fulfill the needs of its customers. With
such simplistic coding, “full-service restaurant”
can include everything from
a large steak and seafood house to a
small sandwich deli. Both use energy and
resources in completely different ways to
serve their customers.

If, however, the data indicated that
the full-service restaurant was actually
a pizza parlor with an eat-in area, an
extrapolation on the energy needs of that
restaurant could be made. For instance,
you would expect to find a certain number
of pizza ovens to meet the demands of a
certain dining-area capacity. You would
expect seating areas to be air-conditioned
in the warm months and heated in the
cooler months. You would also expect
refrigeration in scale with the number of
potential patrons served.

One approach to protecting service collects
all of this information to create peer
groups and predictive models.

Consumer Modeling and Peer Grouping

Basic consumer models take into consideration
the standard industry code, but
more advanced models expect that code
to be broken down into greater detail.
These models are designed to understand
energy and resource consumption needs
based on in-depth knowledge of expected
usages. For the example shown in Figure
1, the broad category of full-service restaurant
is refined by cuisine, then by a
sub-type and, finally, into groups depending
on the environment where the food
product is served. When you break data
down into subcategories, you can determine
that the consumption of energy for,
as an example, a takeout pizza restaurant
is significantly different than that of an
eat-in pizza restaurant.

The addition of chain affiliation can
make these models even more precise
in determining expected energy and
resource usage, because most locations
of the same chain will have standard
equipment specifications. In Figure 2,
expected consumption varies not only
by the restaurant environment but also
by the national chain that dictates the
installed equipment specification.

Applying the Logic to Other Business Types

The restaurant example may seem like
an obvious case for consumer modeling,
but in reality, consumer modeling can be
applied to all business types. Hotels and
motels can be classified by number of
rooms and leisure and business amenities
provided. Gas stations can be classified by
complimentary services such as car wash,
shopping and restaurant facilities.

Gathering the Data

The obvious next question then is, if the
data does not reside in the customer
information system, then from where does
all this information about the consumer
come? It might surprise you to learn that
the information is gathered from a huge
variety of publicly available sources.

The Internet offers access to vast
repositories of information on commercial
consumers. These are commercial
entities that can create good will, and
benefit in other ways from having the
public know about them. All we have to
do is know how and where to obtain that
information for our own purposes.

There are numerous databases you can
purchase that offer a wealth of information
about commercial consumers, such
as type of business, products and services
offered; environment in which the
products and services are offered; size
of business; location; number of employees;
hours of operation; sales volumes;
and so forth. This information, once
paired with data from the utility’s own
computer information system, provides
quite a clear picture of how commercial
consumers utilize the energy and
resources delivered to them.

Establishing Peer Groups

Gathering data on a single consumer is
only valuable if there is a like set of data
on similar consumers available for purposes
of comparison. Therefore, peer
groups must be established and similar
data elements must be gathered for each
account in a peer group.

Peer groups can be developed for each
service territory as well as nationally. Geographically,
local peer groups may represent expected energy and resource usage
more reliably, but national peer grouping
offers a larger base with which to make
comparisons, develop trends and establish
expected norms. Figure 3 shows plots of
consumption for various businesses of
the same type against an expected norm
calculated from the peer group’s data. The
expected norm is illustrated by the dark
magenta line and outliers are considered
those that do not follow the same pattern.
Actual monthly consumption is not a significant
factor, as this particlar grouping is
not concerned with service capacity.

Eliminating False Alarms

It’s possible to become overwhelmed with
increases in false alarms as the volume
of data being processed increases. This
is especially true when comparing businesses
based on expected similarities. Any
number of things can change a business’s
consumption pattern such that it appears
anomalous – changes in ownership or
management, a shift in operating hours,
an incorrect business classification, multiple
meters under different
names, seasonal conditions,
closures for remodeling, even
the simple act of upgrading
equipment. The key to
successfully identifying
anomalous consumption is to
filter out these false alarms
through a process of screening
and analysis.

Before a seemingly anomalous
account is identified for
investigation, it gets reviewed
and verified for accuracy and
any potential changes in how
business is conducted. This
process tends to catch most of the false
alarms and eliminates false cases from
investigation. Furthermore, it corrects the
account’s peer grouping to ensure integrity
in other comparisons.

This process is illustrated in Figure 4.
The confidence factor on the right side
indicates the likelihood of a theft being
discovered. The more thorough the
screening and analysis, the higher the
likelihood of success in the field.

Other Sources of Data

Another significant data source is to look
at other services delivered. Whether
delivered by one utility or by separate
utilities, multiple service data can add
tremendous value to the analysis process.
A great example would be a Laundromat.
A Laundromat typically has gas dryers
and electric washers. If we know the
usage of either gas, electric or water, then
consumption projections can be made for
the other services too. It is very safe to
extrapolate this way because most Laundromat
customers will use an amount of
water and electricity in direct proportion
to the amount of gas used for drying. If
one of the services is not metered properly,
it will show up as anomalous based
on its ratio to the other metered services.
Even if all services had been tampered
with, the ratio of stolen service is not
likely to be consistent, so it would still
appear as anomalous usage.

These same energy use ratios can
be developed and modeled for almost
all business types, whether they’re
restaurants, hotels, service stations, public
facilities, etc. Energy use ratios have
consistently identified significant theft
cases that date back years and would otherwise
have remained undetected.

Summary

Theft is not going away and is only likely
to increase in the coming years. That’s the
bad news. The good news is that there are
effective tools for identifying anomalous
usage patterns. Data gathered in different
service areas can be used to build
consumer models and peer groups. By
integrating data from AMR tamper flags
with those models, valid theft cases can be
identified more accurately.

Most industries have experienced
significant transformations over
the past few decades, which have
resulted in improved business processes
and a more efficient operating structure.
These transformations, to a large extent,
have been facilitated by applying appropriate
technology improvements, in concert
with changed business processes.

The utility industry is just starting to
take advantage of these transformational
opportunities, which are, in turn, affected
by several significant operational challenges,
including:

• An aging transmission & distribution
  (T&D) workforce, coupled with
  fewer workers entering the field
  and increased training costs and
  requirements;
• An aging T&D physical infrastructure,
  making it more challenging to manage
  the distribution network in a safe and
  efficient manner;
• Increased pressure to provide a reliable
  distribution network in support of
  today’s digital economy; and
• Increased demand for products and
  services that allow customers to
  manage their energy use more efficiently,
  thus reducing future power
  requirements.

Solution building blocks that are critical to
the development of the intelligent utility
network (IUN) include:

• Business and process models;
• Architecture design, including a common
  data model;
• Advanced analytics;
• Advanced communication network
  architectures, and embedded sensors
  and actuators;
• Additional technical infrastructure
  elements as required and
• Industry partnerships, like IBM/Cisco.

IBM’s focus in this effort is to put a sensor
fabric in place across the utility value
chain, from the data sources, to the
applications that use the data, and then
to business support and operations (see
Figure 1). This model provides a utility
with the flexibility and agility to respond
to ever-changing requirements and integrate
or upgrade new operational environments.
In other words, the intelligent
network unifies the utility’s equipment,
systems, customers and employees. It
enables on-demand access to information
about customers, assets and the T&D grid,
which is then used to continuously optimize
operations and planning.

Intelligent Utility Network Functions

The IUN enables companies to manage
operations across the entire enterprise,
rather than in individual business units or
departments. One overarching objective of
the IUN is the deployment of various sensors
along the grid to collect and analyze
a wide variety of data in order to automate
certain actions to the grid (such as reconfiguring
the grid automatically). While
there are many elements of the IUN, the
following form the core design:

• Feature 1: Enable Internet Protocol
  Communications
  The IUN is based on the conversion
  from analog to digital, providing greater
  quality and access to operating information.
  By enabling all devices on the
  network through IP (Internet protocol)
  communication, companies will be able
  to grow their networks quickly and take
  advantage of new innovations. Examples
  of these technologies include wireless,
  broadband over power line (BPL)
  and voice over IP (VoIP).
• Feature 2: Open Technologies, Open
  Standards, Consistent Architecture
  With the IUN, the complexity of using
  devices is decreased by adoption of

  industry standards. Internal users open
  devices with technologies already familiar
  to them, thus lowering the cost of
  acquiring new talent for development
  and maintenance. A common architecture
  drives the implementation, incorporating
  industry standard data models,
  open technology communications, and
  adoption by business partners.

• Feature 3: Enable/Support New
  Business Opportunities
  As new business models are created,
  energy companies need to explore new
  sources of revenue. By building flexibility
  into their network design, businesses
  will be able to expand quickly
  and accommodate new acquisitions or
  divestures. Examples sweeping the utility
  industry include IP telephony, broadband
  Internet access and security-monitoring
  services.
• Feature 4: Consolidation Through
  Public and Private Networks
  To realize the promise of convergence,
  utilities need a clear strategy for moving
  from a “disconnected” business to
  one that has key processes integrated
  through collaborative portals. Today the
  convergence of voice, video and data is
  set to transform business relationships
  and collaborative strategies forever.
• Feature 5: Security Based on
  Data/Applications
  Regardless of the current or future
  infrastructure, information access
  and security is critical to energy
  and utility companies. By migrating
  to the IUN, companies can leverage
  the latest technology advances for
  securing people and devices throughout
  the network.
• Feature 6: Business Resilience of
  Critical Infrastructure
  Today’s dynamic business climate
  mandates a shift from the traditional,
  reactive disaster recovery methods
  to a more process-oriented, proactive
  approach to information availability and
  business protection.

IUNs in Utilities

In the utility industry the device, network
and data domains reach throughout many
established networks, including corporate
enterprise wide area network (WAN) and
local area network (LAN), land mobile
radio, public cellular, supervisory control
and data acquisition (SCADA) and microwave.
To put it in historical context, these
networks enabled discrete capabilities
prior to the existence of many common
carrier services. Within utilities’ operating
environments, land mobile radio predates
cellular services; and microwave existed
prior to T-1, frame relay and Internet service
providers. When the current utility
networks are viewed holistically, in present-
day terms with present-day capabilities,
significant opportunities for reduced
costs and enhanced capabilities emerge.

At the physical infrastructure level,
the IUN’s value is in facilitating the
transition to a common IP-based environment
in which each element of the network
is assessed and optimized according
to the best, lowest-cost IP service available.
The IUN is not a panacea of perfect
solutions but rather a target that establishes
connections from the data source
to the decision maker and back. This
provides the functionality for tools like
end-to-end security, resource management,
data mining, common business
views, and adaptive computing and telecommunications.

Building the Right Foundation

The IUN infrastructure connects and
integrates information from substations,
IEDs/assets and smart meters via an
IP-enabled network. The network infrastructure
is typically divided into areas
based on physical, logical or wireless connectivity.
The basic building block of any
network is the LAN, which can be as small
as a switch/router in a one-room building
or large enough to support multiple buildings
in a campus environment with several
switches and/or routers. Historically LANs
were based on layer 2 of the open systems
interconnection model. As more network
devices and applications began to support
IP, LANs migrated to layer 3. Although
routers can be found in LANs, the predominant
devices are layer 2 or layer 2/3
switches. The LAN will normally provide
the highest speed to the end user or
device, which is 100 megabits per second
(Mbps) to 10 gigabits per second (Gbps).

A WAN spans multiple cities, states
or countries and is used to connect the
LANs. Like LANs, WANs have also been
migrating from a layer-2 environment to a
layer-3 network that supports IP. Although
the bandwidth of the WAN can be up to 10
Gbps, it normally supports multiple locations
simultaneously.

The metropolitan area network (MAN) is
typically between a LAN and WAN in size.
It may span a city, county, state or several
states, depending on the requirements.
MANs usually take advantage of optical
technologies like synchronous optical network
technology or dense wavelength division
multiplexing; some MANs may even
use Ethernet via fiber optic cables or BPL.
The bandwidth of the MAN can be up to 10
Gbps, while supporting multiple locations.

In most cases, LANs, MANs and WANs
are created by connecting network
devices physically. Although microwave
and satellite communications have been
around for a long time, newer and less
expensive wireless technologies, like wireless
fidelity (WiFi), have become increasingly
popular. This wireless technology
includes IEEE 802.11a, b and g and supports
speeds up to 54 Mbps. In addition,
these frequencies do not require a license.
For those applications that do not require
higher speeds and when it is not costeffective
to pull cable or lay fiber, wireless
is a very good alternative. Keep in mind
that wireless is not meant to replace the
traditional physical connectivity of a LAN,
MAN or WAN, but is designed to augment
and expand the existing infrastructure, as
well as provide additional options. Wireless
also provides additional options such
as ubiquitous access, two-way communications,
data capture and zero-touch service.

In the past, there were separate communications
infrastructures for voice,
call centers, video conferencing, video
surveillance, security, SCADA and building
management systems for air-conditioning,
heat, ventilation and fire. Substations are
a good example of having separate infrastructures
with analog lines for phones,
RS-232 connectivity for SCADA (or remote
terminal units), coaxial cables for video
surveillance, fiber for LAN connectivity
and card systems for physical security.
More and more of these systems are being
converged onto one network infrastructure
in an effort to reduce implementation
and support costs, while driving significant
increases in productivity by providing better-
quality information more quickly.

Security is a common requirement in all
the areas and technologies. Although most
companies are 75 to 95 percent vulnerable
to external sources via the Internet, these
same companies are almost 100 percent
vulnerable to internal exploitation. This
vulnerability comes from the inability to
control the desktop – the fact that antivirus
software is based on signatures and
cannot address zero-day attacks, physical
connectivity to the internal network that
isn’t controlled and vendors or contractors
with non-company PCs who can and do
connect to the internal network. Nowadays
firewalls are being deployed internally
throughout the company to provide
security zones. One of these important
zones would be in front of the data centers
– and even within data centers – to protect
critical applications and data. Encryption
is another security technology that
used to be deployed across the Internet
exclusively but is now being used within
companies to provide an additional level of
security and data protection.

Leveraging the Foundation: The IUN in Action

Truck of the Future
Another way to leverage the IUN for
increased efficiency and productivity is to
extend it to utility trucks. These high-tech
vehicles are sometimes called the “Truck
of the Future.” Obviously, the trucks use
wireless technologies to send and receive
information instantly. Work orders and
tickets are opened, updated and closed in
real time, regardless of the truck’s location,
eliminating the need for truck crews
to return to the office for printouts.

One wireless technology used to enable
the Truck of the Future is the Cisco wireless
mesh network, which can span a city
or metropolitan area (see Figure 2). The
truck thus becomes a rolling wireless hot
spot that is never out of touch. This also
allows the truck to “heal” a part of the
wireless network if it goes down. When the
truck is dispatched to a work site where
part of the wireless network is down, the
truck itself (with its built-in wireless capabilities)
can bridge the wireless network
until the crew completes its work.

Cisco’s wireless technology provides
RFID capabilities, allowing tracking of the
truck, personnel and assets. With RFID,
assets like special tools can be identified/
located anywhere in the city or metropolitan
area to minimize loss or duplication.

Like their wired counterparts, wireless
networks are also being converged. The
general trend is to integrate voice, video
and data into unified communications
(UC). This allows centralized experts to see
equipment problems truck crews encounter
at the site and provide real-time advice.
By extending collaboration applications to
the trucks, utilities save time, money, and,
in some cases, lives.

Automating the Work-Order Process
In addition to receiving work orders in real
time, it is also possible to automate the
entire work-order process. Mobile workers
can use voice recognition software, realtime
pictures and video clips when they
submit or escalate the work-order process,
receive approval for repairs, order
parts or request additional resources.
With these capabilities, repairs are performed
more quickly and efficiently.

Securing Distant Assets
Another way to leverage the IUN, and
another benefit of UC, is the ability to
provide security via video surveillance. In
many cases, the video is captured via wireless
surveillance cameras and integrated
into enterprise security software systems.
This allows energy and utility companies
to provide enhanced protection of key
assets (like substations) that are located
remotely (see Figure 3). It is also possible
to integrate card reader systems (e.g., for
magnetic door locks) and RFID into security systems to provide a more complete
view of security, personnel and assets.

While leveraging this same utility network,
it is also common to provide network
security with an integrated firewall, virtual
private network (VPN) and intrusion
prevention. VPNs can be created with IP
security (IPSec), secure socket layer (SSL)
or multiprotocol label switching (MPLS).
Although all three of these technologies
enable VPN via some form of tunneling,
only IPSec and SSL provide encryption. If
encryption is needed across an MPLS environment,
IPSec is typically added.

Wireless Smart Meters
Another way to leverage the IUN is to
extend it to electric, gas and water meters,
as shown in Figure 4. Wireless meter reading
has existed for many years, but technology
has introduced some valuable new
capabilities, such as two-way communications,
continuous connectivity for short
interval (15-minute) reads, outage event
notification, remote connect/disconnect
and real-time price signals to customers.
Automated meter reading also improves
accuracy; reduces head count and turnover
rate; and delivers overall cost savings
and increased efficiency and productivity.

Summary

The intelligence of a utility network hinges
on the quality of its information and its
ability to access and integrate that information
anywhere, anytime. It must be built
upon the right infrastructure, along with
effective enterprise integration and business
applications for maintaining intelligence
both on the grid and among the
service functions designed to support it.
The benefits of establishing such a
network are manifold. By unifying a utility’s
equipment, systems, customers and
employees, the IUN enables on demand
access to information that the utility can
use to optimize operations and planning.
In the face of so many operational challenges
and an ever-changing environment,
the utility industry must apply
these types of technology solutions for
its ongoing success.

Additional content was contributed by
Ron Aberman and Jeffrey S. Katz, IBM.