METER DATA
DELIVERING INFORMATIONENABLED ENERGY: THE NEW
UTILITY MANDATE
By Meir Shargal
Not long ago, I heard a utility-savvy friend grousing about her
electricity provider. She wasn’t upset about the rates or the
service reliability. She was upset about postcards. “Why does
the utility keep sending me postcards and letters about their air
conditioner load control program?” she asked. “They send me
four or five of these marketing pieces every year. Can’t they tell
by my summer bill that I don’t have an air conditioner? What a
waste of their advertising budget!”
My friend assumed her utility understood that she didn’t have an
air conditioner because of the rates she was being charged. At her
utility, a two-tiered rate structure goes into effect from June 1 to
September 30. For residential customers who use less than
500 kWh, summer rates are the same as winter rates. For usage
over 500 kWh, the rate essentially doubles. My friend asked why
the utility marketing team didn’t simply send the promotion to
those who got hit with the higher rates, as those were the people
most likely to have air conditioning.
That’s a good question. Why don’t utilities use metering, billing
and other demographic data to target likely prospects for their
energy efficiency programs? In this case, the customer didn’t
have a smart meter, but since monthly consumption tripped the
rate increase, the utility didn’t need smart meters to make its
marketing smarter.
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What the utility needed was business analytics. The data were
there, but my friend’s utility wasn’t using it to guide marketing
decisions.
This story illustrates the kind of wasted opportunities utilities will
face if they don’t employ business analytics to leverage all the data
that will come from the smart grid. Now is the time for utilities to do
more than merely collect such data. They must improve their data
infrastructure to be able to process all the incoming information
or transactions. In this effort, utilities should employ an integrated
smart grid data framework that delivers actionable insight from
systems and processes.
The figure illustrates what one such data framework might
encompass. In the top half of the image, you see data’s lifecycle,
which begins with smart meters, sensors and other devices that
create the data, then moves through collection and transport
phases and on to systems that deliver enhanced intelligence. At
the same time, the data lifecycle must be managed, people must
be trained to use data effectively, and processes must change to
reflect the efficiency that the data enables. Once all these issues
have been addressed, utilities will begin to see results in the form of
distribution automation, asset optimization and more.
On the bottom half the figure, you see the building blocks of
the data framework.
Infrastructure,
administration and data
management underlay all
the processes and utility
changes shown in the
top half of the graphic.
Security is something
that must be considered
throughout a smart grid
initiative. Other elements of
the data framework come
into play only at certain
times during the data life
cycle. Event management,
for instance, is something
dealt with in the collection
and movement phases
of the life cycle. Models
and metrics come into
play as you transform the
data into insight, manage
it and incorporate it into
processes. Analytics begin
at the end of the data life
cycle. They deliver the
results that will transform
the utility into a truly smart
organization.
METERING INTERNATIONAL ISSUE - 3 | 2012
METER DATA
Doing this takes more than hardware and software. It will take a
shift in mindset, as well. In the end, information-based decision
making must become a core part of a utility’s corporate culture and
DNA.
CONVERGING FORCES
Now, more than ever, utilities need to run efficiently. It’s well known
that some 30% of utility workers in North America, Europe and
other locations are likely to retire in the next five years, leaving
utilities with a potential shortage of skilled staff. Likewise, carbon
concerns are bringing new regulatory mandates, such as various
renewable generation requirements in the US and the 20/20/20
energy policy adopted by the European Union. It calls for a 20%
reduction in greenhouse gas emissions, a 20% reduction in energy
use, and 20% of generation to come from renewable energy by the
year 2020.
Then, too, there’s the pressure to maintain reliability. In an
independent study sponsored by the Electric Power Research
Institute (EPRI), it was estimated that power outages and related
power disturbance are costing the US economy billions of dollars
annually. The study, which was released in July 2011, examined
roughly 2 million businesses and concluded that $45.7 billion is
lost annually to outages, while another $6.7 billion is lost to power
quality disturbances. Yet, aging infrastructure in the US is proving
to be increasingly vulnerable to failure. Massoud Amin, a professor
of electrical and computer engineering at the University of
Minnesota, found that the number of non-disaster-related outages
affecting at least 50,000 consumers grew 124% when you compare
numbers from 1991 to 1995 against the numbers from 2001 and
2005. Specifically, there were 41 such blackouts in the earlier period
and 92 in the latter. In 2006 alone, utilities reported 36 outages of
this magnitude.
AN OUNCE OF PREVENTION
Use of analytics is one way utilities can meet all of the challenges
noted above more effectively. On the grid side of the utility
business, smart grid sensors and communications technologies
promise to make predictive maintenance not only possible, but
probable.
Traditionally, utilities might have had periodic inspection
processes in place to check for gross level weaknesses but, in
general, they ran distribution assets until failure occurred. Now,
utilities are putting sensors in to measure and capture a variety of
metrics, then using the insights gained to implement proactive
maintenance.
Among factors measured are loading and its suitability for an
asset’s load ratings, oil temperatures and exhaust gasses, asset
acoustics – or the noises the asset makes – and vibrations. For
switches, utilities are monitoring cycles, because the more times
you use a switch, the more opportunities this mechanical device
has to fail. Some utilities are starting to look at the types of trees
around distribution lines to better predict storm-related failures.
Pine trees are more likely to fall over in a wind storm than oak
trees.
How much can predictive maintenance save? When Arizona
Public Service (APS) implemented a transformer oil analysis and
notification system at substations to monitor more than 100
critical transformers (230 kV and above), the utility was able to
predict and prevent failures far more effectively than it could with
other methods. The system uses sensors to monitor the dissolved
gas of insulating oil and thereby determine asset health. Other
monitoring methods have an effectiveness rate in the 23% to 43%
range. The APS system automates both monitoring and analysis
METER DATA
and operates in near real time, leading to a 93% accuracy rate in
failure prediction. This means maintenance can be done without
overtime and the lights can stay on for APS customers.
Analytics also can deliver strategic insights to improve grid
operations. Along with predictive maintenance analytics facilitates
integration of renewable generation into the distribution grid
without affecting reliability, as well as volt/var control that affects
power quality. And, at the end of the day, analytics will improve
grid financial planning through better forecasting because it
enables engineers to build predictive models for demand planning;
plus analytics will help utilities extend distribution asset life and
defer investments.
ENHANCING SERVICE
Speaking of customers, many utilities are applying analytics to their
grid operations, but a smaller number are using smart grid data on
the customer side of the house. Here, too, is wasted opportunity.
Consider the operation and marketing of energy efficiency
programs and rates designed for demand management. For such
programs to work, you need to have the right program, the right
products and they need to be offered to the right people. Analytics
will tell utility managers if they’ve managed to get all those factors
right.
One East Coast utility applied analytics to its energy efficiency
programs and discovered that it takes two months from the time
a customer first becomes a lead to the point where that same
customer completed an energy efficiency project. This insight,
combined with analytics that tracked progress against goals,
allowed utility managers to see if they were on target to meet
financial and regulatory commitments. If not, the managers knew
about deficiencies in time to make changes to their programs and
marketing.
Analytics will play a part in new service offerings, too. Ahead,
consumers may start buying smart outlets that let them monitor
energy consumption of household appliances. Such energy
savers become service opportunities for utilities when the IT team
applies analytics. For example, by applying algorithms to look at
appliance efficiency, a utility could offer alerts when load curves
for a given appliance veer from normal parameters. If we see
consumption going up from a fridge, it’s probably an indication that
the compressor isn’t working or the seals on the door are starting
to fail. The utility can give this maintenance information back to
consumers. A service like that would help customers save money
and, most likely, boost customer satisfaction scores.
THE DATA DRIVEN CORPORATE CULTURE
A study conducted by researchers at the Massachusetts Institute
of Technology’s Sloan School of Management examined 179 large,
publicly traded companies and concluded that firms using data
driven decision making “have output and productivity that is
5% to 6% higher than what would be expected given their other
investments and information technology usage.” The researchers
spotlighted their investigation in a paper titled, “Strength in
Numbers: How Does Data Driven Decision Making Affect Firm
Performance?” There, they also cited another study, which found
that organizations using analytics to guide business decisions and
operations are two times more likely to be top performers than
other companies.
Why aren’t utilities pursing business analytics more actively?
Chances are, it’s a people problem. In a May 2011 paper by
the McKinsey Global Institute, “Big data: The next frontier for
innovation, competition, and productivity”, analysts estimated
that organizations will need to fill an additional 140,000 to 190,000
analytical talent positions and 1.5 million positions for data savvy
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CLASSIC CATEGORIES OF ANALYTICS
Descriptive analytics: What has happened or is happening.
• Technologies and processes that use data to understand and
analyze operational data or business performance.
Predictive analytics: Why it happened and what might
happen in future.
• Extensive use of data and mathematical models to uncover
explanatory and predictive models of business performance.
Analyze historical data and outcomes to explain why it
happened and try to predict what might happen.
Prescriptive analytics: Which prediction or outcome is best?
• Mathematical modeling techniques that determine a set of
high value alternative actions or decisions given a complex
set of objectives, requirements and constraints, with the goal
of improving business performance.
managers in the US alone to take full advantage of the avalanche of
data now gaining momentum.
Several studies have shown that change management – or
the lack of it – can make or break an IT project. For example,
investigators at Prosci, an independent research firm focused
on change management, found that 95% of organizations with
excellent change management programs in place were able to
meet or exceed project objectives. Among organizations with
poor change management programs, only 16% met or exceeded
project objectives. In a recent survey, SAS queried more than
200 utility executives and found that nearly 60% of respondents
named change management as the second biggest barrier to
implementing analytics software. A third of survey respondents said
organizational silos were an issue in their utilities.
A truly intelligent utility will apply information technology to
energy and service processes to maximize reliability, affordability
and sustainability. To do this, utilities must make quantitative
analytical capabilities part of the organization’s culture. Everyone,
from executives to engineers, must see analytics capabilities as
a core competency and competitive differentiator. And, access
to information will need to cross functional barriers, something
utilities have not yet mastered. Analytics is the lifeblood that will
fuel business success.
The time has come for utilities to take that tsunami of data coming
from the proliferation of intelligent devices on the electrical grid
and share it across functional areas so that the right people get
the right insights and intelligence when they need it. Until that
happens, utilities will be challenged in the effort to drive business
value by effectively leveraging information. Still, I have confidence
that utilities can make the necessary changes. And, when they do,
maybe my friend will quit receiving postcards that try to sign her up
for load control on an air-conditioner she does not own. MI
ABOUT THE AUTHOR
Meir Shargal is part of the leadership team of CSC Global Energy and
Utility vertical leading the strategy, “smart” utility, generation and
transmission and distribution practices, with a focus on sales and
delivery of strategy, business transformation and enterprise solution
architecture. He is a multifaceted strategist and technologist with more
than 22 years experience who combines innovation with pragmatism,
and vision with execution and a sense of urgency.
mshargal@csc.com
ABOUT THE COMPANY
CSC’s Global Utilities Division provides gas, electric and water concerns
with utility experience and technical expertise to address today’s
challenges including exponential data growth, supply chain risks,
cybersecurity, aging workforce and infrastructure, public safety and
privacy, regulatory compliance and profitability. Serving the utilities
industry for 25 years, CSC clients include 60% of the utilities in the Dow
Jones Utility Index.
www.csc.com/utilities
METERING INTERNATIONAL ISSUE - 3 | 2012