IEEE Spectrum April, 2017 - 55

But it would be easy to design a control system, based on
readily available IoT components, that could communicate
with similar systems in surrounding houses. These systems
would work together, for example, to turn air conditioners
on or off ahead of or behind schedule, or alter their thermostats by half a degree, to accommodate intermittent,
unexpected shortfalls in capacity. What would enable this
plan to work is the fact that most modern homes are well
insulated, so it takes time before the internal temperature
changes enough to trigger the HVAC system. The reason why
homes would be grouped together in this scheme is that it
would make the task easier: In the group, some homeowners
would be willing to sacrifice a lot of comfort, some less. But
the power needs of the group of houses would be relatively
predictable and manageable, from the utility's standpoint.
Most consumers do not want to make frequent and detailed
decisions on energy use. So imagine a device-let's call it an
energy thermostat-that permits you to set a range of comfortable temperatures, rather than entering a single one.
The wider you set the range, the less you'll pay for power.
The grid or microgrid operator would use the range-yours
and everybody else's-to dynamically match supply and
demand on a minute-by-minute basis. On a hot afternoon,
with demand at its peak, the temperature in your home
would be at the top of the range.
Electric utilities will also begin making greater and much
more effective use of big data. Utilities have been using data
since the very beginning: When Thomas Edison opened
the Pearl Street power station, in New York City in 1882, it
had indicator lights to show when the load had increased or
decreased enough to warrant adjustments to the dynamo
producing the DC power. But that system clearly was not
scalable. If a utility had to readjust its generators every
time a customer came online, the industry would have
died out long ago.
Having a large number of loads makes the aggregate
demand predictable-and manageable. This happy condition obviously depends on there being little correlation of
usage from house to house and business to business. But
just suppose that at 3:00 p.m. on a hot summer day, everyone in a medium-size city turned off their air conditioners at
the very same second, waited 15 minutes, and then turned
them all back on again at exactly the same time. That would
almost certainly cause a massive blackout.
With big-data tools, it may no longer be necessary to
depend on consumers' actions being only loosely similar.
It should be possible to understand how to adjust production
and consumption to enhance system behavior. For example,
with the energy-thermostat concept outlined above, the system operator needs to have not only the appropriate controllers but also access to real-time data to determine the risk
of system failure when load-management actions are taken.
Utilities in many areas have embarked on this path using
various customer incentives to permit, say, time-of-day pricing or some other form of load management by the utility
rather than by the consumer. But we are now taking just
baby steps. Big-data tools will soon let us take larger strides
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and may well one day let us run. It may be possible to use
real-time operational data to optimize the performance of
large sections of the grid and to predict future performance.
lthough my main goal is to describe a hopeful
vision that many of us in the utility business
have for the electric grid, I would be remiss if I
did not point out some of the challenges. These
include financial ones, regulatory ones, and technical ones.
And they come in all shapes and sizes.
One of the most fundamental is slow growth. To pay for
costly system upgrades, utilities in the past would have
relied heavily on growth in demand, and therefore sales.
But improvements in efficiency, which consumers seek (and
rightly so), have slowed growth in demand to the extent that
it is now increasing at a rate lower than that of the growth
in gross domestic product. And the figures are sobering:
In 2014, the U.S. DOE predicted that in the period from
2012 to 2040, the demand for electricity will grow by only
0.9 percent per year. So, utilities cannot expect to fund the
required system changes in the same ways as they have in
the past, through growth.
Other shifts in the industry will only exacerbate these
money woes. For example, in the past utilities could count on
key pieces of equipment lasting a long time. But smart grids
depend on electronic components, such as smart meters,
controlled by software, which have shorter lifetimes and
require much more frequent upgrades.
The biggest unknown is how swiftly the regulatory process
can adapt. If it can't move quickly enough to keep up with
the technology, expect agonizingly slow change. And what
if governments try to prop up outmoded technologies with
subsidies? That could drag out the process further. On the
other hand, some would argue that regulators should slow
the rate of change. Though the arguments for that are worthy of political discussion, I'm certainly not in that camp.
Historically, regulations have been driven mainly by legal
and economic considerations rather than by technical ones.
But now, with the pace of technology outrunning other factors, regulators in the United States and Europe are reacting to this new state of affairs in many different ways. My
view is that the staffing of regulatory agencies will need to
become more technically savvy if we are to navigate these
turbulent waters while continuing to provide electric power
with the lowest cost and highest reliability.
I'm confident that in the end, we'll have electrical grids
that are less costly, more sustainable, and more user friendly
than the ones that came before. The United States' National
Academy of Engineering recently selected electrification as
the top engineering accomplishment of the 20th century.
But electrification now needs to be reengineered to meet
the needs and opportunities of the 21st century. This is our
chance to show that we are as good as our forebears of two,
three, or four generations ago at technology, regulation,
public policy, finance, and the management of change in
general. And to leave to posterity a legacy as fine and enduring as the one that was left to us. n

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Table of Contents for the Digital Edition of IEEE Spectrum April, 2017