IEEE Power & Energy Magazine - May/June 2018 - 108

in my view

Le Xie

data technology
the new normal

I

I WAS SITTING AT THE FIrST IEEE
Workshop on Utility Big Data, held in
San Antonio in September 2017. It was
right after the historical Hurricane Har-
vey had devastated Texas. Valentine
Emesih, CenterPoint Energy's Divi-
sion vice president of grid and market
operations, talked about painting the
layer of real-time outage data in geo-
graphic information systems (GISs)
to make customers aware how power
restoration was progressing. As bad as
the hurricane was, big data offered a
glimpse of optimism and hope for the
postemergency recovery. I could not
help but think about the potential of big
data and analytics in transforming the
power industry.
Today, there are two major catego-
ries of experts in utilities and system
operators: the IT (information tech-
nology) folks and the OT (operational
technology) folks. While listening to
the talks, I kept thinking that, in the
future, the third pillar of the power in-
dustry would very likely be experts in
"DT" (data and analytics technology)
as the new normal.
Given the critical nature of the electric
power infrastructure, state-of-the-art
technologies in sensing, communica-
tion, and computation have been always
tightly coupled with the development of
the grid. In the past decade or so, thanks
to the smart grid initiatives around the
world, billions of dollars' worth of in-
Digital Object Identifier 10.1109/MPE.2018.2793479
Date of publication: 18 April 2018

108

ieee power & energy magazine

formation and communication tech-
nologies have been deployed. They
generate large amount of data in vari-
ous timescales.
The industry has started to take ad-
vantage of these emerging data by ad-
dressing many problems through a
data-driven approach. In addition to the
visualization of data using GISs, there
are many other applications. For exam-
ple, how can smart-meter data be used
to better calibrate nontechnical losses?
How can distribution system models
and measurements be used to calibrate
parameters for equipment such as trans-
ducers? How could one consolidate
many events in an integrated data mod-
eling framework? How can one model
and trace electrical changes at the end-
user level while still keeping the com-
plexity of the model relatively easy to
handle at the transmission level? Addi-
tional use cases include storm damage
prediction, high-risk feeder evaluation,
and many others. As a power engineer,
this opens up so many new opportuni-
ties to solve big problems with immense
societal impact.
From a research point of view, the in-
tegration of data from massively de-
ployed sensors with underlying physical
models, cybercalculations, and human-
based operations provides a major op-
portunity for the power system analysis
community. At the core of this unprec-
edented architectural change lies the
scientific challenge of modeling and
operating the increasingly complex elec-
tric power system. The technical perfor-

mance and economic value proposition
of distributed resources could be aligned
in restructured electricity markets. State-
of-the-art data science can be integrated
with real-time dynamic power systems
and offer streaming analytics for the op-
eration and planning of the underlying
power grid. Streaming synchrophasor
data can be used for the online monitor-
ing of system anomalies and even pro-
vide classifications of root cause system
events for predictive and preventive con-
trol means. Similarly, at a different tim-
escale, the behavior of consumers subject
to real-time prices can be collected and
analyzed through a dynamic systems
lens. A recent study suggests that the
behavior could be quite nonlinear with
respect to price, due to the recollection of
previous price patterns.
The value of the data is likely to pro-
vide the power industry with a renewed
paradigm that leads to a renaissance
of its business models. Over the past
decade, the power industry has been
undergoing fundamental paradigm shifts
due to a number of regulatory and eco-
nomic drivers. The first major driver
is the drastic change of the generation
portfolio. Some 200 GW of renewable
variable energy capacity contributes
about 20% of the entire generation
capacity in the United States, while
16 GW of coal generation was retired
in 2015 alone, with more retirements
to come in the coming decades. The
reliability and security of the grid will
(continued on p. 104)
may/june 2018



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IEEE Power & Energy Magazine - May/June 2018 - Cover3
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