IEEE Power & Energy Magazine - May/June 2018 - 104

in my view (continued from p. 108)
become new avenues of innovations and
business opportunities. Examples include
home-energy efficiency advising, peak-
time rebate, or even transportation elec-
trification, just to name a few. All these
would require the third pillar of the future
power industry, the DT-savvy experts.
Just as data are transforming the way
people conduct retail businesses, hotel
businesses, and many other businesses,
data are likely to have a major impact
on the modernization of the electric grid.
In addition to technical issues, there are
policy barriers that regulate access to and
the use of data. Unless there is a clear
policy framework that lays out what is
accessible, sharable, and securable, it
would be difficult for technologies to
succeed by themselves. The industry
needs a clear regulatory framework to
address the seams issue across organiza-
tional borders and define the ownership
and usage of data in different instances.
Another key issue is how to keep the
data closely coupled with the physical

need to be carefully engineered for such
changes in the generation portfolio.
The second major driver is the dis-
tribution grid's increasing level of in-
novation and utilization when compared
to the wholesale-level transmission
grid. For example, in the European
Union, more than 90% of the newly
installed solar photovoltaics was inte-
grated at the lower voltage distribution
systems in 2016. This prompts funda-
mental questions for utilities of the fu-
ture. What would be the new business
model that would align various stake-
holders' interests under the new environ-
ment? How should the utility companies
redefine their business models so that
their stakeholders (including utility
customers, distributed resources provid-
ers, infrastructure owners, system op-
erators, and vendors) can be properly
incentivized to improve social welfare?
In this quest, data would likely play an
important role. Many value-added servic-
es from utility companies could very well

models. Figure 1 shows the possible
vision of integrating data with underly-
ing physical models. It would not be
wise to throw away all of the wonderful
knowledge that engineers and research-
ers have accumulated over decades and
start a brand new data-only approach
to power and energy systems. Instead,
such models and physical principles of-
fer tremendous insights to guide the
learning aspects of data-enabled dis-
covery. In terms of practical implication,
it would be important to place data scien-
tists and power engineers side by side so
they can communicate with each other
and appreciate each other's value and in-
sights. On the research front, it would be
extremely fruitful to combine machine
learning and data science tools, with a
deep integration of the dynamic physi-
cal model at various timescales.
The third issue is data availabil-
ity and accessibility. While not every
piece of data is sharable in the public
domain, there are many venues that

PMU Data
SCADA Data
AMI Data

Milliseconds

Physics

Seconds

Transient Machine
Dynamics and Interaction

Streaming
Data

Timescale

Minutes

Quasi-Static Power Flow

Steady-State Optimal Power
Flow with Ramp Constraints

Analytics
Milliseconds Seconds

* Anomaly Detection
* Etc.

Minutes

* State Estimation
* Predictive Voltage Control
* Etc.

Timescale

* Phase Detection
* Market Bidding

figure 1. The integration of data and physics-based models in power system operations at various timescales.
104

ieee power & energy magazine

may/june 2018



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
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