IEEE Power & Energy Magazine - May/June 2018 - 14

the second article, "distribution synchrophasors," Mohsenian-rad et al. present a
series of real-world examples based on
the μPMU data and events collected from
several 12.47-kV test feeders in riverside, california. the authors start with
five examples that illustrate the applications of descriptive and predictive analytics on distribution faulty diagnosis and
grid monitoring. they then use two additional examples to motivate improved
applications of μPMU data and call for
scalable solutions.
Although there is broad support for a
low-carbon future, transitioning to a lowcarbon economy presents an enormous
challenge. the United Kingdom, for instance, is facing a diminishing subsidy for
renewable generation and shrinking network capacity. Both factors are constraining the acceleration of the transition. li et
al., in the third article, "Big data Analytics
for Flexible energy sharing," introduce two sharing schemes for exploiting the flexibility in an energy system:

1) shared energy and flexibility via peerto-peer energy trading in local energy
markets and 2) shared network access.
in such a flexible-sharing energy system, the users and market participants
require comprehensive information for
situational awareness; the prices have
to be established in real time; the flexibility in demand needs to be matched
with variable energy resources through
extensive data analysis. therefore, big
data analytics becomes a key enabler of
these sharing schemes.
in this decade, the power industry
has been busy with managing and analyzing data from new sources, such as
smart meters and PMUs. Meanwhile,
not too much attention has been paid to
weather data., even though the weather
is affecting many facets of the power
industry, such as electricity demand,
renewable generation, and distribution
system outages. in "Weather data for
energy Analytics," the fourth article,
Black et al. demonstrate novel uses of

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weather data for energy analytics via
two utility applications. the first pertains to distribution outage and reliability index modeling, where the authors
use some easily accessible weather data
collected from a local airport weather
station to develop a probabilistic view
of the distribution reliability trend. the
second application simulates behindthe-meter photovoltaic fleets, where
high-resolution, commercial-grade weather data are being used to develop realistic simulations.
china, one of the largest and fastest
growing countries in the world, is accelerating the implementation of big data
strategy. While many business sectors
have embarked on big data analytics,
the power industry is one of the frontrunners. chinese government agencies
and the private sector have devoted a
significant amount of funding to support big data analytics research and
development for the power industry. in
the fifth article, "Big data Analytics in

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