IEEE Power & Energy Magazine - January/February 2016 - 61

the Cim extensions to electrical distribution and the Cim
extensible markup language (Xml) were created to facilitate information exchange related to ieee radial test feeders
across power distribution applications.
for most, if not all, system interactions involving information exchange or database access, the Cim approach to solving the problems described in this article is to be based on the
Cim semantic model and supporting standards. the scope of
this article includes systems for advanced distribution management, geographic information, meter data management, outage
management, work management, asset management, facilities
management, customer information, and many others. however,
the overall scope of the Cim also includes models for generation,
transmission, and markets, which are described in other articles in
this special issue of IEEE Power & Energy Magazine on the Cim.

The Unique Data Challenges
for the Electric Utility Industry
electric utilities have a unique operating environment in that
they must serve all willing customers, they are among the most
capital-intensive industries, and the product they produce is
consumed immediately yet the planning and pricing for that
product might have lead times greater than a year. against
this backdrop, utilities are also challenged with the same
rapidly evolving technology (increased computing power,
smaller footprints, increased connectivity, etc.) that affects
many other industries. in particular for utilities, as assets that
are deployed get "smarter," there is a greater amount of data
that needs to be managed. Data from renewable generation,
sensors, smart meters, and load management-and the need
to act on this data for internal and external use-all contribute to the issue. at some point, the sheer volume of the data
becomes a problem. for example, interval data from a few

million smart meters seemed so daunting-adding dozens of
terabytes to the storage requirements-that pundits labeled
the resulting flood of ones and zeros a "data tsunami." to
brace themselves against the data tsunami, utilities needed to
ramp up the capacity of their storage area networks.
no portion of the electric power grid is changing more
significantly and rapidly than the electric distribution system. the traditional philosophy of those who design and
manage distribution systems has been to maintain acceptable electrical conditions at the lowest possible cost for all
customers. recent distribution operating practice seeks to
improve the efficiency and reliability of the distribution system, accommodate a high penetration of distributed energy
resources, and maximize utilization of existing distribution
assets without compromising safety and established operating constraints. significant changes to the distribution
design and operating practices-often referred to as grid
modernization-are needed to accommodate these requirements. these changes are impacting utility systems and creating new sources of data, often at unprecedented volumes.
fortunately, the concern about sheer volume has been mitigated by the decreasing cost and increasing capacity of storage.
a terabyte of data was previously considered a spectacular volume but is now considered "volume as usual," and this capacity
curve for storage will continue its own moore's law-like trend.

The CIM for Analytics:
Realizing the Value in Data
now that data acquisition and storage are no longer challenging, the question of what to do with the data is the central
challenge of grid modernization. the value of data is realized
through the application of analytics across the various domains
shown in figure 2. as this figure suggests, asset management

Real-Time Simulation and Contingency Analysis
Distributed Generation and Alternative Energy Sources
Self-Healing Wide-Area Protection and Monitoring
Asset Management and Online Equipment Monitoring
Demand Response and Real-Time Pricing
Energy Market Participation

figure 2. Examples of some grid data sources and the challenges posed.
january/february 2016

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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2016