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Private Broadband Opens the World of Big Data for Utilities

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This item is part of the Special Issue - 2020-12 - Data Analytics & Intelligence, click here for more

Collecting and analyzing data is not new to the utility industry—it has been doing that for decades on an array of devices, from customer meters to a broad range of distribution and transmission sensor and control devices.  But historically, those data sets have been restricted both in distribution and usability, rendered largely inaccessible by proprietary, narrowly focused applications and their single use networks carrying the data.  A smart meter or connected distribution transformer monitor for example, reliably records a variety of typical parameters including voltage, current, power, frequency, temperature, events, and alarms.  However, its application is usually for a narrow, and often proprietary, purpose or application, often only extracting a fraction of the possible value the data generated could provide if it were made accessible and even combined across application domains. Though there may be good security and privacy reasons for closely restricting sensitive data, the inability to fully capture, aggregate, analyze and then act upon a myriad of data streams has substantially delayed the utility industry’s ability to realize the vast benefits of modern data analytics, machine learning and artificial intelligence.  The answer may be in the introduction and integration of private broadband into the utility ecosystem.

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With two recent actions, the Federal Communications Commission has cleared the way for Utility Big Data.  First, in May the FCC realigned the 900 MHz band so it can be used for private broadband networks.  And then just this fall, the Commission held a successful auction of CBRS broadband spectrum in the 3.5 MHz band.  Utilities are pursuing deployment of private LTE networks in both bands to support their grid management systems. 

Modern grids are called “smart” grids because of the myriad devices that sense and create data reflecting grid conditions.  But just as reading doesn’t make us smarter unless our nervous systems carry the information to our brains for processing, the grid depends upon communications networks to carry real-time grid status data from the sensing devices to the industrial control system for analysis.  With a recognized standards-based, secure, private broadband network to provide that open and real-time communication, utilities can collect vast amounts of data from thousands of devices, and more importantly, can act on that data.

And today, with incredible advances in machine learning, artificial intelligence, hyper scale cloud services, and the advent of “data lakes” that allow the storage of both structured and unstructured data at scale, utilities not only can collect via broadband networks more grid data than ever before, but they can perform advanced analytics on it to improve the safety, security, and efficiency of their operations.

The need for collection, analysis, and action on grid data has never been greater.  The drive to reduce carbon emissions and the associated increase in distributed energy resources, electric vehicles, and microgrids are placing demands on the grid that it simply was not designed to accommodate.  Data analytics can help utilities manage the bi-directional, intermittent flow of energy that characterizes the modern grid, identifying and even forecasting issues before they become problems.  Similarly, consumer demands for greater information about, engagement in, and control over their energy usage are driving the development and deployment of new applications and analytical tools made possible by the torrents of data flowing over utility broadband networks from field sensors to analytic systems.

With hyperscale cloud providers like AWS and Microsoft offering storage and processing resources at virtually any scale, it doesn’t seem that utilities need to worry any longer about creating too much data.  These providers operate both public and private clouds in a distributed architecture, helping ensure not just the privacy and security of the utility’s data, but also the system’s ability to quickly receive the data, perform prompt analysis, and execute any resulting action in a timely manner, including near-real-time when required.

As operational data rush in from all over the grid, the utility’s cloud-based data analytics system can also consume business data from throughout the enterprise.  With full access to this data and modern analytic tools on top of vast storage and compute capacity, the utility can now integrate and analyze many data streams together, merging OT and IT to improve resiliency, efficiency, and customer operations.

The key to unlock this future is a standardized connectivity platform with private broadband.

Though utilities are starting to implement these remarkable capabilities today, perhaps the most impactful are just around the corner.  In the future, utilities will begin to aggregate their data with that of other utilities and other enterprises, and—taking care to ensure data privacy and security—they will perform analytics that reach beyond specific use cases and even beyond the individual utility enterprise.  With enough collaboration and sharing of data, utilities will perform meaningful analytics that provide value at the regional and even national level – the Network Effect.  The insights resulting from these efforts will drive future innovation and utility performance enhancement.  The future belongs to Big Data, and by deploying private broadband networks, our industry is positioned to ride that wave.

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Matt Chester's picture
Matt Chester on Dec 2, 2020

With hyperscale cloud providers like AWS and Microsoft offering storage and processing resources at virtually any scale, it doesn’t seem that utilities need to worry any longer about creating too much data.

While true, I think it's also important not to collect more data just because you can-- carefully analyze what data is being collected and what it might be used for, otherwise you could be crushed behind too much data with no real actionable plan for it. 

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