People can talk terabytes until they're blue in the face, but when you see a graph that shows the proverbial hockey stick growth in data heading skyward, you begin to realize the challenge of not just storing and managing that data but extracting value from it.
In an Energy Central webcast on Monday sponsored by vendor Black & Veatch, presenters flashed a graph (courtesy of the IntelliGrid effort at the Electric Power Research Institute) that showed how a sequence of application implementation will send annual data levels from about 200 terabytes to more than 1,000 terabytes quite rapidly. (A petabyte is 1,024 terabytes.)
(That sequence includes distribution automation, mobile workforce management, substation automation, upgrades to remote terminal units and outage management systems, distribution management and advanced metering infrastructure—all separate and apart from customer devices beyond the meter, which sends the incoming data curve skyward.)
That graph served to bring urgency to the topic at hand. The webcast then laid out two "case studies." The second one, on distribution system optimization and the role of analytics, was most pertinent to our readers.
(The webcast, "Demystifying Utility Analytics," sponsored by Black & Veatch (B&V), is available for replay by clicking on the title.)
The presentation leaned on the MIT Sloan Management Review series of articles titled "Big Data, Analytics and the Path from Insights to Value," that we featured in "Data Analytics for Smarter Grids," for strategic logic.
With the mantra that "emerging applications create many new opportunities, but complexity must be managed," Stephen Stolze, vice president, management consulting division, B&V, pointed out the many new systems on the grid and the devices and software that feed them data.
"Data analytics can take place at all different levels of detail," added Scott Stallard, vice president for asset management services at B&V Energy. "Some capabilities are extremely detailed, others are more conceptual and strategic. Linking all those together, thinking about interdependency, illustrates the opportunity for reuse of data to look at a wide range of problems."
One key theme: how leveraging historic, descriptive data ("what happened") can inform prescriptive analytics (choosing an option based on business intelligence).
The potential efficacy of analytics around day-to-day decisions on operating distribution system applications—the well-known acronyms such as OMS, GIS, MDMS, SCADA, etc., come to mind—is well-known, according to Eric Henlon, smart grid distribution consultant to B&V.
"But," Henlon said, "there's still a need for the analytics that drive not only operations and maintenance but also the deployment of these applications."
Advice: apply analytics in the planning and program management phase—at the beginning—and continue through your pilot projects. You'll find a richer solution because you've become familiar with the data outputs of various applications, Henlon said. Then tie analytics into operations and maintenance.
For example, how can analytics improve the deployment of substation automation? How can it improve operational productivity?
"Making use of the data that comes back and utilizing a platform that gives you a cohesive solution from all the data that's coming back can drive your decisions around optimized operations and maintenance . and monitoring reliability," Henlon said.
Among the benefits touted for substation automation analytics: improved asset management, improved operational productivity, deferred capital expense and optimized operations and maintenance.
Understanding the data output from volt/VAR optimization efforts and advanced metering infrastructure can help prioritize your capital investments to achieve a more strategic and operationally sound solution. It ties together reliability, efficiency and operational decisions.
"When you start thinking about wind, about solar, about energy storage, when do you use each asset?" Henlon asked, rhetorically. "Which asset do I address to improve efficiency? During a storm, how do I use distributed energy resources to increase reliability? Understanding the data, understanding how to apply analytics, will help you maintain your system in a more efficient manner."
The promise of analytics now being fairly pervasive, the question really turns to how to implement them? Answering that question effectively, according to the MIT paper cited above, is a major hurdle to adoption.
In sum, the steps seem to start with an initial understanding of the value of analytics for both strategic and tactical outcomes. Then an effort will require both perennial and new factors to succeed. Organizational commitment, led by executive sponsorship, is a time-honored factor. The integration of data analytics into operational and business processes is much newer and requires attention to change management. Budgetary logic and return-on-investment justifications must be hashed out.
The biggest challenge, perhaps, is the presenters' exhortation to "plan big, but seek early wins."
"It's not an easy task, it's a journey," Stolze said.
For more on that last point, you may want to read about the experience at Hydro One, which we detailed in "Hydro One: Data Analytics Requires Lead Time, Legwork."
Intelligent Utility Daily