IoT in Utilities: A Look at Future Applications
Tremendous value can be unlocked from data to plan for infrastructure investments or required skill sets, information that is greatly informed by what happened in the past, Schnugg said.
“For a long time, our utilities have done a great job being able to manage this given the depth of experience of individuals in the workforce,” he said. “But as it ages out, we’re hoping to help codify that experience into software.”
Strother also sees workforce challenges with IoT in utilities and the trend toward digitalization. “It’s tough,” he said. “People studying AI and machine learning, they aren’t rushing to apply their skills at the utilities. You’re competing with Google and Facebook and Amazon and doing robotics.”
More Data, More Errors
Paradoxically, as the utilities generate more and more information, less of it can be trusted, Schnugg said.
“What if a model could be created to run through a series of data checks automatically, and not only surface the inaccuracies, but plug those back in?” he said, adding that GE’s new analytics tool can help reduce uncertainty in network data.
The goal is to reach a point where people don’t have to check through data themselves, or file a ticket for analytics, or literally drive out to see if a sensor is on a particular power line, he said.
Hopefully, he added, at some point in the future, every utility will be able to recreate any event in its grid from different perspectives because data has been appropriately stored and modeled, can be efficiently recalled, and the analysis can be immediately plugged into operation afterward. He also sees a day when data will be used en masse to predict future outcomes.
Currently complicating the ability to synthesize data, Strother said, is the growth in renewables and resulting two-way flow needed when customers with solar PV and wind power return energy to the grid.
“The business model and economics are changing,” he said. “It’s getting more complex. The challenge is how to figure out all the above and be good stewards of the environment.”
To deal with that complexity, Schnugg said he sees increased engagement among utilities, national power researchers and think tanks. He mentioned GE’s R&D partnership with Pacific Northwest National Laboratories.
The lab, under the Department of Energy, studies among other things smart grids that enable two-way transactions of power, machine learning and analysis tools, and the sharing of cyberthreat information among utilities.
“Data will continue to converge,” Schnugg said. “You have to very careful. When you create the most interesting data set on the planet, it’s interesting for the bad guys as well.”
As cyberthreats between the United States, Russia and Iran increasingly appear in the news, PNNL and its partners in the Cybersecurity Risk Information Sharing Program are using “high-performance compute and machine-learning to spot trouble in the vast sea of data generated by IoT devices and sensors,” according to the lab. “By quickly identifying trends and relationships that may reveal a potential threat, grid operators and automated protection systems can rapidly take action to protect the grid.”