ORIGINAL REPORTING: When AI Will Turn On The Lights

How does AI improve grid performance? No one fully understands and that's limiting its use; "For 75% of what utilities are doing, outside of maybe forecasting or managing capacity, AI is at the infancy stage and there is no real use case."

Herman K. Trabish, Nov. 14, 2019 (Utility Dive)

Editor’s note: There are important things done in power system operations with data analytics and automated response, but they don’t rise to the level of real artificial intelligence and here’s why.

Just as power system operators are mastering data analytics to optimize hardware efficiencies, they are discovering how the complexities of artificial intelligence tools can do far more, and how to choose which to use.

With deployment of advanced metering infrastructure (AMI) and smart sensor-equipped hardware, system operators are capturing unprecedented levels of data. Cloud computing and massive computational capabilities are allowing data analytics to make these investments pay off for customers. But it may take machine learning (ML) and artificial intelligence (AI) to address new power grid complexities.

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AI is a form of computer science that would make power system management fully autonomous in real time, researchers and private sector providers of power system services told Utility Dive. ML is a part of AI that passes human-supervised data analytics through preset or learned rules about the system to inform AI of normal and abnormal operational conditions.

"Knowing when to use data analytics and when to use machine learning and AI are the fundamental questions utilities are asking," GE Digital VP for Data and Analytics Matt Schnugg told Utility Dive. Continuing to use an approach "that has been good enough for years" has merit, but new tools and capabilities may justify "turning to data scientists and cloud computing" and there are "parameters" for knowing how to choose between them.

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The sheer volume of data is beginning to exceed human capabilities, but system operators often don't have the technology to deploy demonstrated AI and ML solutions for power flow management, researchers told Utility Dive. The mathematics of solutions are not yet fully understood, they acknowledged. The next big question may be whether system operators will risk ML and AI for results humans cannot yet provide or understand.

The value of putting power system data to work is increasingly evident. It has saved system operators time and customers money. And it is providing predictive infrastructure maintenance, which can reduce the growing frequency and duration of service interruptions and help avoid major unintended cascading blackouts… click here for more

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