Designing a decentralized energy grid: Page 3 of 3

August 13, 2019 //By Nick Flaherty
NREL researchers Annabelle Pratt, Chin-Yao Chang, Bri-Mathias Hodge, and Benjamin Kroposki collaborate in the Power Systems Energy Center in the Energy Systems Integration Facility (ESIF) at NREL working on AEG .
Autonomous energy grid technology and machine learning aim to protect the grid from disruption and cyberattacks

AEG is also opening a Pandora's box of technical challenges. "The more we dig in, the more topics we find that need to be addressed," said Kroposki. 

Among them is scale. The team is currently simulating AEGs with a few hundred nodes on the high-performance computer housed at NREL's Energy Systems Integration Facility. But regions such as the Bay Area in California have more than 20 million control points. "Algorithm solve times are needed every one second. Trying to decide the fate of a million things on a second-by-second basis is where the challenge comes in," said King.

In the real world, power systems pose real problems. Communications are delayed, grid devices come from many vendors, and data isn't always available where it's needed. This is a special challenge for Bernstein and team, whose algorithms must be robust despite not-so-ideal conditions.

"Let's say we produce very nice algorithms," said Bernstein. "They still depend on physics—the topology of the lines and models of the devices. If you're in a building and you want to choose what to turn on or off, you need to have an accurate model of that building, which can be difficult to find."

To overcome peculiarities such as device models, Bernstein is using big data and tools from machine learning. "Sometimes, defining the model is harder than learning how to be optimal from data and measurements," he said. ‘Instead of building the models, we're using data to learn the optimal behavior directly.'

Still other conditions are limiting AEG; there are questions about how to arrange the communications infrastructure, and critically, how to secure that future infrastructure from cyberthreats. Such practical questions will be the focus as AEG takes a real-world form.

While Kroposki predicts a 10-year effort, there is already progress toward commercialization of AEG algorithms. Siemens is working with NREL to develop distributed control techniques with support from DOE's Solar Energy Technologies Office, and Eaton is drawing from the AEG effort for autonomous, electrified mobility solutions.

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