Autonomous energy grids project envisions ‘self-driving power system’

Autonomous energy grids project envisions ‘self-driving power system’
Technology News |
Autonomous energy grid technology and machine learning aim to protect the grid from disruption and cyberattacks
By Rich Pell


A team at the US National Renewable Energy Laboratory (NREL) is working on autonomous energy grid (AEG) technology to ensure the electricity grid of the future can manage a growing base of intelligent energy devices, variable renewable energy, and advanced controls.

“The future grid will be much more distributed too complex to control with today’s techniques and technologies,” said Benjamin Kroposki, director of NREL’s Power Systems Engineering Center. “We need a path to get there—to reach the potential of all these new technologies integrating into the power system.”

The AEG effort envisions a self-driving power system – a very “aware” network of technologies and distributed controls that work together to efficiently match bi-directional energy supply to energy demand. This is a hard pivot from today’s system, in which centralized control is used to manage one-way electricity flows to consumers along power lines that spoke out from central generators.

Instead, AEG grids are composed within one another, like a fractalized group of microgrids. Sections, or “cells” of AEG use pervasive communication and controllability to continually pursue their best operating conditions, which adjust to the temperament of customer demand, available generation, and pricing.

Decentralized control solves a few challenges as billions of new energy devices generating energy from variable resources are difficult to manage centrally. At the moment, the AEG is a highly theoretical framework for future energy systems to build from, with potential application 10 years out and a few early adopters currently trialing the technology. 

AEG follows from an Advanced Research Projects Agency-Energy (ARPA-E) project called Network Optimized Distributed Energy Systems (NODES) developing real-time optimization and control methods for power systems. “I would say for us, it all started with NODES,” said Andrey Bernstein, Senior Researcher and AEG Technical Lead . “In terms of algorithms and framework, NODES covers just one cell—one bounded community.

“Then Ben had this idea of having cells that communicate with each other to form a hierarchical system that could cover the entire grid. That’s how it went to the multicell perspective.”

With the launch of NODES in 2015, Bernstein and fellow NREL Researcher Emiliano Dall’Anese set their sights on new algorithms for a distributed grid. These algorithms use the limited computation of many customer devices such as inverters to functionally run the grid.

“Our main algorithms are coming from optimization and control theory,” said Bernstein. “If you go to the literature, there’s a gap between the two: Optimization finds solutions (but ignores real-world conditions) while control algorithms work to stabilize in not-ideal conditions. We’re bridging the two domains.”

“What’s novel in our solution is that we address a two-part problem,” said Kroposki. “First, because of the large number of devices, we cannot use central control, but must instead distribute the optimization problem. The other problem is that we have time-varying conditions, therefore the optimization is changing every second and must be solved in real time.”

Other challenges remain, such as identifying the complete set of inverter functions required to help stabilize the grid, as well as the necessary incentives.

At a theoretical level, AEG stitches these developments together—along with NREL expertise in control technology development, microgrid and distribution system controls, and cybersecurity—into a larger, more complete theory.

Like the grid that the team is optimizing, distributed “cells” of support are appearing for AEG. One of these domains is wind energy, in which an AEG-future also presumes an autonomous wind farm.

“It’s one of my favorite projects by far,” said Jennifer King, a researcher at NREL who has spent the past year constructing the wind slice of AEG. “It’s a nice mix of applied research, but we still get to work at the fundamental, technical level. The techniques and the communication across technologies just don’t exist today,” she said. “One thought is that buildings can shift their load to try and match (the variable output of wind), so we’re working with the buildings team to understand how.”

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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