Article by Emily Cerf via UCSC Newscenter

It’s a story that’s become all too familiar — high winds knock out a power line, and a community can go without power for hours to days, an inconvenience at best and a dangerous situation at worst. UC Santa Cruz Assistant Professor of Electrical and Computer Engineering Yu Zhang and his lab are leveraging tools to improve the efficiency, reliability, and resilience of power systems, and have developed an artificial intelligence (AI) -based approach for the smart control of microgrids for power restoration when outages occur. 

They describe their new AI model and show that it outperforms traditional power restoration techniques in a new paper published in the journal IEEE Transactions on Control of Network Systems, a top journal in the field of control systems and network science. Shourya Bose, a Ph.D. student in Zhang’s lab, is the paper’s first author.

“Nowadays, microgrids are really the thing that both people in industry and in academia are focusing on for the future power distribution systems,” Zhang said.

In many communities, infrastructure and its users are totally reliant on a local power generating utility company for electricity. This means that in the case of a disaster or extreme weather event, or even just a tree falling on a line, power goes out until repairs can be made. 

Today, many electricity systems are smart in that they are interconnected with computers and sensors. They often incorporate local renewable energy sources such as rooftop solar panels or small wind turbines, and some households and buildings rely on backup generators and/or energy batteries for their electricity demand. 

This mix of power sources presents an opportunity to address outages locally by using alternative energy sources to provide electricity before upstream power is restored. One way to do this is with a microgrid, which distributes electricity to small areas such as a few buildings or a town — although the size of the microgrid can vary. 

The microgrid can be connected to the main power utility source, but also can function while disconnected in “islanding mode,” self-supported by alternate energy sources and unaffected by the issues impacting the main utility. Zhang’s research team focuses on optimizing how microgrids pull from these various alternate sources such as renewables, generators, and batteries to restore power quickly and correctly. 

“Essentially, we want to bring the power generation closer to the demand side in order to get rid of the long transmission lines,” Zhang said. “This can improve the power quality and reduce the power losses over the lines. In this way, we will make the grid smaller, but stronger and more resilient.”

To optimally operate microgrids, Zhang’s lab developed an AI-based technique called deep reinforcement learning, the same concept that underpins large language models, to create an efficient framework  that includes models of many components of the power system. Reinforcement learning depends on rewarding the algorithm for successfully responding to the changing environment  —  so an agent  is rewarded when it is able to successfully restore the demanded power of all components of the network. They explicitly model the practical constraints of the real-world system, such as the branch flows that power lines can handle.

Interested in learning more about AI? Check out our upcoming in person event, AI Horizons Part 2.