A Deep Reinforcement Learning Approach to Automate Smart Power Restoration in Microgrids

Thursday - December 7 2023, 03:48 UTC - 11 months ago

UC Santa Cruz Assistant Professor Yu Zhang and his lab have developed an AI-based approach for the smart control of microgrids for power restoration when outages occur. The model they have developed is based on deep reinforcement learning, which rewards the algorithm for successfully responding to the changing environment when restoring power. This model outperforms traditional power restoration techniques, and is more efficient and cost-effective.

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. 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 because 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 microgrid size can vary.

The microgrid can be connected to the main power utility source but can also 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-worl and then synthesize numerical procedures to solve the problem utterly.