Mahmoud Kabalan
Liam James Doyle / University of St. Thomas

Professor Mahmoud Kabalan Receives NSF CAREER Award for Microgrid Research

Mahmoud Kabalan, an assistant professor at the University of St. Thomas in Minnesota and the director of its Center for Microgrid Research, was recently awarded a five-year grant from the National Science Foundation that will help advance microgrid research and expand student opportunities.

The $529,000 Faculty Early Career Development (CAREER) award will support Dr. Kabalan’s work to increase the reliability, security and resiliency of the electric power grid via the use of microgrids.

Engineering Professor Mahmoud Kabalan leads mircogrid research and testing at the Facilities and Design Center at the University of St. Thomas in St. Paul, Minnesota.

The CAREER award is one of the NSF’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. This is the first NSF CAREER Award for St. Thomas’ School of Engineering.

“This award provides a strong foundation for me to grow as a teacher-scholar and empowers me to create a truly unique educational experience for my students. The outcomes of this award will get us one step closer to a more resilient and reliable electric grid.”

Microgrids are local electric energy systems that can operate with the grid and separate from the grid during emergencies and can improve grid resiliency and sustainability and accelerate disaster recovery.  

The Center for Microgrid Research at St. Thomas is one of just a handful of premier research and educational facilities of its kind in North America.

Kabalan’s research will bring transformative change to how microgrids are designed and operated by addressing the gap between theoretical studies and real-world applications. To achieve this goal, state-of-the-art data-driven and machine learning algorithms will be employed. The intellectual merits of the project include developing a new approach to accurately model real-world conditions, using machine learning to reduce model complexity, and creating and field-validating a microgrid stability prediction tool.

The broader impacts of the research include an improved method to design and operate microgrids, which would reduce implementation costs. By reducing costs, microgrids can be deployed faster in both developing and developed nations. This would quicken the electrification of historically marginalized communities and improve grid resiliency, robustness, and sustainability. The newly created knowledge would be disseminated through hands-on courses and workshops on power engineering.