National Science Foundation (NSF) CAREER Award Grant

A five-year $ 529,776 Faculty Early Career Development Award ( NSF CAREER) titled: “Towards Data-Driven and Field-Validated Microgrid Modeling and Analysis Techniques”.

This NSF CAREER project aims to increase the reliability, security, and resiliency of the electric power grid via the use of microgrids. Microgrids are local electric energy systems that can operate with the grid and separate from the grid during emergencies. Microgrids can improve grid resiliency and sustainability, and accelerate disaster recovery. The project 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 project 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.

Stability prediction for microgrids require accurate mathematical modeling of the physical system to capture important dynamics and subtleties. Current modeling practices do not account for two critical real-world phenomena, namely, controller saturation and protection action, both of which have drastic effects on system stability. The first technical contribution of this project will address this gap by developing an approach to concurrently model those two phenomena. Additionally, microgrid stability studies are approached through linear or nonlinear techniques. Stability techniques can become too complex due to model order and number of nonlinearities. The second technical contribution will leverage advances in Scientific Machine Learning (SciML) to reduce a system?s model order by creating surrogates. The third technical contribution will be a microgrid stability prediction tool using SciML that will predict transient stability under different operating conditions and design factors. Data from an industry-grade microgrid and real-world equipment will be used to tune and confirm the accuracy of those surrogates and tools.

Principal Investigator: Mahmoud Kabalan, Ph.D., P.E.