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UW’s Nguyen Receives Grant to Study Reliability of Clean Power Systems Through Machine Learning

October 4, 2022
head photo of a woman
Nga Nguyen, an assistant professor in UW’s Department of Electrical Engineering and Computer Science, recently was awarded a $503,459 U.S. Department of Energy grant to study how to optimize the operation of energy storage systems to improve reliability of the electric grid while handling the rapid move to renewable energy resources and electric vehicles by the United States and the world. (UW Photo)

A University of Wyoming assistant professor has received a U.S. Department of Energy (DOE) grant to study how to optimize the operation of energy storage systems to improve reliability of the electric grid while handling the rapid move to renewable energy resources and electric vehicles (EVs) by the United States and the world.

Nga Nguyen, in UW’s Department of Electrical Engineering and Computer Science, recently received a $503,459 DOE grant for her project titled “Optimal Operation of Large-Scale Energy Storage Systems to Improve Reliability of Clean Power Systems Using Machine Learning.”

Despite providing environmental benefits, the rapid integration of renewable energy resources and EVs is expected to increase operational challenges for the grid. Nguyen hopes her research will be able to meet these operational challenges.

“To mitigate these negative impacts and take advantage of high renewable energy sources and EV penetration, energy storage systems can be used due to their fast response and high storage capacity,” says Nguyen, who is the principal investigator of the project. “However, given the current cost scenario of storage technologies, deployments of energy storage systems can be economically impracticable if not properly located and sized.”

Her project is one of 29 energy-relevant research projects for which DOE recently granted $21 million in total Established Program to Stimulate Competitive Research (EPSCoR) funding. Projects funded span a wide range of energy research topics, including fundamental work in chemistry and materials science for clean energy, fusion energy, advanced computing and nuclear physics, as well as early-stage research and development for advanced manufacturing, hydrogen production and use, bioenergy, nuclear power and carbon management.

Projects were chosen based on competitive peer review under a DOE funding opportunity announcement for Building EPSCoR-State/DOE-National Laboratory Partnerships. The DOE EPSCoR program is managed by DOE’s Office of Science through its Office of Basic Energy Sciences. The grants, announced Sept. 12, began Sept. 1 and run through Sept. 1, 2025.

Nguyen’s project proposes to create advanced operation and control strategies for energy storage systems with optimal siting, sizing and technology to maximize system reliability under stability constraints while facilitating higher integration of renewable energy resources and EVs.

“Due to the presence of many uncertain variables and diverse constraints, this project develops a deep neural network technique inside Monte-Carlo simulations to solve composite reliability to reduce the computational burden,” Nguyen says of the broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. “The proposed research would have significant impacts on the operation of the modern power grid with the targets being multiple economic, technical, environmental and societal benefits.”

According to Nguyen, the benefits include:

-- Increasing public awareness of the requirement that renewable energy resources/EV-integrated energy systems must be sufficiently stable and resilient while enhancing environmental quality.

-- Fostering economic development by providing optimal operation and energy management.

-- Facilitating social equity by increasing electric production with high power quality to more customers.

-- Promoting a clean environment by advancing the integration of clean energy production via renewable energy resources and through the use of EVs. This would facilitate the reduction of the power grid’s dependence on fossil fuels.

The proposed framework also can potentially shape research in supporting the application of machine learning in power system reliability and can act as an initial step toward more early warning tools, operator decision support tools and better grid asset management, Nguyen says.

John Pierre, a professor, and Dongliang Duan, an associate professor -- both in the UW Department of Electrical Engineering and Computer Science -- are assisting Nguyen on the grant. The project also includes funding for three undergraduate students and two graduate students, she says.

Another notable impact of the grant will be the integration of educational and outreach activities to the state’s community colleges and K-12 students. The proposed project also will foster new collaborations between the principal investigators and researchers at Sandia National Laboratories, headquartered in Albuquerque, N.M. This will allow for impactful and long-lasting relationships for her lab, Nguyen says.

“In summary, the proposed tool shall support the improvement of quality power supply from the U.S. power systems, ensuring energy security while promoting clean and sustainable energy and transportation systems,” Nguyen says.

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