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Conference: 2023 NSF Algorithms for Modern Power Systems (AMPS) Workshop

Barry Lee·Southern Methodist University, TX·2023–2026·COMPLETED
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INSTITUTION

Southern Methodist University, TX

PRINCIPAL INVESTIGATOR

Barry Lee

FUNDING

$20K

YEAR

2023

MOONBASE SCORE

Not commercializable

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Abstract

The project is to hold a workshop, titled "2023 NSF Algorithms for Modern Power Systems (AMPS) Workshop," which will be held at George Mason University in Fall (October) 2023. Modernizing the power grid infrastructure continues to be a grand challenge in the United States and around the world. Modernization will lead to a more reliable source and delivery of electric power, which is vital to the national security and economy of this country. The urgency of this goal has been further accentuated by the devastating societal consequences that have recently occurred due to disruptions in the delivery of electric power. But to achieve this goal, a combined effort from many branches of science and engineering will be needed. In particular, because of the substantial mathematical and statistical basis of power grid analysis and modelling, mathematicians and statisticians can greatly contribute to this effort. Innovative mathematics and statistical approaches can be applied to traditional and modern power grid applications. To accelerate this contribution, the Division of Mathematical Sciences at NSF has recently partnered with the Office of Electricity at the Department of Energy (DOE) to create the Algorithms for Modern Power Systems (AMPS) program. The goal of this conference is to bring together mathematicians and statisticians of active AMPS projects and DOE engineers and scientists to report on research development, and to facilitate creation, transfer and application of knowledge on modern power grids. In addition, this conference will foster synergism between the different AMPS projects and DOE projects on the emerging power grid. Technical talks and posters will be presented in this conference. Topics to be presented include machine learning and neural networks approaches to power grid dynamics, uncertainty quantification and dimension reduction techniques for weather renewables, real-time approaches for fault detection, and power grid vulernability. Student and post-doctoral researchers are strongly encouraged to attend this workshop. This workshop will provide these early-career researchers exposure to other directions in power grid research supported through AMPS, and opportunities to connect with DOE scientists and engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

machine learningneural networksmathematical modelinginterdisciplinary collaborationalgorithmsuncertainty quantificationrenewable energyfault detectionpower systemsgrid modernization

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