A Talk On: Accelerating Probabilistic Power Flow in Electrical Distribution Networks (Open to all IEEE members)
The ongoing democratization of energy is creating a high degree of operational uncertainty in electrical distribution networks. To combat this uncertainty, advanced Uncertainty Quantification (UQ) tools can be used to characterize the forecasted operational state of a network via probabilistic power flow (PPF) analysis. Such UQ tools, though, often suffer from the dreaded curse of dimensionality, requiring thousands of power flow solves in order to characterize the parameters inside the resulting UQ models. In massive distribution networks, with three unbalanced phases and tens of thousands of state variables, sequential power flow solves can become a serious computational bottleneck. This talk develops a computationally efficient algorithm which speeds up the PPF problem by leveraging a variety of tools from numerical linear algebra. These tools, grounded in fast matrix inversion techniques and a dynamic projection-based model order reduction scheme, allow us to solve the PPF problem an order of magnitude faster than the traditional Newton method applied to a full order model. Along the way, we will highlight how the selection and application of tools from numerical linear algebra benefit greatly from a thorough understanding of the underlying problem and its physical characteristics.
Date and Time
Location
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Registration
- Date: 20 Nov 2020
- Time: 01:00 PM to 02:00 PM
- All times are (GMT-05:00) US/Eastern
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- Burlington, Vermont
- United States
Speakers
Sam Chevalier
Accelerating Probabilistic Power Flow in Electrical Distribution Networks
The ongoing democratization of energy is creating a high degree of operational uncertainty in electrical distribution networks. To combat this uncertainty, advanced Uncertainty Quantification (UQ) tools can be used to characterize the forecasted operational state of a network via probabilistic power flow (PPF) analysis. Such UQ tools, though, often suffer from the dreaded curse of dimensionality, requiring thousands of power flow solves in order to characterize the parameters inside the resulting UQ models. In massive distribution networks, with three unbalanced phases and tens of thousands of state variables, sequential power flow solves can become a serious computational bottleneck. This talk develops a computationally efficient algorithm which speeds up the PPF problem by leveraging a variety of tools from numerical linear algebra. These tools, grounded in fast matrix inversion techniques and a dynamic projection-based model order reduction scheme, allow us to solve the PPF problem an order of magnitude faster than the traditional Newton method applied to a full order model. Along the way, we will highlight how the selection and application of tools from numerical linear algebra benefit greatly from a thorough understanding of the underlying problem and its physical characteristics.
Biography:
Samuel Chevalier received the B.S. degree in electrical engineering from the University of Vermont in 2015. Advised by Dr. Paul Hines, he then completed the UVM Accelerated Master’s Program (AMP) in electrical engineering in 2016, where his research focused on characterizing the statistical warning signs of voltage collapse in power systems. Currently, he is working toward the Ph.D. degree in mechanical engineering at the Massachusetts Institute of Technology, where his research focuses on a variety of inverse problems in power systems. This includes locating the sources of forced oscillations in transmission networks, black box modeling of wide-area dynamics, and state estimation. In the spring of 2021, Sam will join the Energy Analytics & Markets group at the Technical University of Denmark as a postdoctoral researcher.