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Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Jie Feng · Yuanyuan Shi · Guannan Qu · Steven Low · Anima Anandkumar · Adam Wierman
Event URL: https://www.climatechange.ai/papers/neurips2022/34 »

This paper is a summary of a recently submitted work. Deep Reinforcement Learning (DRL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of explicit stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in both single-phase and three-phase distribution grids. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of our approach with IEEE test feeders, where the proposed method achieves the best overall performance, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.

Author Information

Jie Feng (UCSD)
Yuanyuan Shi (University of California San Diego)

Yuanyuan Shi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego. Previously, she was a postdoc in the Computing and Mathematical Sciences Department at Caltech from 2020-2021 and finished her Ph.D. in Electrical and Computer Engineering from the University of Washington Seattle. Her research focuses on energy systems using machine learning, optimization and control.

Guannan Qu (Carnegie Mellon University)
Steven Low (California Institute of Technology)
Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Adam Wierman (Caltech)

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