Title: Efficient Management of Grid-scale Energy Storage Using Physics-informed Machine Learning
Abstract: Energy storage is increasingly recognized as a crucial asset for integrating renewable energy and enhancing grid reliability. However, due to the limited storage cap
acity and state-of-charge constraints, effective planning and operation of storage systems require a careful balance between future uncertainties and the physical constraints of storage technologies. This talk addresses the current challenges and opportunities in scaling up energy storage deployment within power systems, emphasizing the need for advanced tools to optimize and monitor storage operations. We will explore how integrating physics-based storage models with machine learning can lead to computationally efficient strategies for managing grid-scale energy storage. Furthermore, we present methods for incorporating power and energy constraints into these learning models, including decision-focused learning and opportunity value function quantification, enabling more effective and scalable solutions for energy storage management.
Bio: Dr. Bolun Xu is an assistant professor at Columbia University in Earth and Environmental engineering, with affiliation in electrical engineering. He received his Ph.D. from the University of Washington, Seattle, MS from ETH Zurich, and BS from Shanghai Jiaotong University, all in electrical engineering. Before joining Columbia, he was a postdoc at MIT Energy Initiative. His research focuses on designing and optimizing sustainable energy and power systems and integrating new technologies. He is a recipient of the NSF CAREER award, the Young Investigator Award from the IISE Energy Systems Division, and the Early Career Award from Informs Energy, Natural Resources, and Environment section.
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