Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing - Tapa blanda

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Rui Xie received the B.E. degree in electrical engineering and the B.Sc. degree in mathematics in 2017, and the Ph.D. degree in electrical engineering in 2022 from Tsinghua University, Beijing, China. She is currently a postdoctoral fellow with the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong SAR. Her current research interests include stochastic optimization problems in power systems.

Wei Wei received the B.Sc. degree and Ph.D. degree both in electrical engineering from Tsinghua University, Beijing China, in 2008 and 2013, respectively. He was a Postdoc Research Associate with Tsinghua University from 2013 to 2015. As a visiting scholar, he worked with Professor Lang Tong at Cornell University, Ithaca, NY, USA from March to December in 2014, and with Professor Na Li at Harvard University, Cambridge, MA, USA from December 2014 to March 2015. He joined Tsinghua University as a faculty in July 2015. He is currently a Tenured Associate Professor with the Department of Electrical Engineering. Dr. Wei is a Fellow of the Institution of Engineering and Technology and the Operational Research Society.

De la contraportada

This book introduces the mathematical foundations of distributionally robust optimization (DRO) for decision-making problems with ambiguous uncertainties and applies them to tackle the critical challenge of energy storage sizing in renewable-integrated power systems, providing readers with an efficient and reliable approach to analyze and design real-world energy systems with uncertainties.

Covering a diverse range of topics, this book starts by exploring the necessity for energy storage in evolving power systems and examining the benefits of employing distributionally robust optimization. Subsequently, the cutting-edge mathematical theory of distributionally robust optimization is presented, including both the general theory and moment-based, KL-divergence, and Wasserstein-metric distributionally robust optimization theories. The techniques are then applied to various practical energy storage sizing scenarios, such as stand-alone microgrids, large-scale renewable power plants, bulkpower grids, and multi-carrier energy networks.

This book offers clear explanations and accessible guidance to bridge the gap between advanced optimization methods and industrial applications. Its interdisciplinary scope makes the book appealing to researchers, graduate students, and industry professionals working in electrical engineering and operations research, catering to both beginners and experts