This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered.
Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text.
The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in Julia Opt).
Table des matières
Chapter 1: Decision Making under Uncertainty in the Power Sector.- Chapter 2: Static Robust Optimization.- Chapter 3: Adaptive Robust Optimization.- Chapter 4: Distributionally Robust Optimization.- Chapter 5: Hybrid Adaptive Robust Optimization Models.- Chapter 6: Robust Optimization in Short-Term Power System Operations .- Chapter 7: Medium-Term Planning Models.- Chapter 8: Long-Term Planning Models.
A propos de l’auteur
Andy Sun is an assistant professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech, USA. Dr. Sun conducts research in optimization and stochastic modeling with applications in electric energy systems and electricity markets. He also works on theory and algorithms for robust and stochastic optimization, and large scale convex optimization.
Antonio J. Conejo received an M.S. from MIT, US and a Ph.D. from the Royal Institute of Technology, Sweden. He has published over 190 papers in refereed journals and is the author or coauthor of books published by Springer, John Wiley, Mc Graw-Hill and CRC Press. He has been the principal investigator of many research projects financed by public agencies and the power industry and has supervised 19 Ph D theses. He is an IEEE Fellow.
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