01077nam a22002651i 450099100422383970753620031010084541.0031111s1970 fr |||||||||||||||||fre b1255120x-39ule_instARCHE-058447ExLDip.to LingueitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.843.7Sand, George137342Oeuvres autobiographiques /Sand ; texte établi, présenté et annoté par Georges LubinParis :Gallimard,1970-19712 v. ;17 cmBibliothèque de la PléiadeLubin, Georges.b1255120x02-04-1413-11-03991004223839707536LE012 848.7 SAN 19V. 112012000151062le012-E0.00-no 00000.i1300218113-11-03LE012 848.7 SAN 20V. 212012000151048le012-E0.00-no 00000.i1300219313-11-03Oeuvres autobiographiques175130UNISALENTOle01213-11-03ma -frefr 0203464nam 22005535 450 991086419680332120250807143500.0981-9725-66-610.1007/978-981-97-2566-3(MiAaPQ)EBC31352501(Au-PeEL)EBL31352501(CKB)32141989800041(DE-He213)978-981-97-2566-3(EXLCZ)993214198980004120240523d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDistributionally Robust Optimization and its Applications in Power System Energy Storage Sizing /by Rui Xie, Wei Wei1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (461 pages)981-9725-65-8 Introduction -- Preliminary -- Basic Distributionally Robust Optimization -- Moment-Based Distributionally Robust Optimization -- Divergence Distributionally Robust Optimization -- Wasserstein-Distance Distributionally Robust Optimization.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.Electric power productionMathematical modelsOperations researchElectrical Power EngineeringMathematical Modeling and Industrial MathematicsOperations Research and Decision TheoryElectric power production.Mathematical models.Operations research.Electrical Power Engineering.Mathematical Modeling and Industrial Mathematics.Operations Research and Decision Theory.621.31Xie Rui1741038Wei Wei559260MiAaPQMiAaPQMiAaPQBOOK9910864196803321Distributionally Robust Optimization and Its Applications in Power System Energy Storage Sizing4166965UNINA