03283nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153072001600169072002300185072001500208100007100223245014700294250001800441264007200459300004300531336002600574337002600600338003600626347002400662505024400686520140800930650003102338650002502369650002502394650003402419650005402453650004502507700007102552710003402623773002602657776003602683776003602719776003602755856004602791978-981-97-2566-3DE-He21320251030112057.0cr nn 008mamaa240523s2024    si |    s    |||| 0|eng d  a97898197256637 a10.1007/978-981-97-2566-32doi 4aTK1001-1841 7aTHR2bicssc 7aTEC0310202bisacsh 7aTHR2thema1 aXie, Rui.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aDistributionally Robust Optimization and its Applications in Power System Energy Storage Sizingh[electronic resource] /cby Rui Xie, Wei Wei.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXX, 452 p. 81 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- Preliminary -- Basic Distributionally Robust Optimization -- Moment-Based Distributionally Robust Optimization -- Divergence Distributionally Robust Optimization -- Wasserstein-Distance Distributionally Robust Optimization.  aThis 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. 0aElectric power production. 0aMathematical models. 0aOperations research.14aElectrical Power Engineering.24aMathematical Modeling and Industrial Mathematics.24aOperations Research and Decision Theory.1 aWei, Wei.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981972565608iPrinted edition:z978981972567008iPrinted edition:z978981972568740uhttps://doi.org/10.1007/978-981-97-2566-3