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020 _a9789819725663
_9978-981-97-2566-3
024 7 _a10.1007/978-981-97-2566-3
_2doi
050 4 _aTK1001-1841
072 7 _aTHR
_2bicssc
072 7 _aTEC031020
_2bisacsh
072 7 _aTHR
_2thema
100 1 _aXie, Rui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977371
245 1 0 _aDistributionally Robust Optimization and its Applications in Power System Energy Storage Sizing
_h[electronic resource] /
_cby Rui Xie, Wei Wei.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXX, 452 p. 81 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Preliminary -- Basic Distributionally Robust Optimization -- Moment-Based Distributionally Robust Optimization -- Divergence Distributionally Robust Optimization -- Wasserstein-Distance Distributionally Robust Optimization.
520 _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.
650 0 _aElectric power production.
_965675
650 0 _aMathematical models.
_966910
650 0 _aOperations research.
_957943
650 1 4 _aElectrical Power Engineering.
_965867
650 2 4 _aMathematical Modeling and Industrial Mathematics.
_966912
650 2 4 _aOperations Research and Decision Theory.
_967143
700 1 _aWei, Wei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977372
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819725656
776 0 8 _iPrinted edition:
_z9789819725670
776 0 8 _iPrinted edition:
_z9789819725687
856 4 0 _uhttps://doi.org/10.1007/978-981-97-2566-3
912 _aZDB-2-ENE
912 _aZDB-2-SXEN
999 _c60096
_d60096