標(biāo)題: Titlebook: Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing; Rui Xie,Wei Wei Book 2024 The Editor(s) ( [打印本頁] 作者: incoherent 時(shí)間: 2025-3-21 16:10
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書目名稱Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing讀者反饋
書目名稱Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing讀者反饋學(xué)科排名
作者: deface 時(shí)間: 2025-3-21 22:58
https://doi.org/10.1007/0-306-48369-6s in a more accurate mixed-integer linear programming problem. The second reformulation, based on conditional value-at-risk, provides a more tractable, albeit conservative, linear programming approximation. This chapter introduces the research presented in Xie et?al. (.).作者: Freeze 時(shí)間: 2025-3-22 04:11 作者: 狼群 時(shí)間: 2025-3-22 08:03
Sizing Wind Farm and?Energy Storage Considering Wake Effecteveloped. This method effectively transforms the DRO model into a linear programming problem. Following this, an iterative algorithm is formulated, which incorporates methods for evaluating the Lipschitz moduli.作者: pacifist 時(shí)間: 2025-3-22 09:30 作者: 口音在加重 時(shí)間: 2025-3-22 14:31 作者: 口音在加重 時(shí)間: 2025-3-22 20:01
Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing作者: CREEK 時(shí)間: 2025-3-22 23:22
Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing978-981-97-2566-3作者: Conscientious 時(shí)間: 2025-3-23 03:37
Book 2024rgy storage sizing scenarios, such as stand-alone microgrids, large-scale renewable power plants, bulk power 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. 作者: 牌帶來 時(shí)間: 2025-3-23 07:05
plants, bulk power 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. 978-981-97-2568-7978-981-97-2566-3作者: 火海 時(shí)間: 2025-3-23 12:51
Preliminaryprograms and duality, multi-objective optimization, stochastic programming, and robust optimization. Some results are stated without proof and references are given for readers interested in the details. Besides the mathematical preliminary, we also summarize some popular power flow models, which are useful in power system planning and operation.作者: 拋棄的貨物 時(shí)間: 2025-3-23 16:22
Moment-Based Distributionally Robust Optimization and mature ways to model the inexactness of empirical probability distribution?for uncertainty-integrated optimization problems. This chapter focuses on the moment-based DRO, typical moment-based ambiguity sets are summarized, the reformulation methods are explained, and finally, the moment-based DRO incorporating unimodality is also introduced.作者: GLARE 時(shí)間: 2025-3-23 21:01
-Divergence Distributionally Robust Optimizationwhat the .-divergence ambiguity set is, including the definitions, important properties, and the relationships between different types of .-divergences. Then the .-divergence ambiguity set is formally defined, followed by the reformulation methods of the corresponding worst-case expectations and robust chance constraints.作者: 彎曲道理 時(shí)間: 2025-3-24 00:21
Wasserstein-Distance Distributionally Robust Optimizationn about what a Wasserstein-distance ambiguity set should be like. Subsequently, Wasserstein-distance ambiguity sets of discrete and general probability distributions are both defined, with discussions about the parameter choice. Finally, the reformulation methods of worst-case expectations and robust chance constraints are introduced.作者: BUOY 時(shí)間: 2025-3-24 05:37 作者: 一加就噴出 時(shí)間: 2025-3-24 08:27 作者: 不連貫 時(shí)間: 2025-3-24 14:21 作者: 小說 時(shí)間: 2025-3-24 16:03
Introduction, of ES and introducing the models for ES operation. Subsequently, a comprehensive review is provided for ES sizing under different scenarios from the perspectives of the generation side, the grid side, and the distribution/demand side. A holistic view of the book’s content is offered in the end.作者: Dictation 時(shí)間: 2025-3-24 21:13 作者: 有機(jī)體 時(shí)間: 2025-3-25 00:46
Sizing Transmission and Energy Storage for Remote Large-Scale Renewable Power Plants ambiguity set based on Wasserstein distance. Through piecewise-affine function expressions, the proposed sizing model is transformed into a linear programming problem. This chapter introduces the work in Xie et?al. (.).作者: 貝雷帽 時(shí)間: 2025-3-25 05:04
y robust optimization by energy storage sizing problems.Brid.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 renewab作者: HEAVY 時(shí)間: 2025-3-25 08:07 作者: 出沒 時(shí)間: 2025-3-25 12:46
Codetermination and Profit-sharing, and mature ways to model the inexactness of empirical probability distribution?for uncertainty-integrated optimization problems. This chapter focuses on the moment-based DRO, typical moment-based ambiguity sets are summarized, the reformulation methods are explained, and finally, the moment-based DRO incorporating unimodality is also introduced.作者: indenture 時(shí)間: 2025-3-25 17:04 作者: 信任 時(shí)間: 2025-3-26 00:01
https://doi.org/10.1007/978-3-030-17847-5n about what a Wasserstein-distance ambiguity set should be like. Subsequently, Wasserstein-distance ambiguity sets of discrete and general probability distributions are both defined, with discussions about the parameter choice. Finally, the reformulation methods of worst-case expectations and robust chance constraints are introduced.作者: PHAG 時(shí)間: 2025-3-26 03:45
https://doi.org/10.1007/978-1-349-00475-1m into bi-objective linear programming (LP). According to the theory of parametric LP, it is explained that the Pareto frontier is piecewise affine, and an algorithm for solving the analytical expression of the Pareto frontier is proposed. This chapter introduces the work in Xie et?al. (.).作者: AIL 時(shí)間: 2025-3-26 04:57 作者: 粘 時(shí)間: 2025-3-26 11:39
https://doi.org/10.1007/978-1-349-01916-8ibutionally robust chance-constrained program for the optimal sizing problem. This is then reformulated into a manageable linear programming problem through conservative approximation. This chapter introduces the work in Guo et?al. (.).作者: Hyaluronic-Acid 時(shí)間: 2025-3-26 14:29 作者: encyclopedia 時(shí)間: 2025-3-26 17:31 作者: 確定方向 時(shí)間: 2025-3-26 22:14
Sizing Renewable, Transmission, and Energy Storage in Low-Carbon Power Systemsst-case carbon emission expectation and distributionally robust risk of load shedding are transformed into linear models. This makes the proposed sizing model a matter of solving mixed-integer linear programming problems. This chapter introduces the work in Xie et al. (Energy 263:125653, 2023), (.).作者: heterogeneous 時(shí)間: 2025-3-27 01:44
Book 2024ties 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 start作者: neologism 時(shí)間: 2025-3-27 06:29
Introduction,es. Then the key concepts of optimization in the uncertain environment are summarized, including the basic ideas of stochastic programming, robust optimization, and distributionally robust optimization. The chapter then shifts to the critical role of ES in power systems, analyzing the diverse usages作者: INCUR 時(shí)間: 2025-3-27 12:30
Preliminaryn, the basics of probability and risk measures are summarized. Subsequently, optimization theories are reviewed, including convex optimization, conic programs and duality, multi-objective optimization, stochastic programming, and robust optimization. Some results are stated without proof and referen作者: Atmosphere 時(shí)間: 2025-3-27 15:54 作者: BRINK 時(shí)間: 2025-3-27 20:52 作者: 多樣 時(shí)間: 2025-3-27 23:28
-Divergence Distributionally Robust Optimization.-divergence to the empirical probability distribution is within a given threshold. An overview of .-divergence is presented first to give a sense of what the .-divergence ambiguity set is, including the definitions, important properties, and the relationships between different types of .-divergence作者: 面包屑 時(shí)間: 2025-3-28 03:03 作者: 星星 時(shí)間: 2025-3-28 09:41
Sizing Renewable and Energy Storage in Fully Renewable Stand-Alone Microgridsbjective optimization model to minimize load shedding Shortfall risk and total investment cost, taking into account the uncertainty of renewable energy generation and load. To account for the inaccuracy of empirical probability distributions, an ambiguity set of probability distributions is construc作者: PTCA635 時(shí)間: 2025-3-28 13:14 作者: electrolyte 時(shí)間: 2025-3-28 14:39
Storage Sizing in Power Networks to Reduce Renewable Generation Curtailmentpecialized power flow model incorporating voltage and reactive power is utilized. The uncertainty of renewable energy production is expressed through inexact probability distributions, encapsulated in a data-driven ambiguity set based on Wasserstein distance. Using this data, the rate of renewable e作者: acclimate 時(shí)間: 2025-3-28 19:29
Sizing Renewable, Transmission, and Energy Storage in Low-Carbon Power Systemsity sets based on Wasserstein distance are used for modeling renewable energy generation and load demand uncertainties. A distributionally robust bi-objective sizing model is proposed that minimizes the total investment cost and the worst-case expectation of carbon emissions in normal situations whi作者: concubine 時(shí)間: 2025-3-29 02:03 作者: 上流社會(huì) 時(shí)間: 2025-3-29 03:21 作者: 放棄 時(shí)間: 2025-3-29 07:44 作者: 嘴唇可修剪 時(shí)間: 2025-3-29 12:48
Rui Xie,Wei WeiProvides a tutorial on the cutting-edge mathematical theory of distributionally robust optimization.Illustrates how to apply distributionally robust optimization by energy storage sizing problems.Brid作者: negligence 時(shí)間: 2025-3-29 16:28 作者: DALLY 時(shí)間: 2025-3-29 20:54
https://doi.org/10.1007/978-981-97-2566-3Distributionally Robust Optimization; Energy Storage Sizing; Data-driven Robust Optimization; Energy St作者: nerve-sparing 時(shí)間: 2025-3-30 02:08
978-981-97-2568-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: vector 時(shí)間: 2025-3-30 04:34 作者: ovation 時(shí)間: 2025-3-30 09:14
The Notebooks from Underground,n, the basics of probability and risk measures are summarized. Subsequently, optimization theories are reviewed, including convex optimization, conic programs and duality, multi-objective optimization, stochastic programming, and robust optimization. Some results are stated without proof and referen作者: 腐敗 時(shí)間: 2025-3-30 13:57
https://doi.org/10.1007/978-3-030-64210-5e, followed by an explanation of distributionally robust uncertainties, including worst-case expectations, robust chance constraints, and distributionally robust risk measures. Subsequently, a model of two-stage DRO is presented, which will serve as the main optimization framework applied to various作者: 抗體 時(shí)間: 2025-3-30 18:36
Codetermination and Profit-sharing,second-order moments constraints. Moment-based DRO not only has the longest history among different DRO techniques but also is one of the most popular and mature ways to model the inexactness of empirical probability distribution?for uncertainty-integrated optimization problems. This chapter focuses作者: esthetician 時(shí)間: 2025-3-30 23:17 作者: 啪心兒跳動(dòng) 時(shí)間: 2025-3-31 00:59 作者: Alveolar-Bone 時(shí)間: 2025-3-31 06:16
https://doi.org/10.1007/978-1-349-00475-1bjective optimization model to minimize load shedding Shortfall risk and total investment cost, taking into account the uncertainty of renewable energy generation and load. To account for the inaccuracy of empirical probability distributions, an ambiguity set of probability distributions is construc
