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170203 ||| eng |
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|a 9781493967681
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| 100 |
1 |
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|a Huang, Yuping
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| 245 |
0 |
0 |
|a Electrical Power Unit Commitment
|h Elektronische Ressource
|b Deterministic and Two-Stage Stochastic Programming Models and Algorithms
|c by Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a New York, NY
|b Springer US
|c 2017, 2017
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| 300 |
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|a VIII, 93 p. 24 illus., 16 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction -- Deterministic Unit Commitment Models and Algorithms -- Two-Stage Stochastic Programming Models and Algorithms -- Nomenclature -- Renewable Energy Scenario Generation
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| 653 |
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|a Mechanical Power Engineering
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| 653 |
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|a Operations Research, Management Science
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| 653 |
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|a Electric power production
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| 653 |
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|a Operations research
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| 653 |
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|a Management science
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| 653 |
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|a Electrical Power Engineering
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| 653 |
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|a Energy policy
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| 653 |
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|a Energy Policy, Economics and Management
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| 653 |
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|a Energy and state
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| 700 |
1 |
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|a Pardalos, Panos M.
|e [author]
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| 700 |
1 |
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|a Zheng, Qipeng P.
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
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|a SpringerBriefs in Energy
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| 028 |
5 |
0 |
|a 10.1007/978-1-4939-6768-1
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4939-6768-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 333.7
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| 520 |
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|a This volume in the SpringerBriefs in Energy series offers a systematic review of unit commitment (UC) problems in electrical power generation. It updates texts written in the late 1990s and early 2000s by including the fundamentals of both UC and state-of-the-art modeling as well as solution algorithms and highlighting stochastic models and mixed-integer programming techniques. The UC problems are mostly formulated as mixed-integer linear programs, although there are many variants. A number of algorithms have been developed for, or applied to, UC problems, including dynamic programming, Lagrangian relaxation, general mixed-integer programming algorithms, and Benders decomposition. In addition the book discusses the recent trends in solving UC problems, especially stochastic programming models, and advanced techniques to handle large numbers of integer- decision variables due to scenario propagation
|