Electrical Power Unit Commitment: Deterministic and Two-Stage Stochastic Programming Models and Algorithms (SpringerBriefs in Energy) von Huang, Yuping; Pardalos, Panos M.; Zheng, Qipeng P.: New (2017)

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pp. 120.

Bestandsnummer des Verk�ufers 26375078911

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Bibliografische Details

Titel: Electrical Power Unit Commitment: Deterministic and Two-Stage Stochastic Programming Models and Algorithms (SpringerBriefs in Energy)
Verlag: Springer
Erscheinungsjahr: 2017
Sprache: Englisch
ISBN-10: 1493967665
ISBN-13: 9781493967667
Einband: Softcover
Zustand: New

�ber diesen Titel

Inhaltsangabe

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

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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

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