Electrical Power Unit Commitment

Yuping Huang

ISBN 10: 1493967665 / ISBN 13: 9781493967667

Editore: SPRINGER NATURE Jan 2017, 2017

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This item is printed on demand - it takes 3-4 days longer - Neuware -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 93 pp. Englisch.

Codice articolo 9781493967667

Dati bibliografici

• Titolo: Electrical Power Unit Commitment
• Editore: SPRINGER NATURE Jan 2017
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 1493967665
• ISBN 13: 9781493967667
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 238 L x 156 A x 10 P (millimetri) 238 millimetri di larghezza per 156 millimetri di altezza per 10 millimetri di profondità
• Peso: 183 grammi

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Riassunto

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