Electrical power unit commitment : deterministic and two-stage stochastic programming models and algorithms / Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng.
2017
TK1005
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Title
Electrical power unit commitment : deterministic and two-stage stochastic programming models and algorithms / Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng.
Author
ISBN
9781493967681 (electronic book)
1493967681 (electronic book)
9781493967667
1493967681 (electronic book)
9781493967667
Publication Details
New York, NY : Springer, 2017.
Language
English
Description
1 online resource (98 pages).
Item Number
10.1007/978-1-4939-6768-1 doi
Call Number
TK1005
Dewey Decimal Classification
621.042
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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text file PDF
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Description based on print version record.
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SpringerBriefs in energy.
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Table of Contents
Introduction
Deterministic Unit Commitment Models and Algorithms
Two-Stage Stochastic Programming Models and Algorithms
Nomenclature
Renewable Energy Scenario Generation.
Deterministic Unit Commitment Models and Algorithms
Two-Stage Stochastic Programming Models and Algorithms
Nomenclature
Renewable Energy Scenario Generation.