國立虎尾科技大學 |

Electrical power unit commitment = deterministic and two-stage stochastic programming models and algorithms /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Electrical power unit commitment/ by Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng.
Reminder of title:	deterministic and two-stage stochastic programming models and algorithms /
Author:	Huang, Yuping.
other author:	Pardalos, Panos M.
Published:	Boston, MA :Springer US : : 2017.,
Description:	viii, 93 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Electric power - Mathematical models. -
Online resource:	http://dx.doi.org/10.1007/978-1-4939-6768-1
ISBN:	9781493967681

Electrical power unit commitment = deterministic and two-stage stochastic programming models and algorithms /
Huang, Yuping.

Electrical power unit commitmentdeterministic and two-stage stochastic programming models and algorithms /[electronic resource] :by Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng. - Boston, MA :Springer US :2017. - viii, 93 p. :ill., digital ;24 cm. - SpringerBriefs in energy,2191-5520. - SpringerBriefs in energy..

Introduction -- Deterministic Unit Commitment Models and Algorithms -- Two-Stage Stochastic Programming Models and Algorithms -- Nomenclature -- Renewable Energy Scenario Generation.

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.

ISBN: 9781493967681

Standard No.: 10.1007/978-1-4939-6768-1doiSubjects--Topical Terms:

1250482
Electric power
--Mathematical models.

LC Class. No.: TK1005

Dewey Class. No.: 621.042

Electrical power unit commitment = deterministic and two-stage stochastic programming models and algorithms /
LDR:02182nam a2200337 a 4500 001 958355
003 DE-He213
005 20170808171538.0
006 m d
007 cr nn 008maaau
008 201118s2017 mau s 0 eng d
020 $a 9781493967681 $q (electronic bk.)
020 $a 9781493967667 $q (paper)
024 7 $a 10.1007/978-1-4939-6768-1 $2 doi
035 $a 978-1-4939-6768-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1005
072 7 $a TH $2 bicssc
072 7 $a KNB $2 bicssc
072 7 $a BUS070040 $2 bisacsh
082 0 4 $a 621.042 $2 23
090 $a TK1005 $b .H874 2017
100 1 $a Huang, Yuping. $3 1250481
245 1 0 $a Electrical power unit commitment $h [electronic resource] : $b deterministic and two-stage stochastic programming models and algorithms / $c by Yuping Huang, Panos M. Pardalos, Qipeng P. Zheng.
260 $a Boston, MA : $c 2017. $b Springer US : $b Imprint: Springer,
300 $a viii, 93 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in energy, $x 2191-5520
505 0 $a Introduction -- Deterministic Unit Commitment Models and Algorithms -- Two-Stage Stochastic Programming Models and Algorithms -- Nomenclature -- Renewable Energy Scenario Generation.
520 $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.
650 0 $a Electric power $x Mathematical models. $3 1250482
650 0 $a Determinants. $3 1250483
650 1 4 $a Energy. $3 784773
650 2 4 $a Energy Policy, Economics and Management. $3 784769
650 2 4 $a Power Electronics, Electrical Machines and Networks. $3 768690
650 2 4 $a Energy Technology. $3 768691
650 2 4 $a Operations Research, Management Science. $3 785065
700 1 $a Pardalos, Panos M. $3 669384
700 1 $a Zheng, Qipeng P. $3 884955
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in energy. $3 892218
856 4 0 $u http://dx.doi.org/10.1007/978-1-4939-6768-1
950 $a Energy (Springer-40367)