國立高雄大學圖資館 |

Language: English

Back

Robust optimization in electric ener...

Conejo, Antonio J.

Robust optimization in electric energy systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust optimization in electric energy systemsby Xu Andy Sun, Antonio J. Conejo.
Author:	Sun, Xu Andy.
other author:	Conejo, Antonio J.
Published:	Cham :Springer International Publishing :2021.
Description:	x, 329 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Robust optimization.
Online resource:	https://doi.org/10.1007/978-3-030-85128-6
ISBN:	9783030851286$q(electronic bk.)

Robust optimization in electric energy systems
Sun, Xu Andy.

Robust optimization in electric energy systems[electronic resource] /by Xu Andy Sun, Antonio J. Conejo. - Cham :Springer International Publishing :2021. - x, 329 p. :ill., digital ;24 cm. - International series in operations research & management science,3132214-7934 ;. - International series in operations research & management science ;v. 140..

Chapter 1: Decision Making under Uncertainty in the Power Sector -- Chapter 2: Static Robust Optimization -- Chapter 3: Adaptive Robust Optimization -- Chapter 4: Distributionally Robust Optimization -- Chapter 5: Hybrid Adaptive Robust Optimization Models -- Chapter 6: Robust Optimization in Short-Term Power System Operations -- Chapter 7: Medium-Term Planning Models -- Chapter 8: Long-Term Planning Models.

This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered. Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text. The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in JuliaOpt)

ISBN: 9783030851286$q(electronic bk.)

Standard No.: 10.1007/978-3-030-85128-6doiSubjects--Topical Terms:

747261
Robust optimization.

LC Class. No.: QA402.5 / .S85 2021

Dewey Class. No.: 519.6

Robust optimization in electric energy systems
LDR:02842nmm 22003495a 4500 001 613803
003 DE-He213
005 20211108175727.0
006 m d
007 cr nn 008maaau
008 220627s2021 sz s 0 eng d
020 $a 9783030851286$q(electronic bk.)
020 $a 9783030851279$q(paper)
024 7 $a 10.1007/978-3-030-85128-6 $2 doi
035 $a 978-3-030-85128-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5 $b .S85 2021
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .S957 2021
100 1 $a Sun, Xu Andy. $3 911882
245 1 0 $a Robust optimization in electric energy systems $h [electronic resource] / $c by Xu Andy Sun, Antonio J. Conejo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a x, 329 p. : $b ill., digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v 313
505 0 $a Chapter 1: Decision Making under Uncertainty in the Power Sector -- Chapter 2: Static Robust Optimization -- Chapter 3: Adaptive Robust Optimization -- Chapter 4: Distributionally Robust Optimization -- Chapter 5: Hybrid Adaptive Robust Optimization Models -- Chapter 6: Robust Optimization in Short-Term Power System Operations -- Chapter 7: Medium-Term Planning Models -- Chapter 8: Long-Term Planning Models.
520 $a This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered. Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text. The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in JuliaOpt)
650 0 $a Robust optimization. $3 747261
650 0 $a Electrical engineering $x Mathematical models. $3 911883
650 0 $a Electric utilities. $3 224280
650 1 4 $a Operations Research/Decision Theory. $3 273963
650 2 4 $a Macroeconomics/Monetary Economics//Financial Economics. $3 731063
650 2 4 $a Operations Research, Management Science. $3 511451
700 1 $a Conejo, Antonio J. $3 491943
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a International series in operations research & management science ; $v v. 140. $3 490892
856 4 0 $u https://doi.org/10.1007/978-3-030-85128-6
950 $a Business and Management (SpringerNature-41169)