國立高雄大學圖資館 |

Language: English

Back

Non-centralized optimization-based control schemes for large-scale energy systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Non-centralized optimization-based control schemes for large-scale energy systemsby W. Wicak Ananduta.
Author:	Ananduta, W. Wicak.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 154 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power systemsAutomatic control.
Online resource:	https://doi.org/10.1007/978-3-030-89803-8
ISBN:	9783030898038$q(electronic bk.)

Non-centralized optimization-based control schemes for large-scale energy systems
Ananduta, W. Wicak.

Non-centralized optimization-based control schemes for large-scale energy systems[electronic resource] /by W. Wicak Ananduta. - Cham :Springer International Publishing :2022. - xvii, 154 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

Introduction -- Non-centralized MPC-Based Economic Dispatch -- Distributed Augmented Lagrangian Methods.

This book describes the development of innovative non-centralized optimization-based control schemes to solve economic dispatch problems of large-scale energy systems. Particularly, it focuses on communication and cooperation processes of local controllers, which are integral parts of such schemes. The economic dispatch problem, which is formulated as a convex optimization problem with edge-based coupling constraints, is solved by using methodologies in distributed optimization over time-varying networks, together with distributed model predictive control, and system partitioning techniques. At first, the book describes two distributed optimization methods, which are iterative and require the local controllers to exchange information with each other at each iteration. In turn, it shows that the sequence produced by these methods converges to an optimal solution when some conditions, which include how the controllers must communicate and cooperate, are satisfied. Further, it proposes an information exchange protocol to cope with possible communication link failures. Finally, the proposed distributed optimization methods are extended to the cases with random communication networks and asynchronous updates. Overall, this book presents a set of improved predictive control and distributed optimization methods, together with a rigorous mathematical analysis of each proposed algorithms. It describes a comprehensive approach to cope with communication and cooperation issues of non-centralized control schemes and show how the improved schemes can be successfully applied to solve the economic dispatch problems of large-scale energy systems.

ISBN: 9783030898038$q(electronic bk.)

Standard No.: 10.1007/978-3-030-89803-8doiSubjects--Topical Terms:

919776
Electric power systems
--Automatic control.

LC Class. No.: TK1007 / .A53 2022

Dewey Class. No.: 621.317

Non-centralized optimization-based control schemes for large-scale energy systems
LDR:02844nmm a2200337 a 4500 001 619469
003 DE-He213
005 20211211115632.0
006 m d
007 cr nn 008maaau
008 221103s2022 sz s 0 eng d
020 $a 9783030898038$q(electronic bk.)
020 $a 9783030898021$q(paper)
024 7 $a 10.1007/978-3-030-89803-8 $2 doi
035 $a 978-3-030-89803-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1007 $b .A53 2022
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 621.317 $2 23
090 $a TK1007 $b .A533 2022
100 1 $a Ananduta, W. Wicak. $3 919775
245 1 0 $a Non-centralized optimization-based control schemes for large-scale energy systems $h [electronic resource] / $c by W. Wicak Ananduta.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xvii, 154 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5061
505 0 $a Introduction -- Non-centralized MPC-Based Economic Dispatch -- Distributed Augmented Lagrangian Methods.
520 $a This book describes the development of innovative non-centralized optimization-based control schemes to solve economic dispatch problems of large-scale energy systems. Particularly, it focuses on communication and cooperation processes of local controllers, which are integral parts of such schemes. The economic dispatch problem, which is formulated as a convex optimization problem with edge-based coupling constraints, is solved by using methodologies in distributed optimization over time-varying networks, together with distributed model predictive control, and system partitioning techniques. At first, the book describes two distributed optimization methods, which are iterative and require the local controllers to exchange information with each other at each iteration. In turn, it shows that the sequence produced by these methods converges to an optimal solution when some conditions, which include how the controllers must communicate and cooperate, are satisfied. Further, it proposes an information exchange protocol to cope with possible communication link failures. Finally, the proposed distributed optimization methods are extended to the cases with random communication networks and asynchronous updates. Overall, this book presents a set of improved predictive control and distributed optimization methods, together with a rigorous mathematical analysis of each proposed algorithms. It describes a comprehensive approach to cope with communication and cooperation issues of non-centralized control schemes and show how the improved schemes can be successfully applied to solve the economic dispatch problems of large-scale energy systems.
650 0 $a Electric power systems $x Automatic control. $3 919776
650 0 $a Mathematical optimization. $3 183292
650 1 4 $a Control and Systems Theory. $3 825946
650 2 4 $a Power Electronics, Electrical Machines and Networks. $3 338660
650 2 4 $a Optimization. $3 274084
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 557607
856 4 0 $u https://doi.org/10.1007/978-3-030-89803-8
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)