Parallel augment Lagrangian relaxation method for transient stability constrained unit commitment

This paper presents a transient stability constrained unit commitment (TSCUC) model which achieves the objective of maintaining both transient stability and economical operation. In the TSCUC model, transient stability constraints are incorporated into the framework of unit commitment. In order to s...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on power systems Ročník 28; číslo 2; s. 1140 - 1148
Hlavní autoři: Jiang, Quanyuan, Zhou, Boran, Zhang, Mingze
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Augmented Lagrangian relaxation
Conferences
Constraints
Dynamic programming
Economics
Hybrid power systems
hybrid sub-gradient
Load flow
Operations research
Optimization
parallel computing
Parallel processing
Power system stability
reduced-space interior point method
Reproduction
Stability criteria
Transient analysis
Transient stability
transient stability constrained optimal power flow
transient stability constrained unit commitment
Unit commitment
ISSN:0885-8950, 1558-0679
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper presents a transient stability constrained unit commitment (TSCUC) model which achieves the objective of maintaining both transient stability and economical operation. In the TSCUC model, transient stability constraints are incorporated into the framework of unit commitment. In order to solve TSCUC problem, augmented Lagrangian relaxation (ALR) combined with variable duplication techniques and the auxiliary problem principle (APP) is used to decompose the TSCUC problem into two sub-problems: one sub-problem is a traditional unit commitment (UC) problem with prevailing constraints; another sub-problem is modeled as a transient stability constrained optimal power flow (TSCOPF) problem. The first sub-problem is solved by dynamic programming, while the second sub-problem is solved using a reduced-space interior point method. In ALR, an efficient hybrid sub-gradient method is developed to update all Lagrangian multipliers. The iterative process continues until the duality gap is sufficiently small. Finally, case studies show that the proposed methodology is very efficient for solving TSCUC problems within a parallel computing framework.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ObjectType-Conference-3
SourceType-Conference Papers & Proceedings-2
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2012.2216553