Multi-Objective Dynamic VAR Planning Against Short-Term Voltage Instability Using a Decomposition-Based Evolutionary Algorithm

Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for optimal placement of dynamic VAR support against short-term voltage instability. The problem is formulated as a multi-ob...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems Vol. 29; no. 6; pp. 2813 - 2822
Main Authors: Xu, Yan, Dong, Zhao Yang, Meng, Ke, Yao, Wei Feng, Zhang, Rui, Wong, Kit Po
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automatic voltage control
Dynamic VAR support
Evolutionary computation
Load modeling
Motors
multi-objective optimization
Optimization
Pareto optimality
Power system dynamics
Power system stability
Reactive power
reactive power planning
short-term voltage stability
Stability criteria
STATCOM
ISSN:0885-8950, 1558-0679
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for optimal placement of dynamic VAR support against short-term voltage instability. The problem is formulated as a multi-objective optimization model minimizing two conflicting objectives: 1) the total investment cost and 2) the expected unacceptable short-term voltage performance subject to a set of probable contingencies. STATCOM is employed for its stronger dynamic VAR support capability. Indices for quantifying the short-term voltage stability and the related risk level are proposed for problem modeling. Candidate buses are selected based on the concept of trajectory sensitivity. Load dynamics are fully considered using a composite load model containing induction motor and other typical components. A relatively new and superior multi-objective evolutionary algorithm called MOEA/D is introduced and employed to find the Pareto optimal solutions of the model. The proposed method is verified on the New England 39-bus system using industry-grade models and simulation tool.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2014.2310733