Welcome to the InfoSci Platform
Reference Hub5
Congestion Management Using Hybrid Particle Swarm Optimization Technique

Congestion Management Using Hybrid Particle Swarm Optimization Technique

Sujatha Balaraman, N. Kamaraj
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 16
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781609608934|DOI: 10.4018/jsir.2010070104
Cite Article Cite Article

MLA

Balaraman, Sujatha, and N. Kamaraj. "Congestion Management Using Hybrid Particle Swarm Optimization Technique." IJSIR vol.1, no.3 2010: pp.51-66. http://doi.org/10.4018/jsir.2010070104

APA

Balaraman, S. & Kamaraj, N. (2010). Congestion Management Using Hybrid Particle Swarm Optimization Technique. International Journal of Swarm Intelligence Research (IJSIR), 1(3), 51-66. http://doi.org/10.4018/jsir.2010070104

Chicago

Balaraman, Sujatha, and N. Kamaraj. "Congestion Management Using Hybrid Particle Swarm Optimization Technique," International Journal of Swarm Intelligence Research (IJSIR) 1, no.3: 51-66. http://doi.org/10.4018/jsir.2010070104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solving congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of unexpected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Optimization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch flow limits and load bus voltage magnitude limits are included as penalties in the fitness function. Numerical results on three test systems namely modified IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with PSO and EP approaches in order to demonstrate its performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.