TITLE:
An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling
AUTHORS:
Muneeb Ul Bashir, Ward Ul Hijaz Paul, Mubassir Ahmad, Danish Ali, Md. Safdar Ali
KEYWORDS:
Congestion Management, Deregulation, Optimal Power Flow, Teaching-Learning-Based Optimization (TLBO), Power System Modeling
JOURNAL NAME:
Smart Grid and Renewable Energy,
Vol.12 No.8,
August
30,
2021
ABSTRACT: In the present deregulated electricity market, power system congestion is
the main complication that an independent system operator (ISO) faces on a
regular basis. Transmission line congestion trigger serious problems for smooth
functioning in restructured power system causing an increase in the cost of
transmission hence affecting market efficiency. Thus, it is of utmost importance
for the investigation of various techniques in order to relieve congestion in
the transmission network. Generation rescheduling is one of the most
efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a
hybrid optimization based on two effective algorithms viz Teaching
learning-based optimization (TLBO) algorithm and Particle swarm optimization
(PSO) algorithm. For binding the constraints, the traditional penalty function
technique is incorporated. Modified IEEE 30-bus test system and modified IEEE
57-bus test system are used to inspect the usefulness of the suggested
methodology.