TY  - JOUR
T1  - Genetic Algorithm (GA) and Ant Colony Optimization (ACO)
Based Hybrid Technique for Solving Transmission Congestion
Problem in Deregulated Power System
AU - Kalaimani, P. AU - Sundaram, K. Mohana 
JO  - International Journal of Soft Computing
VL  - 12
IS  - 1
SP  - 50
EP  - 58
PY  - 2017
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2017.50.58
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2017.50.58
KW  - Transmission congestion
KW  -power loss congestion removal
KW  -minimizing cost
KW  -hybrid technique
KW  -genetic algorithm
KW  -ant colony algorithm
AB  - In this study, an integrated technique for solving congestion problem in deregulated power system
is proposed. The proposed integrated technique is a hybrid combination of Genetic Algorithm (GA) and Ant
Colony Optimization (ACO) algorithm first in class. The GA is one of the global optimization algorithms which
generates best solution to optimization problems. In this study, Genetic Algorithm (GA) is used to optimize the
real power changes of the generators from the generation limits while congestion occurred. The Ant Colony
Algorithm (ACO) is one of the probabilistic based local search algorithm for solving computational problems
which can be reduced to find good paths through graphs. In this study, the Ant Colony Algorithm (ACO) is
used to minimize the congestion cost optimally by optimizing the incremental, decremented active power and
the corresponding generator price bids. The proposed integrated technique is tested in IEEE standard 30 bus
system to prove its robustness. The proposed technique effectively reduces the congestion management cost
and the power loss of the system considered. The proposed integrated technique is implemented in MATLAB
software and the output is compared with genetic algorithm.
ER  -