Abstract: 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.
P. Kalaimani and K. Mohana Sundaram, 2017. Genetic Algorithm (GA) and Ant Colony Optimization (ACO) Based Hybrid Technique for Solving Transmission Congestion Problem in Deregulated Power System. International Journal of Soft Computing, 12: 50-58.