Ant colony optimization applied to distribution system reconfiguration

Abstract

Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different combinatorial optimization problems. In this method, a number of artificial ants build solutions for an optimization problem and exchange information on their quality through a communication scheme that is similar to the one adopted by real ants. In this thesis, Ant Colony Optimization is used to solve reconfiguration of a benchmark distribution system consisting of 33 buses for loss minimization. Solving this problem is a formidable task even for a simple distribution network as the number of possible switching options that are to be considered is numerous. The results obtained using any meta-heuristic method strongly depends on the control parameter values chosen. The proposed algorithm is coded in MATLAB, and power world simulator to check the result obtained by MATLAB. The performance of the proposed technique is tested on two standard distribution systems with size (33 buses). The optimal configuration result shows that 31.9% and reduction of loss from the initial configuration in the test case. And also significant reductions in CPU time achieved.