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Optimal Capacitor Placement in Distribution Systems Employing Ant Colony Search Algorithm

This article introduces an ant colony search algorithm (ACSA) to solve the optimal capacitor placement problem. This ACSA is a relatively new meta-heuristic for solving hard combinational optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The ACSA was inspired from the natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. Therefore, through a collection of cooperative agents called ants, the near-optimal solution to the capacitor placement problem can be effectively achieved. In addition, in the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Through operating the population of agents simultaneously, the process stagnation can be effectively prevented. Namely the optimization capability can thus be significantly enhanced. Moreover, the capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints, which is commonly solved by employing mathematical programming methods, and will be solved using ACSA in this article. The proposed approach is demonstrated employing two application examples. Numerical results of a small-size example system show that the proposed method can achieve an optimal solution like the exhaustive search, but with much less computational burden. Also, this proposed method is superior to some other methods adopted herein in terms of power loss and costs.