artificial bee colony algorithm (abc)

Due to the increasing load growth and expansion and significant changes in structure of electricity distribution networks, the issues related to the optimization of these networks face many challenges. Optimal placement and capacity of Distribution substations and their feeder networks are among the important issues of optimization of distribution networks from technical and economic viewpoints. In this paper, considering two important factors including loss reduction and improvement of reliability in distribution networks, a new multi-objective function was presented for optimal placement of location and capacity of Distribution substations based on their service area. The proposed objective function includes all construction and operation costs of equipment, loss costs, and reliability index (Energy Not Supplied). The Artificial Bee Colony (ABC) algorithm was applied to optimize the proposed objective function with the purpose of determining the optimal placement and capacity of substations of two sample distribution networks. A comparison of results obtained from optimization of the proposed objective function by the Artificial Bee Colony (ABC) algorithm with results of other algorithms in two sample networks indicates the efficiency and effectiveness of the proposed algorithm and objective function.

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation sources and capacitor banks in parallel are increased. In this paper a modified multi-objective artificial bee colony (MABC) algorithm to solve the optimal placement and sizing of distributed generation and shunt capacitor is proposed. The objectives consist of minimization of costs, active losses, voltage deviation and improved voltage stability index incorporated with different load models. In the MABC, the bees adjust their trajectories based on the solutions also mutation and crossover operators are used to enhance the proposed algorithm. The proposed algorithm uses an external repository to save founded Pareto optimal solutions during the search process and fuzzy decision making process is employed to extract one of the Pareto-optimal solutions. The proposed algorithm is tested on the 69-bus IEEE test system and the results demonstrate the applicability and effectiveness of the proposed approach compared to that of other algorithms.