Optimal Power Flow Solution Using Artificial Bees Colony Algorithms

摘要

This paper illustrates an application of Artificial Bees Colony (ABC) algorithms to optimal power flow (OPF) problems in comparative with some effective mathematical and evolutionary optimization methods. The ABC is one of swarm intelligences, which was just developed since 2005. This algorithm is to simulate the intelligent behaviour of honey bee swarms. It is a population-based search method and simply employs only common control parameters such as colony size and maximum generation number. The artificial bees are created to discover places of food sources with high amount of nectar. At the end of ABC searching procedure, the food source with the highest nectar is found to represent the global optimum. The proposed algorithm was tested with four mathematical test functions (simple quadratic function, Rosenbrock's function, circle function and Bohachevski's function) and five standard IEEE test power systems (6-bus, 14-bus, 30-bus, 57-bus and 118-bus test systems). The test power systems were divided into two cases. The first test case was given by applying a quadratic function to generators' fuel-cost curve whereas a non-smooth fuel-cost function was assigned to the second. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA) and artificial bees colony (ABC) were carried out. As revealed from the simulated results, the effectiveness of the ABC algorithm for solving OPF problems was confirmed.