Abstract

This paper illustrates an application of adaptive tabu search (ATS) to optimal power flow (OPF) problems in comparison with some effective mathematical and evolutionaryoptimization methods. Although, the ATS was originally developed for solving a combinatorial optimization problem whose parameters are discrete, it has the ability to handle continuous variables by treating them as discrete ones with a very small variable step-size to gain accuracy. The proposed algorithm was tested with 9-bus and 300-bustest systems to represent a small-scale and a comparatively large-scale power system, respectively. Each test power system was challenged by performing three test 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. In addition, the system voltage profile was considered and set as the objective function to be minimized in the last test case. The comparisons among solutions obtained by sequential quadratic programming (SQP), evolutionary programming (EP) and the ATS were carried out, from which satisfactory results and the selection of solution methods to OPF problems were summarized.

Key words: Optimal power flow problem, sequential quadratic programming, evolutionary programming, adaptive tabu search, quadratic fuel cost, non-smooth fuel cost.