Optimal power flow solutions through multi-objective programming

Despite the progress achieved in the development of optimal power flow (OPF) programs, most of the solution techniques reported in the literature suffer from the difficulty of dealing with objective functions of different natures at the same time. However, the need for alternative power network solutions during the planning of a power system operation requires the optimization of several performance indexes simultaneously. In this study, attention is focused on the modelling and solution of a parameterized multi-objective OPF problem. The proposed OPF model combines two classical multi-objective optimization approaches, the weighted sum and the constraint methods, through a parameterization scheme to manipulate the objective functions. This parameterization allows relaxation of the constraints imposed to handle the performance indexes, to facilitate the convergence of the iterative process. The resulting optimization problem, which ultimately is a mono-objective optimization problem, is solved through the nonlinear version of the predictor–corrector interior point method. The IEEE 24-bus test system was used to obtain the numerical results of the computational simulation.

► The optimal power flow problem is formulated as a multi-objective optimization problem. ► The weighting and the constraint methods of multi-objective programming are combined through a scheme of parameterization. ► The resulting OPF problem is solved through the nonlinear version of the predictor–corrector interior point method. ► This strategy allows to deal with objective functions of different nature, with flexible additional constraints, as desirable in the operation planning of the power system. ► Numerical results obtained with the IEEE 24-bus test system is used to illustrate the main features of the proposed approach.