Multi-Objective Optimisation Technique for Optimum Allocation of DG in Distribution Systems Using Weight Factors

Abstract

Governments and policymakers have started paying close attention to distributed generation (DG). Recognition of suitable site(s) and size(s) for these generators can increase network performance. Genetic algorithms (GAs) and particle swarm optimisation (PSO) are presented in this study to determine the appropriate supply of distributed generators with the goal of reducing overall active and reactive losses and improving network voltage regulation. The backward/forward sweep approach was modified to allow for the examination of the iterative process's convergence. IEEE 33-bus networks were used in this research. To determine the weight factors associated with each aim, a specific approach was proposed and used. To eliminate human decision-making influence in the optimisation process, several optimisation objectives were scalarised using determined weight factors. Compared with the stated values in other literature, GAs outperform the PSO method. Furthermore, the used technique increased the number of iterations and the standard deviation in the studied circumstance.