Optimal Power Flow through Artificial Intelligence Techniques

Abstract

Context: The integration of optimization methods into the various processes carried out by an electric power system seeking energy efficiency have led to satisfying results in the reduction of consumption as well as in terms of technical losses, security increase and system reliability. Objective: The purpose of this article is to identify a method offering the best optimization outcome for the power flow of an energy distribution system with 10 nodes at 13.2 kV. Methodology: The results of voltage profiles are presented for a 10-node energy distribution system using the Newton Raphson method. Afterward, the system is optimized using genetic and ant colony algorithms. Results: Their implementation determined that the sum of the potential differences of distribution lines is notably reduced with the genetic algorithm. However, the ant colony optimization code takes less time to run and has a lower number of iterations. Conclusions: The most efficient optimization is achieved with the genetic algorithm since the evolution of the population shows better optimization levels in comparison to the ant colony algorithm. Financing: Universidad Francisco José de Caldas and Colciencias