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Tecnura
Print version ISSN 0123-921X
Abstract
HERNANDEZ, César; SANCHEZ-HUERTAS, William and GOMEZ, Víctor. Optimal Power Flow through Artificial Intelligence Techniques. Tecnura [online]. 2021, vol.25, n.69, pp.150-170. Epub Nov 15, 2021. ISSN 0123-921X. https://doi.org/10.14483/22487638.18245.
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 that provides the best optimization outcome for the power flow of an energy distribution system with 10 nodes at 13,2 kV.
Methodology:
The results obtained from the voltage profiles are presented for a 10-node energy distribution system using the Newton-Raphson method. Afterwards, the system was optimized using genetic and ant colony algorithms.
Results:
The implementation of these techniques 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, given that the evolution of the population shows better optimization levels in comparison with the ant colony algorithm.
Financing:
Universidad Francisco José de Caldas and Colciencias
Keywords : ant colony optimization; artificial intelligence; genetic algorithm; optimization; power system.