Opposition-based elitist real genetic algorithm for optimal power flow

Abstract

Optimal power flow (OPF) algorithms are widely used in the operation of modern power systems, and numerous variations and enhancements have been developed over the last four decades. Yet, with decreasing time intervals and increasing complexities introduced by market policies and stochastic inputs, the need for further improvements in speed and performance of OPF algorithms persists. This paper proposes using elitist real genetic algorithm (ERGA) and opposition-based elitist real genetic algorithm (OB-ERGA) to solve the OPF problem. Also, inverse transformation and exponential transformation are implemented to investigate the convergence performance of the proposed methods. The combination of the OB-ERGA, ERGA and the fitness functions is tested on the IEEE 30-bus system to determine the effectiveness of the proposed approaches. The results are presented and compared with the existing evolutionary algorithms in the literature.