Optimising Power Flow in Transmission Networks: A Comparative Study of Genetic Algorithm and Particle Swarm Optimisation Approaches

Abstract

The efficient operation of power systems is crucial for meeting growing electricity demands, but traditional load flow methods often struggle with complex and large networks. This study aims to investigate the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) in optimising load flow in the Etete 33 kV transmission network. The objectives are to evaluate the performance of GA and PSO in reducing power losses and improving voltage magnitude, compare their convergence behaviour, and determine the most effective approach. The study uses a base case load flow analysis to establish a benchmark, followed by GA and PSO optimisation. The results show that PSO outperforms GA in reducing power losses (94.27% reduction) and improving voltage stability. PSO's targeted reactive power injection strategy is more effective in minimising losses and maintaining voltage profiles within acceptable limits. The significance of this study lies in its potential to contribute to the development of more efficient power systems. The findings demonstrate the effectiveness of PSO in load flow optimisation, providing insights for power system operators to improve network performance. The study's results have implications for power system planning, operation, and control, enabling utilities to optimise their networks and reduce power losses. This study provides a valuable comparison of GA and PSO for load flow optimisation, highlighting the benefits of using advanced optimisation techniques in power system operation.