Integrating multiple slack bus operations and metaheuristic techniques for power flow optimization.

The increasing complexity of modern energy grids amplifies the importance of realistic power flow studies in power system analysis. This study implements a Multiple Slack Bus Operation (MSO) framework to enhance the realism and efficiency of optimal power flow (OPF) analysis. This paper introduces a comparative evaluation of three metaheuristic algorithms: particle swarm optimization (PSO), cuckoo search algorithm (CSA), and grey wolf optimization (GWO) within the MSO framework. These algorithms are assessed based on their effectiveness in minimizing system loss, optimizing line loading, adjusting the angle of the generator voltage, and optimizing the generation distribution. Using the Reduced Nordic 44 model and the IEEE benchmark test systems in various load conditions, the findings reveal that the GWO algorithm, when integrated with the MSO framework, achieves the most significant reduction in total system losses. The implementation of MSO alone reduced system losses by 5%, and its combination with GWO led to an additional 8.3% decrease. This study investigates the application of metaheuristic algorithms within a multiple slack bus context, highlighting their potential to enhance power network efficiency and suggesting broader applications for future power flow optimization strategies.