Comparison of Genetic Algorithm and Particle Swarm Optimization for DC Optimal Power Flow

In this paper, a comparative analysis is provided for Genetic Algorithm (GA) and Particle Swarm optimization (PSO) applied to DC Optimal Power Flow (OPF). The study was conducted on a 4-bus network with three generating units connected to three different buses and supplying loads at two distinct buses. The proposed approach focuses on optimizing the control variables for the DC OPF. By comparing the GA and PSO, this study aims to assess their effectiveness in improving the accuracy of the optimized objective functions. To validate the efficacy of this comparative analysis, a MATLAB-based code was developed and tested under various objectives, including fuel cost minimization, while considering line constraints. The results provide insights into the strengths and weaknesses of both GA and PSO in the context of the DC OPF.