Optimal Power Flow (OPF) is a critical problem in power system operation and planning, aimed at determining the most efficient operational conditions of the system while respecting various operational constraints. Genetic Algorithms (GA), with their ability to solve complex optimization problems, have been increasingly employed to address the OPF problem. This study focuses on performing a parametric analysis to investigate the impact of selecting appropriate control and state variables on the efficiency and effectiveness of GA-based OPF solutions. Various combinations of control variables (such as generator voltages, active power generation, and reactive power generation) and state variables (such as bus voltages and branch power flows) are analyzed in this study. The results highlight how the selection of control and state variables influences the convergence rate, computational time, and solution accuracy of the genetic algorithm. A series of parametric studies are conducted to optimize the parameters of the genetic algorithm, including population size, crossover rate, and mutation rate, to improve the overall performance of the OPF model. The study demonstrates the significance of variable selection in achieving more efficient and practical solutions for power system optimization. The findings suggest that the choice of control and state variables plays a crucial role in balancing the trade-offs between solution quality and computational efficiency.

Optimal Power Flow, Genetic Algorithm, Control Variables

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