Metaheuristic optimization of wind turbine farm siting in power grids: a comparative study of PSO and GA

Abstract

This paper addresses the optimal integration of wind turbines into distribution networks with the aim of reducing active power losses and improving voltage stability. Two metaheuristic optimization methods genetic algorithm (GA) and particle swarm optimization (PSO) are applied to determine the optimal siting and sizing of wind turbines in the IEEE 14-bus system. The problem is formulated as a multi-objective function combining loss minimization and voltage profile enhancement under standard network constraints. Simulation results using MATLAB/PSAT show that both algorithms improve system performance compared to the base case, with PSO providing superior loss reduction and voltage stability. Wind variability is represented through a Weibull distribution to reflect realistic operating conditions. The study demonstrates the effectiveness of metaheuristic optimization for renewable integration and highlights PSO’s stronger robustness. The work contributes a comparative evaluation of GA and PSO, supported by stability analysis and realistic wind modelling.