Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability

Abstract In this paper, a hybrid genetic algorithm with particle swarm optimization (GA-PSO) is applied for the optimal sizing of an off-grid house with photovoltaic panels, wind turbines, and battery. The GA-PSO is one of the most powerful single-objective optimization algorithms. In the other hand, the multi-objective PSO (MOPSO) can solve the optimization problems considering all objectives without transforming them. Minimizing the total present cost including initial cost, operation and maintenance cost, and replacement cost with satisfying the load demand is the main goal of this study. In this optimization problem, the considered reliability factor is a loss of power supply probability, which specifies the subtraction of the load power and generated power. The wind velocity, solar irradiance, and load demand are simulated in 12 months of a year by the HOMER software for a suburbs of Tehran. Then, the optimal size of PV and WT are obtained with both GA-PSO and MOPSO methods, and compared with the HOMER results. At last, the strengths and weaknesses of each method are explained. The results show that the proposed approach with 0.502 of the levelized cost of energy for the PV/WT/BAT system has the best result through the compared methods.