This paper presents compared performances of three metaheuristic algorithms in determining the cost of hybrid renewable energy system. Using genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), the best affordable sizes of solar photovoltaic array, battery bank, and a minimum-rated diesel generator that could be hybridized to meet the demand of a community in Southwest Nigeria were determined. Load profile, solar radiation, and temperature data were employed as required inputs, and the parameters of the algorithms were properly set to ensure the best result. Bonferroni– Holm method was deployed to ascertain the statistical significance among the algorithms. It was found that ABC produced the best configuration comprising 427 numbers of solar photovoltaic panels, 19 battery units, and 163.2 kW-rated diesel generator. With this, a total annualized cost of $167 284 and 0.2443 estimated cost of energy were obtained. These were the lowest when compared with PSO and GA. The t-test between PSO and ABC are both 5.83 × 10−10 < 0.01666667, between ABC and GA are 6.09 × 10−6 0.01666667 and 9.13 × 10−1 > 0.05. PSO/ABC and ABC/GA groups are clarified significant, while GA/PSO group is insignificant; post hoc test reveals that ABC produced the best result. Hence, a reliable and sustainable power supply at a reduced cost is guaranteed for the community.
Cite this article as: T.O. Ajewole, O. Oladepo, K.A. Hassan, A.A. Olawuyi, O. Opeyemi, Comparative study of the performances of three metaheuristic algorithms in sizing hybrid-source power system. Turk J Electr Power Energy Syst, 2022; 2(2), 134-146.