  With the decline of fossil fuel reserves and the escalating global average
temperature, the quest for environmentally friendly and renewable energy
sources has gained significant momentum. Focus has turned to wind and
photovoltaic energy, but their variable inputs necessitate energy storage for
reliable power. Economic viability of hybrid renewable power requires
meticulous optimization of generating units to ensure uninterrupted and
efficient energy production. This paper presents an optimal sizing approach for
a Wind-Photovoltaic-Biogas-Battery system using a single objective optimization
(SOO) method. A comprehensive comparative analysis is conducted, evaluating the
convergence speed and objective mean (for minimization) of seven metaheuristic
optimizers: Particle Swarm Optimization (PSO), Aquila Optimizer (AO), Pelican
Optimization Algorithm (POA), Dandelion Optimizing Algorithm (DOA), Gazelle
Optimization Algorithm (GOA), Zebra Optimization Algorithm (ZOA), and Osprey
Optimization Algorithm (OOA). The results demonstrate that the Pelican
Optimization Algorithm (POA) outperforms other existing algorithms, exhibiting
faster convergence and lower objective mean.
