Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains diesel generator, photovoltaics (PV), wind turbine (WT), and batteries as storage system. The proposed HRES aims electrify remote region in northern Saudi Arabia based on annualized cost (ASC) minimization power reliability enhancement. To differentiate evaluate performance, BO was compared four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), genetic algorithm (GA), butterfly optimization (BOA), terms of worst solutions captured, mean, convergence rate, standard deviation. obtained results reveal efficacy other algorithms where it achieved solution with lowest ASC (USD 149,977.2), quick time, fewer oscillations, followed by BOA 150,236.4). Both BBBC GA failed capture global had high time. addition, they deviation, which revealed their were more dispersed obvious oscillations. These simulation proved supremacy comparison algorithms.