This paper presented an optimal design of a grid-independent hybrid renewable energy system (HRES) that comprises Photovoltaic, Biomass, Hydrogen Fuel Cell, and battery storage. Renewable energy-based system have been endorsed for remote off-grid communities electrification. However, it is difficult to design an optimal hybrid energy system due to the stochastic resource nature, load variation, and high cost of renewable components. The sizing of components for the proposed HRES is determined through the application of an innovative metaheuristic optimization technique called salp swarm algorithm (SSA). Addressing the limitations of the salp swarm algorithm, which include low precision, optimization dimension and convergence rate, a modified version of the salp swarm algorithm (SSA), known as the Levy and sine cosine operator-based (LSC-SSA), was introduced. The proposed algorithm is compared with standard SSA and Genetic Algorithm (GA). The primary goal of the research is to reduce the annualized cost of the hybrid system, whilst taking into account the reliability constraint. The novelty of this research lies in its approach to enhance the performance of a HRES by optimizing its size and energy management strategy (EMS). It is achieved by employing a combined framework that integrate the proposed LSC-SSA into the supervisory EMS. The potential benefits of this approach include reducing the cost of energy and the annualized system cost. The comparative results validate that the LSC-SSA surpasses the standard SSA, and GA algorithms examined, by realizing significant cost reductions amounting to $82,023, and $202,127 respectively. Additionally, the outcome indicates that, the LSC-SSA offers the least cost of energy (COE) of $0.927/kWh, in comparison with the COE values of $0.931/kWh for SSA and $0.949/kWh for GA, which are higher. Furthermore, the results indicate that the applied supervisory EMS has effectively assisted the establishing an eco-friendly and economical energy system.

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid energy solutions to ensure reliability, sustainability, and economic viability. However, optimizing the design of hybrid renewable energy systems, particularly those incorporating both hydrogen and battery storage, remains challenging due to system complexity and fluctuating energy trading conditions. This study addresses these gaps by proposing a novel framework that combines the Chimp Optimization Algorithm (ChOA) with a rule-based energy management strategy (REMS) to optimize component sizing and operational efficiency in a grid-connected microgrid. The proposed system integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), hydrogen storage, fuel cells (FC), and battery storage (BAT), while accounting for seasonal variations and dynamic energy trading. Each contribution in the Research Contributions section directly addresses critical limitations in previous studies, including the lack of advanced metaheuristic optimization, underutilization of hydrogen-battery synergy, and the absence of practical control strategies for energy management. Simulation results show that the proposed ChOA-based model achieves the most cost-effective and efficient configuration, with a PV capacity of 1360 kW, WT capacity of 462 kW, 164 kWh of BAT storage, 138 $H_2$ tanks, a 571 kW ELZ, and a 381 kW FC. This configuration yields the lowest cost of energy (COE) at $0.272/kWh and an annualized system cost (ASC) of $544,422. Comparatively, the Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) produce slightly higher COE values of $0.274, $0.275, and $0.276 per kWh, respectively. These findings highlight the superior performance of ChOA in optimizing hybrid energy systems and offer a scalable, adaptable framework to support future renewable energy deployment and smart grid development.