## 1 Introduction
It highlighted the reduction in carbon emissions while presenting comprehensive modeling and performance evaluation across diverse environmental situations. The study probably depended on particular environmental situations, thereby constraining the generalizability of the findings. It inadequately addressed economic viability, maintenance difficulties, or large-scale deployment concerns, which are essential for practical implementation. [PERSON] et al. [10] expanded the topic of smart energy networks by introducing sophisticated approaches for maximizing the integration of renewable energy and enhancing power generation and consumption predictions. It emphasized advanced computational methodologies, including IoT and multi-level reconfiguration frameworks for solar trackersand wind turbines, aimed at improving grid efficiency and sustainability. The project highlighted pragmatic applications, including measures to minimize energy waste and enhance distribution networks. Nonetheless, the constraints were possible difficulties in scalability, elevated implementation expenses, and the heightened complexity arising from the incorporation of sophisticated technologies. [PERSON] et al. [11] concentrated on the design and optimization of autonomous hybrid renewable energy systems that integrate wind, solar, and battery storage for irrigation purposes. The research assessed the technical and economic efficacy of these systems by examining various configurations under certain local conditions. Significant contributions encompassed the identification of suitable sizing solutions to guarantee reliability and cost-effectiveness, especially in areas with fluctuating renewable energy potential. The research emphasized the need for precise demand forecasting and localized resource evaluation in attaining sustainable energy solutions. Nonetheless, the research exhibited certain shortcomings. It mostly concentrated on particular case studies and did not thoroughly examine scalability for larger systems or varied geographical regions. Furthermore, it presumed consistent operational and maintenance expenses, disregarding possible variations during the system's existence. [PERSON] et al. [12] investigated the design of an off-grid hybrid renewable energy microgrid (HREM) specifically for rural agricultural areas. The study employed a modified multi-objective particle swarm optimization (MOPSO) method to optimize the integration of solar photovoltaic systems, run-of-the-river hydropower, battery storage, and diesel backup, aiming to balance cost, reliability, and environmental impact. It highlighted local economic advantages by incorporating the microgrid with agricultural technology to improve production, especially in abaca fiber processing, which is a vital regional endeavor. The study offered a framework for sustainable rural electrification, but it was constrained by its dependence on particular local data, the intricacy of its optimization algorithm, and the omission of alternate renewable sources such as biomass or wind. Moreover, its scalability to more extensive regions or metropolitan environments poses a hurdle. [PERSON] et al. [13] introduced a multi-objective optimization approach for the design of a hybrid photovoltaic, wind, fuel cell, and battery system. The optimization was predicated on three models: energy supply reliability, electricity efficiency, and the capital cost of the hybrid system. The Elephant Herding Optimization (BEHO) algorithm was employed to address the multi-objective optimization problem and was validated against various algorithms and benchmark functions. The primary objective was to identify the Pareto surface, encompassing a range of potential design choices to assist decision makers in achieving the global optimum solution. The final results demonstrated that the proposed method is a viable way for constructing the hybrid system. Consequently, they proposed a novel methodology for the design of a hybrid renewable energy system that decision makers might employ to achieve a global optimal solution.