## 2 System Model  
The system explored in this paper is a hybrid microgrid that integrates a Proton Exchange Membrane Fuel Cell (PEMFC), an alkaline electrolyzer, hydrogen storage tanks, Battery Energy Storage System (BESS), and PV producers. The PEMFC and electrolyzer in this paper are based on the specifications of a Nexa 1.2 kW, 26 V, and 46 A PEMFC intended by Ballard [29] and \(H_{2}\) Igen 300/1/25, 5 kW, 43 V, and 120 A alkaline electrolyzer [30]. A hydrogen-based microgrid introducing both RESs production and hydrogen storage can gain from a multi-stage power and energy management strategy to optimize its performance and ensure resource effectiveness [31]. The microgrid is modeled using the MATLAB/Simulink software [32].  
The RESs considered in this study are photovoltaic panels and wind turbines. In addition, the microgrid has a battery storage system, supercapacitor, and hydrogen storage system. The objective of this step is to optimize the dispatch of RESs and energy storage components to minimize the cost of energy and carbon emissions. Where is a possible approach for such a strategy to design the complete model and its optimization with respect to the operational cost of the system? The convergence level of voltage is considered as well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **EMS Strategy** & **Contribution** & **Constraints** \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{\begin{tabular}{c} Math-Flame \\ Optimization (MFO) \\ \end{tabular} } & An Integrated Management System (IMES) has been devised to oversee energy management. & Taking power and cost under consideration while taking much time about to more than 150 min. \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{[23]} & \multirow{4}{*}{\begin{tabular}{c} African Vulture’s \\ Optimization (AVO) \\ \end{tabular} } & Techno-economic goals have been addressed comprehensively. & Battery performance and operational cost. \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{[25]} & \multirow{4}{*}{\begin{tabular}{c} Marine Predator \\ Algorithm (MPA) \\ \end{tabular} } & Introduces a centralized control system designed to & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{[26]} & \multirow{4}{*}{\begin{tabular}{c} SAC and XGBoost \\ \end{tabular} } & A methodology has been devised that leverages the & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{[27]} & \multirow{4}{*}{\
\begin{tabular}{c} Quantum Teaching \\ Learning-Based \\ Optimization (QTLBO) \\ \end{tabular} } & A daily optimal scheduling framework has been & & Power balancing and \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{4}{*}{[28]} & \multirow{4}{*}{
\begin{tabular}{c} Mixed Integer Linear \\ Programming (MILP) \\ \end{tabular} } & This study introduces an approach for storage systems, & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comprehensive overview of the existing literature.