## 1 Introduction
The integration of centralized and decentralized strategies generates an HMG environment that optimizes the advantages of both methods. This incorporation ensures a harmonious  
Figure 1: Integration of centralized System approach with local control system. blend of grid-wide coordination and local autonomy, leading to resilient, efficient, and sustainable energy management. The hybrid system is illustrated in Figure 3.  
A comprehensive review of the microgrid system's control and energy management method has been conducted [9]. This offers valuable insights for improving the operation and management of MG systems and advancing the transition toward sustainable energy sources [10; 11]. The multiphase stacked interleaved buck converter is a power electronic converter used for controlling the power flow in many applications, including green  
Figure 3: Hybrid system configuration. Figure 2: Local control with Decentralized System approach. hydrogen production [12]. The converter consists of multiple interleaved buck converters stacked in parallel, which provides fault tolerance and high efficiency [13]. A fuzzy logic controller is employed to address the system's nonlinear dynamic features, providing decision-based forecasts for power and voltage [11]. For the efficient planning and design of a hydrogen-based microgrid, numerous case studies have been conducted, exploring various types of DERs through the use of heuristic algorithms [12]. These studies aimed to reduce the cost and enhance the efficacy of the system. PSO and constraint methods were applied to reduce the cost and unmet load [13]. A comparison of several optimization techniques was conducted, with the inclusive findings authorizing that the PSO method emerged as the utmost effectual and practical optimization method for hydrogen-based MG system planning [14; 15; 16]. These assessments were based on criteria such as minimizing costs, maximizing consistency, and exhibiting more robust convergence attributes compared to other optimization methods. In this paper, a heuristic optimization approach, the PSO algorithm, is anticipated to determine the optimal capacity of RESs within an MG system [17]. However, the intermittent and unpredictable nature of renewable sources such as photovoltaic (PV) and wind pose a challenge for their integration into power grids [18]. One of the solutions proposed to address this challenge is incorporation of a hydrogen storage system to enable backup power supply for renewable hybrid energy systems (RHES) [19]. Combining these strategies can enable efficient and reliable energy management in RHES with hydrogen backup. It also identifies areas for future research, including developing more accurate and reliable models that can capture the dynamics of RHES and hydrogen storage systems. The optimization algorithm used in this approach can be based on various methods, such as linear programming, quadratic programming, or mixed-integer programming [20; 21]. The significant advantage includes modest algorithm operation, less parameters tuning, and a reckless convergence rate. The primary contributions of this article are outlined below, derived from a thorough critical examination of prior research conducted by numerous scholars presented in Table 1.  
1. The proposed research design offers notable returns over traditional microgrid design approaches, including the following: Emphasizing the significance of proposed design and energy management optimization in MG systems involves developing a comprehensive cost-driven approach to optimize DERs. This approach considers the integration of hydrogen (\(H_{2}\)) storage and electrolyzer within a grid-tied operation mode. 2. The problem formulation integrates economic and environmental considerations, particularly focusing on the utilization of an electrolyzer for hydrogen (\(H_{2}\)) production to FCs. The analysis explores the ideal scale for various resources in the energy system operation, emphasizing the potential for greater financial returns through increased integration of RESs. 3.