## 1 Introduction  
In the dynamic landscape of technology, microgrids play a crucial role in efficient energy management. DC microgrids, known for minimal losses and seamless integration with energy storage [1, 2, 3], are at the forefront. These systems, encompassing sources, control systems, loads, and energy storage, work collaboratively to mitigate the risk of power outages [4]. In [5], a method based on the battery state of charge (SOC) was employed to determine the power generated or consumed within a DC microgrid. The study incorporated battery management and switching control strategies to regulate the energy level of the DC microgrid. The authors of [6] emphasize the potential of DC microgrids to address challenges in coordinating distributed renewable energy, especially in mitigating adverse effects. However, integrating diverse power sources and storage presents chal lenges in voltage control and power-sharing, requiring effective control techniques, as highlighted in [4]. In the realm of microgrids, a key research focus lies in controlling and stabilizing their operation and performance. [PERSON] et al. [7] introduce a stabilizing controller for DC microgrids, employing a decentralized approach to enhance efficiency by considering various distributed energy resources (DERs) and energy storage elements such as EVs. [PERSON] et al. [8] proposed a nonlinear decoupling method to address transient stability challenges in low-inertia DC microgrids. In [9], a bi-directional interlinking DC-AC converter utilized the AC frequency as a reference value for DC voltage and employed current feedforward control to align the DC voltage with the AC frequency, enhancing stability and controllability in low-inertia DC microgrid systems. The study by [10] focuses on the design and analysis of controlling and optimizing the operation of a DC microgrid with DERs such as photovoltaic (PV). It examines efficiency improvements through a bi-directional DC/DC converter, contrasts various maximum power point tracking (MPPT) techniques, and reports the results of stability analysis using the Lyapunov function, discussing a variety of controllers including fuzzy, MPC, and Robust [11]. Recent studies have increasingly focused on fuzzy logic, PSO, and their combination [12; 13; 14]. The study explains how DERs like diesel engines, micro turbines, fuel cells, photovoltaics (PVs), and small wind turbines are utilized in microgrids. Effectively managing microgrids involves controlling and operating these DERs along with adjustable loads and storage devices such as flywheels, energy capacitors, and batteries. This coordination is crucial for the overall functioning of a microgrid. Leveraging DERs like PV technology presents a viable and economically efficient solution to mitigate pollution and warming, especially in microgrid applications [15; 16]. Authors in [17; 18; 19] emphasize the significance of these energy sources for microgrid applications. The authors of [20] highlight the potential superiority of photovoltaic-based DC microgrids over traditional AC grids. Due to the unpredictable nature of renewable resources, effective storage solutions like batteries and energy storage systems play a crucial role in enhancing microgrid efficiency [21; 22]. Additionally, [23] emphasizes the significance of energy storage systems, especially EVs, in distributed systems to conserve energy and address the unpredictability of renewable energy sources, while integrating EVs into microgrids is proposed to strengthen energy storage, concerns about battery lifespan exist [24]. [PERSON]. [25] presented a technique, called Integrated Battery Life Loss Modeling and Anti-Aging Energy Management (IBLEM), to handle battery aging in battery energy storage systems (BESSs) like EV batteries. The authors of [26] proposed a PV-embedded series DC electric spring (PVES) to reduce battery storage in DC microgrids with significant PVs.