## 1 Introduction  
The exponential growth of electric vehicles (EVs) and the integration of renewable energy sources (RES) have transformed modern power systems, highlighting both opportunities and challenges. EVs provide unique energy storage capabilities, while RES promise a clean and sustainable energy supply. However, the intermittent nature of RES and the variable energy demand from EVs introduce significant power fluctuations, compromising the stability and quality of electrical grids [1]. Low-voltage networks, in particular, are increasingly adopting renewable technologies and Vehicle-to-Grid (V2G) systems, but these face pressing issues of stability and power variability. Such challenges demand advanced solutions. Techniques like fuzzy logic and hybrid energy storage systems have shown promise in mitigating power fluctuations and improving grid reliability [2]. Still, the variability in the state-of-charge (SoC) of storage systems and the complexity of modern grids require optimized strategies that balance energy quality, storage efficiency, and operational sustainability [3]. In this context, it becomes crucial to explore innovative approaches to power management and the efficient integration of EVs and RES into weak electrical grids, addressing the challenges of power fluctuations, stability, and energy management. The integration of EVs and related technologies, such as V2G, is transforming the energy landscape by addressing sustainability and stability challenges in modern power systems. Intelligent control and management systems are vital in this transformation. For instance, a fuzzy logic and artificial neural network-based controller has been proposed to manage communication between charging stations, EVs, and the grid, effectively reducing errors and improving operational stability [1]. Similarly, coordinated control strategies have been developed to mitigate voltage rise/drop issues at distribution nodes by employing fuzzy logic to generate reference signals and manage power flows at charging stations [4]. The hybrid approach of integrating EVs with renewable energy sources has proven effective in maintaining stable voltage profiles and minimizing active and reactive power losses in interconnected electrical systems [5]. In more specific applications, an adaptive strategy for stabilizing isolated grids through the integration of V2G and solar energy has shown promising results by reducing power fluctuations [2]. Furthermore, a differential evolution-based algorithm has been applied to V2G systems to balance energy costs and battery state-of-charge levels, leveraging photovoltaic (PV) generation forecasts [6]. Communication quality is also critical for the success of V2G systems. An analysis of communication systems' impact on grid node voltage highlights the importance of fuzzy logic controllers in environments with multiple charging stations [7]. In parallel, innovative algorithms such as the Fuzzy Discrete Squirrel Search (FDSS) have optimized charging capacity in networks with limited infrastructure, revealing the role of EVs as backup power sources during renewable energy shortages [8]. The development of fast-charging stations in DC microgrids has also been explored. Control methods such as sliding mode and fuzzy logic have demonstrated their capability to mitigate voltage sags while improving dynamic performance [9]. On the other hand, fuzzy logic-based control strategies have been designed to ensure efficient operation in stations integrating PV and battery storage, enabling active and reactive power transfer [10]. The implementation of V2G technology in microgrids addresses power quality challenges, such as total harmonic distortion (THD), through advanced fuzzy logic controllers that outperform traditional methods like PI controllers [11]. A more sophisticated approach integrates neural networks and optimization algorithms to manage battery charging and discharging, achieving consistent efficiencies of 97% under various conditions [12].