## 1 Introduction  
The current literature extensively examines the reduction of fluctuations in the electrical grid caused by the intermittent behavior of renewable sources [1, 2, 3, 4]. For instance, studies such as [5] have evaluated spline-based control methods for smoothing photovoltaic (PV) power, demonstrating that effective smoothing effects can reduce the required capacity of energy storage systems (ESS). Additionally, advanced filters like the Savitzky-Golay (S-G) filter have shown significant improvements. Research such as [6] has compared moving average (MA) and low-pass filter methods. Another promising approach involves moving regression (MR) filters with state of charge (SoC) control. Studies like [7] have shown that the MR filter can reduce PV power variability without increasing ESS capacity, making it more effective than other methods, such as MA and median filters. Improving energy quality is crucial in microgrids (MG) and isolated systems [8, 9, 10, 11]. Research such as [12] has proposed smoothing methods using supercapacitor ESS. Studies like [13] have used smoothing techniques such as MA and ramp rate control to determine optimal battery capacity, providing empirical models for project feasibility assessment. The application of MA filters has also extended to wave energy conversion plants, as described in [14]. Research such as [15] uses enhanced optimization algorithms to tailor hybrid ESS to solar power variations, highlighting the effectiveness of adaptive control in energy management. Power ramp compensation is also essential in MG with high PVpenetration. Studies like [16] have developed compensation strategies based on multilevel ESS, using ramp rate control methods to smooth solar power fluctuations and meet rapid load demands. Another promising approach to ensure network stability is using linear programming techniques with priorities. A recent study proposed a power management method based on linear programming for a multi-feeder ultra-fast DC charging station. This approach maximizes the efficiency of renewable resources, local storage solutions, and electric vehicles to minimize their grid impact while also observing charging priorities and power flow limitations. Simulation results confirm that this approach effectively enhances grid stability amidst significant fluctuations in load [17]. Furthermore, optimizing hybrid power generation plants, as in [18], has shown how the optimal combination of wind, solar, and ESS can reduce output fluctuations and enhance system reliability in grid-connected systems. Thus, energy quality is notably enhanced [19; 20; 21]. Implementing Fuzzy Logic Controller (FLC)-based energy management systems has been highlighted in the literature for its effectiveness in smoothing power profiles in electro-thermal MG. A recent study [22] designed an EMS using FLC to smooth the power profile in a residential MG. Using year-long measured data, they demonstrated an 11.4% reduction in peak network power absorption and a significant decrease in power profile ramp rates compared to previous studies. Study [23] proposed a system integrating a PV Battery Energy Storage System (BESS) with doubly fed induction generator systems, employing an FLC optimized by moth-inspired optimization algorithms to maximize active power and smooth power transfer to the grid converter. Combining fuzzy logic-based maximum power point tracking (MPPT) with active power management offers fast and accurate tracking, reducing output power fluctuations and improving system efficiency compared to classical methods, as demonstrated in [24; 25]. Additionally, using FLC for MPPT and voltage compensation, authors in [26] demonstrated notable effectiveness in enhancing energy efficiency and power quality across various operational conditions. Managing PV/diesel/BESS hybrid systems in isolated systems has been optimized using fuzzy logic-based power management systems (PMS) [27].