## 1 Introduction  
In an environment marked by the growing demand for sustainable energy sources and the consequent reduction in dependence on conventional resources, island power systems stand at the forefront of innovation. Their geographical isolation imposes the need to import fossil fuels for electricity generation and internal transportation, thereby driving the exploration of autonomous and environmentally friendly energy solutions [1, 2]. The progressive integration of renewable sources, such as photovoltaic (PV) and wind power (WP) systems, has represented a transformative step towards energy self-sufficiency [3, 4]. However, the intermittent nature of these sources and their remote locations pose substantial challenges to maintaining stability in island power grids [5]. The widespread adoption of electric vehicles (EVs) emerges as a viable strategy to mitigate WP and PVenergy fluctuations [5; 6; 7]. This viability hinges on the ability to program electric vehicle charging stations (EVCS) to charge or discharge an EV battery energy storage system (BESS) according to energy demand [6]. Efficiently coordinating the charging and discharging schedule of EVs, aiming to increase self-consumption and reduce power fluctuations, presents a significant challenge, especially due to the low adaptability of the ramp-rate (R-R) to respond to sudden changes in renewable energy production. In this context, the energy loss resulting from the necessary load adaptation to new production can disrupt key parameters of the power grid. This study focuses on this issue, proposing an innovative method to reduce WP and PV fluctuations using the EV BESS in an off-grid system based on fuzzy logic and the R-R technique. Frequency and voltage stability in a power system are critical for optimal operation, especially in isolated systems with limited robustness. For this reason, some electric distribution companies impose limits on the ramp-rate of PV or WP, as described in [7; 8], setting a maximum R-R of 10%/min to prevent destabilization of the power grid. This challenge is exacerbated when a fleet of EVs connects to the grid randomly without proper coordination between generation and demand. EV charging can occur in locations such as parking lots, corporate facilities, public areas, or at home, according to [6]. This study explores two grid connection approaches: vehicle-to-grid (V2G) and grid-to-vehicle (G2V). EVs can be charged during off-peak hours and inject surplus energy back into the grid through their bidirectional power converters. There is extensive literature on power smoothing methods applied to renewable sources. For example, the authors in [9] introduce an innovative approach to mitigate wind power fluctuations by dynamically adjusting the time constant of a purposefully crafted adaptive first-order low-pass filter controlled by a fuzzy logic system. This optimization is achieved by integrating a supercapacitor-based energy storage system. The authors in [10] develop a delta control to limit the R-R of a wind farm, highlighting that these limitations can reduce its total energy performance by 18%. Despite the efforts of some wind or PV farms opting for the installation of energy storage systems (ESS) to avoid the reduction in energy productivity, as described in [11], ESS installation is perceived as expensive and environmentally unfriendly. Therefore, leveraging the EV BESS as a demand response mechanism becomes vital for smoothing energy instead of installing battery banks. Power fluctuations from renewable sources can cause premature aging of the BESS by subjecting it to intense charge/discharge cycles, as discussed in [12]. The authors in [13] employ a fuzzy logic algorithm for ESS energy allocation, where the fuzzy controller determines the filter cutoff frequency based on the state of charge (SoC) of the ESS. An analytical model seeking to calculate the available power capacity in a V2G parking lot is presented in [14].