## 1 Introduction
However, the central challenge in this context lies in the uncertainty associated with the amount of power and energy each electric vehicle can supply. Ref. [15] describes a fuzzy controller that sets the battery power, considering SoC and mismatch power. In this context, the unit with the highest SoC injects more energy and absorbs less. Some studies, such as [16; 17], focus on determining the optimal size of the BESS used in EVs. In [18], a learning action automaton is implemented and validated, including two WPs, two battery exchange stations for EVs, and distributed EV penetration. The simulation results indicate interaction between EVs in a mixed-modal environment. The most commonly employed conventional strategies are based on applying filtering techniques using ESS to establish the reference power intended for compensation. Information on design approaches for fluctuation attenuation using ESS in combination with various types of filters, including the first-order low-pass filter, the second-order low-pass filter, and especially R-R filters, has been widely disseminated [19; 20; 21]. The ramp-rate (R-R) technique limits the speed at which power can increase or decrease. Although it is an easy, simple, yet effective solution for smoothing, choosing the appropriate ramp-rate remains a significant research problem. Most studies focus on reducing power fluctuations from renewable sources or optimizing energy using EVs as storage, but few studies simultaneously analyze both strategies, especially in isolated systems. A promising solution is to adjust the ramp-rate in real-time based on the changing conditions of renewable sources and EVs, as demonstrated in [22], where they adjust the time constant of a low-pass filter in real-time to reduce computational load due to ramp-rate adjustment of various sources and intermittent loads. Additionally, researchers in [13] demonstrate that using fuzzy logic algorithms improves the designation of reference powers for a BESS. In [23], two high-frequency attenuation filters calculate the powers associated with a BESS, and a fuzzy controller sets the filter cutoff frequency based on SoC. Notably, these approaches do not consider the direction of energy variations or application in EVs. Primarily, these methodologies are centered around validating the efficacy of smoothing techniques and involve meticulously utilizing the available ESS energy. However, when dealing with an electric vehicle, it becomes crucial to ensure that, following the provision of power smoothing services, the battery remains fully charged when the user unplugs their vehicle from the EV charging station. After a thorough review, some gaps in the literature have been identified that need to be addressed, which are described below:  
* While ref. [24] presents a coordinated strategy of ESS and power smoothing, exploring and optimizing the use of stored energy in EVs is necessary, considering multiple renewable sources. * Investigate the specific impact of uncoordinated interconnection of electric vehicle (EV) fleets on island power systems, considering the variability in EV charging and discharging and its influence on network stability, complementing the study presented in [11]. * Analyze how to dynamically adjust the ramp-rate in real-time according to the changing conditions of renewable sources and EVs based on fuzzy logic to improve system efficiency and stability, inspired by [13,22]. * Explore how considering the direction of energy variations can influence the management of RES-EV hybrid systems for more precise planning. * Careful consideration is needed to compromise the demands of power smoothing while maintaining EV batteries at adequate state of charge levels for users' future needs. * Investigate the optimal coordination between V2G-power smoothing modes to maximize efficiency and minimize impact on the grid.