## 4 Discussion
### Future Work  
The scalability of the proposed smoothing methods to real low-voltage networks is feasible, although it requires specific adaptations to address the complexities of these environments. The experimental tests documented in this article were conducted in a low-voltage microgrid laboratory under controlled conditions and connected to the 22 kV public distribution network. This represents a first step toward operation in an actual low-voltage grid. To further scale these methods, it is necessary to improve the monitoring and control infrastructure by implementing advanced sensors capable of providing high-precision, real-time data to account for variables such as load profiles, solar irradiance, and weather conditions. Furthermore, fuzzy logic systems should be generalized to include additional variables, such as temperature and nonlinear load fluctuations, to enhance their flexibility and performance in diverse scenarios. The incorporation of more robust hardware, such as high-capacity PLCs, is also recommended to manage the increased complexity and ensure computational efficiency and system stability. Finally, thorough validation in dynamic environments with varying loads and weather conditions is essential to confirm the robustness and reliability of the proposed methods when applied to practical grids. These adaptations will enable the effective scaling of the smoothing methods, ensuringtheir applicability in real-world settings while maintaining grid stability and facilitating renewable energy integration. While this study has demonstrated the effectiveness of fuzzy logic-enhanced power smoothing methods in reducing photovoltaic power variability, the influence of sensor tolerances on the overall system performance remains an important aspect for further investigation. The proposed approach relies on real-time electrical measurements retrieved from the commercial three-phase BPCS80 inverter, which introduces inherent tolerances in voltage, current, and power readings. Given that the key operational variables influencing the system's response--such as power fluctuations and SoC--are directly obtained from these measurements, it is necessary to assess further how these tolerances may marginally affect the effectiveness of the proposed method in practical scenarios. A more detailed future study could focus on quantifying the exact impact of measurement uncertainty on power smoothing performance. This could be achieved by conducting statistical analyses to determine whether slight variations in sensor accuracy significantly influence the calculated variance reduction or energy delivery improvements. Additionally, experimental tests with alternative sensing equipment featuring lower measurement tolerances could be performed to compare results and validate whether sensor precision plays a more pronounced role in certain operating conditions. Furthermore, adaptive filtering techniques or real-time data correction algorithms could be explored to minimize the influence of sensor tolerances and further refine the accuracy of power smoothing adjustments. Integrating advanced state-of-charge (\(SOC_{LI}\)) management strategies into the proposed approach have the potential to enhance energy efficiency and extend battery lifespan significantly. One promising avenue involves the use of artificial intelligence (AI)-based predictive models to anticipate charging and discharging patterns, enabling optimized battery operation within the ideal range of 20-80% to prevent overcharging and deep discharges. Additionally, dynamic SoC control systems could adaptively adjust upper and lower SoC limits in response to real-time system conditions, maximizing operational efficiency while safeguarding battery durability. Implementing advanced charging techniques, such as constant current and constant voltage control, would further reduce degradation by precisely regulating charging rates.