## 4 Conclusions  
This review synthesized the findings of 66 selected studies focused on smart microgrid management, organizing the analysis around four guiding research questions (RQ1-RQ4). Each question helped structure the review and identify areas of convergence as well as limitations in the current body of knowledge. For RQI, which examines energy storage configurations, most studies explored hybrid approaches involving batteries, hydrogen storage, and pumped hydro systems. While some of these configurations show technical viability in simulation environments, their deployment remains constrained by cost-performance trade-offs, particularly in isolated or infrastructure-limited regions. Further work should examine how these configurations behave under variable load profiles and develop multi-criteria decision models that account for degradation, spatial constraints, and resource availability. RQ2 concerns control strategies. The findings include both centralized techniques such as model predictive control, and distributed schemes including agent-based models. Some studies demonstrated potential in combining these approaches within layered control architectures. However, technical challenges persist regarding convergence speed, fault tolerance, and consistency in distributed decision-making. More experimentation is needed with real-time hardware emulation and uncertainty quantification in these control layers. Regarding RQ3, which involves the use of artificial intelligence and predictive models, tools such as reinforcement learning, deep neural networks, and hybrid forecasting techniques were frequently employed. These models were applied to areas such as load prediction, energy dispatch, and anomaly detection. Nonetheless, limitations appear in model transparency, generalizability, and training data availability. Current efforts would benefit from applying explainable AI principles and evaluating algorithmic performance on standardized open datasets shared under privacy-preserving frameworks. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Reference** & **Methodology/Model** & **Main Contribution** & **Limitations/Gaps** \\ \hline
[13] & MPC for energy dispatch & High dispatch precision in MGs & Low flexibility in centralized schemes \\ \hline
[19] & Adaptive distributed control & Scalable/fault-tolerant under uncertainty & Requires real-time communication \\ \hline
[15] & Hybrid ESS optimization & Operational metrics for hybrid BESS & Integration/cost complexity \\ \hline
[49] & Reinforcement learning (RL) & Improved fault detection via AI & Risk of overfitting, low explainability \\ \hline
[47] & Battery–SC hybrid storage & Efficiency improvements in BESS & Complex control and maintenance \\ \hline
[48] & Lifecycle-based storage planning & Multi-objective & Scalability in dynamic conditions \\ \hline
[20] & Standardization protocols & Highlighted comm. limitations in MGs & Lack of validated real deployments \\ \hline
[42] & Explainable AI (XAI) & Reliable and transparent diagnostics & High computational burden \\ \hline
[30] & Blockchain for trans. security & Secure decentralized energy exchange & Unclear regulatory standards \\ \hline
[34] & P2P energy trading platforms & Enabling distributed market models & Scalability/user engagement issues \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of key studies in smart microgrid research. RQ4 addressed challenges that affect scalability, reliability, and data security in smart microgrid environments. Issues identified include fragmented cybersecurity practices, lack of interoperability standards, and incomplete frameworks for assessing operational resilience. Ongoing research would gain from defining minimum acceptable thresholds for secure communication, exploring recovery-oriented design metrics, and validating coordination protocols in mixed-vendor system architectures.