## 3 Results and Discussion
### Economic and Resilience Analysis of Microgrid Systems
#### 3.7.4 Business Models for Resilient Microgrids and Financing Mechanisms  
Developing sustainable business models is essential to support the deployment of resilient microgrids. Models such as Energy-as-a-Service (EaaS) and microgrid leasing options help reduce upfront financial barriers for end-users while providing professional management and maintenance services [19; 55]. Financing mechanisms including green bonds, public-private partnerships, and performance-based contracts are increasingly used to support microgrid projects [17; 37]. These models improve access to capital, reduce financial risks, and enable monetization of resilience and ancillary services provided by microgrids [30; 36]. Blockchain platforms and decentralized marketplaces also introduce new possibilities for peer-to-peer energy trading and dynamic pricing models that encourage efficient energy use and investments in system resilience [32; 39]. Future research should explore how these business models can be aligned with regulatory frameworks to support sustainable and scalable microgrid development [68; 70]. Table 2 summarizes the main findings and research gaps identified in this review. For each technological area, the corresponding studies are cited, and current challenges and future research directions are outlined. In addition to the consolidated findings presented in Table 2, a more detailed comparative analysis of selected representative studies is provided in Table 3. This table outlines the methodologies applied, the key contributions made by each study, and their reported limitations or knowledge gaps. This analytical matrix enhances the paper's depth by highlighting not only what each study contributes but also where further research is needed. \begin{table}
\begin{tabular}{l l l} \hline
**Topic** & **References** & **Research Challenges and Future Directions** \\ \hline \multirow{8}{*}{Advanced Energy Management Systems and Control Architectures} & S-037 explores hierarchical control for solar-storage systems. S-127 proposes edge-based architectures for real-time decision-making. S-169 compares rule-based with AI-enhanced control strategies. Challenges include establishing standardized communication protocols for interoperability; integrating blockchain to ensure secure decentralized coordination; adopting explainable AI models in multi-agent architectures; and enhancing adaptive distributed intelligence using MAS. [19; 20; 22; 30; 56; 57; 67] \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 2: Consolidated research findings and future research directions in smart microgrids. In conclusion to Section 3, the findings discussed across the thematic areas directly address the four research questions presented in the introduction. Hybrid storage architectures (RQ1) have been widely implemented in recent studies to enhance flexibility and responsiveness. Control approaches (RQ2) range from centralized methods to adaptive multi-agent systems, as seen in multiple references. The integration of predictive tools and AI-based strategies (RQ3) has become increasingly prevalent, with applications such as reinforcement learning, digital twins, and deep learning models. Finally, persistent challenges (RQ4) include secure data exchange, lack of standardized AI deployment practices, and resilience coordination under uncertainty.