## 4 Discussion  
The findings of this systematic review demonstrate significant progress across all six thematic areas of DES, while simultaneously revealing critical interdependencies and persistent challenges that must be addressed to realize their potential fully. A central insight emerging from our analysis is the inherent tension between theoretical advancements and practical implementation, particularly evident in three key dimensions: the scalability-decentralization paradox, the security-efficiency trade-off, and the simulation-reality gap. The optimization and modeling of energy systems have made notable strides in addressing uncertainties through advanced algorithms and hybrid methodologies. Stochastic-robust optimization and CVaR frameworks have proven particularly effective in managing financial and operational risks, as evidenced by their ability to reduce costs by up to 21.49% in certain configurations [16]. However, these approaches often rely on centralized computational architectures that fundamentally conflict with the decentralized philosophy underlying MAS and distributed control frameworks. This paradox becomes even more pronounced when considering the energy demands of blockchain implementations, where the transparency and security benefits of protocols like PoW [66] come at the cost of significant computational overhead--a trade-off that currently limits their practical adoption in energy--constrained microgrid environments [19]. Multi-agent systems and distributed control have emerged as transformative solutions, offering enhanced flexibility and adaptability through innovations like DRL and FRL [5; 42]. These approaches excel at preserving privacy while managing complexity in decentralized networks yet face substantial challenges when scaling to large, heterogeneous systems. While clustering techniques provide partial solutions [6], the development of more robust coordination mechanisms remains crucial, particularly for applications requiring real-time responsiveness in dynamic market conditions. The integration of trust mechanisms adds an important layer of security [43], but their implementation in rapidly evolving environments requires further investigation to balance security needs with the system performance. The gap between the simulated performance and real-world implementation emerges as a recurring theme across all six analytical dimensions. Hierarchical energy management systems and blockchain-enabled frameworks demonstrate considerable potentialin controlled environments, with some models showing annual savings of 8.96% in the simulation [81]. However, these results often rely on idealized assumptions that fail to account for the full complexity of human behavior, communication failures, or unpredictable market fluctuations [17]. This discrepancy underscores the need for more sophisticated validation approaches, such as hardware-in-the-loop simulations incorporating real-world data streams [52], to bridge the divide between theoretical models and practical applications. The evolution of the electric machine design illustrates both the progress and remaining challenges in DES implementation. While advanced synchronous and reluctance generators have adapted well to variable renewable sources and emerging paradigms like V2G and V2V trading [95], their performance in highly dynamic environments requires further optimization. The integration of these machines with MAS and blockchain frameworks shows promise for enhancing system resilience, but additional work is needed to fully realize these synergies, particularly in edge computing applications where real-time responsiveness is critical. Moving forward, the DES field must prioritize three key areas of development. First, the creation of co-design frameworks that systematically integrate optimization models with distributed control architectures, potentially through quantum-assisted computing approaches, could help resolve the current scalability-decentralization impasse.