Clustering techniques were introduced, allowing MAS frameworks to coordinate large-scale networks of prosumers more efficiently by grouping similar agents and reducing computational complexity [2]. The integration of hierarchical scheduling frameworks into decentralized energy systems gained momentum in 2020. These systems provided innovative solutions for aggregating demand-side flexibility and optimizing energy trading among interconnected microgrids. A landmark study that year demonstrated how hierarchical scheduling could manage Thermostatically Controlled Loads (TCLs) as virtual batteries, significantly improving energy efficiency and enabling privacy-preserving trading mechanisms [17]. Concurrently, the role of EVs within DES became a focal point. Research highlighted the dual functionality of EVs as mobile energy storage units and contributors to Vehicle-to-Grid (V2G) operations, showcasing their potential to enhance grid stability and integrate renewable energy more effectively [18]. From 2021 onward, the integration of advanced Artificial Intelligence (AI) techniques with MAS frameworks marked a turning point in the field. Deep Reinforcement Learning (DRL) and federated learning frameworks were introduced, enabling decentralized systems to make real-time decisions under highly dynamic conditions. For example, a 2022 study employed multi-agent DRL to optimize P2P energy trading strategies, achieving substantial cost reductions and improved resource utilization compared to traditional optimization techniques [5]. At the same time, blockchain technology evolved to include smart contracts, automating energy transactions and ensuring compliance with regulatory frameworks. These advancements highlighted the potential of combining AI with blockchain to address the dual challenges of scalability and transparency in decentralized energy systems [19]. The role of electric machines within DES also gained significant attention during that period. Some research has explored the integration of advanced control algorithms with synchronous and reluctance generators to stabilize microgrid operations. A study in 2022 demonstrated how advanced droop-controlled inverters interfaced with LC filters could maintain the voltage and frequency stability during P2P energy trading, showcasing the critical role of electric machines in ensuring operational reliability [20]. Recent advancements in 2023 and 2024 reflect the convergence of MAS, blockchain, and robust optimization frameworks. A 2023 study introduced a hierarchical blockchain model integrated with MAS to enhance the scalability and security of microgrid operations. This framework employed innovative trust-based consensus protocols to address privacy concerns and ensure robust energy management under uncertain conditions [6]. Simultaneously, a 2024 study examined the application of multi-cluster DRL to coordinate P2P energy trading in large-scale networks. This approach demonstrated a superior performance in handling the heterogeneity of prosumers and optimizing energy allocation [6]. In addition, the integration of EVs with Vehicle-to-Everything (V2X) frameworks has emerged as a promising area of research. A recent study demonstrated how V2X systems could optimize EV battery usage for dynamic energy exchanges, enhancing the flexibility and resilience of distributed networks while ensuring safe operational limits for EVs [21]. While research on decentralized energy systems and P2P energy trading has advanced considerably, significant gaps remain within the identified thematic areas, highlighting opportunities for further investigation. In the domain of optimization and modeling, most existing studies rely on static or deterministic approaches that often fail to account for the dynamic and stochastic nature of RES and demand fluctuations in distributed networks.