Although robust models have been proposed to manage uncertainties, such as those leveraging CVaR to optimize energy allocation [16], they are typically constrained by idealized assumptions and lack the real-time adaptability required for practical deployment in P2P trading environments. The integration of co-optimization strategies that simultaneously address economic, environmental, and operational objectives remains an underexplored area. In the realm of MAS and distributed control, substantial progress has been made in enabling decentralized coordination and flexibility in energy management. However, existing MAS frameworks often face scalability limitations when applied to large, heterogeneous networks. Studies have demonstrated the effectiveness of MAS in small-scale applications [14], but the absence of advanced coordination mechanisms capable of balancing the computational efficiency with the complexity of managing prosumer communities hampers their effectiveness in real-world applications. Furthermore, the integration of MAS with emerging technologies, such as blockchain and AI, has been limited. Few studies, such as [5], explore how hybrid approaches combining these technologies could enhance the scalability, security, and adaptability of P2P energy trading frameworks. Simulations, case studies, and real-world applications have provided valuable insights into validating theoretical models and exploring practical challenges in decentralized energy systems. However, a critical evaluation of the existing literature reveals significant gaps that hinder the scalability and practical deployment of P2P energy trading frameworks. While many studies have demonstrated the feasibility of P2P systems in small-scale or controlled environments, such as isolated microgrids [17], they often fail to address the socio-economic and regulatory complexities that arise in larger, more diverse deployments. This limitation underscores the need for research that considers the broader context of P2P systems, including the integration of heterogeneous energy resources and the impact of varying market conditions. Blockchain technology and smart contracts have emerged as promising tools for enabling secure and transparent energy transactions. However, their adoption in resource-constrained environments, such as microgrids, is hampered by high computational demands and energy-intensive consensus mechanisms. Although hybrid blockchain models have been proposed to mitigate these challenges [6], their practical implementation remains in its infancy, with the limited exploration of optimization strategies tailored to low-power,distributed networks. Similarly, while advanced communication tools and AI have shown potential for predictive optimization in P2P energy markets [19], their integration into real-time energy management systems is still underdeveloped, leaving a gap in leveraging these technologies for dynamic decision making. Frameworks for robust and decentralized energy management have made notable progress in recent years, particularly in addressing resilience and adaptability under uncertainty. However, many of these frameworks, such as those presented in [2], remain largely theoretical, focusing on idealized scenarios rather than the practical challenges of dynamic energy resource allocation in highly distributed systems. For instance, while EVs have been recognized for their dual functionality as energy storage units and grid stabilizers in V2G and V2X systems [18], their broader integration into energy management frameworks is still underexplored. This highlights a critical gap in the literature, as the coordination of diverse resources, such as EVs, is essential for achieving scalable and resilient decentralized energy systems. Furthermore, the role of electric machines in DES is a relatively nascent area of research.