[8] introduces a multi-agent approach with verifiable and data-sovereign information flows, enabling decentralized redispatch in DES while ensuring privacy and scalability through digital Self-Sovereign Identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). This approach addresses the challenges of integrating small-scale assets, such as photovoltaic systems and EVs, into grid stabilization processes. The optimization of EV charging scheduling has also been a focal point in enhancing the efficiency of regional clean energy power supply networks. Ref. [9] demonstrates how intelligent algorithms and machine learning-based demand forecasting can reduce the peak grid load by 15%, increase clean energy consumption by 23%, and lower total electricity consumption by 10%. Furthermore, ref. [10] presents an integrative lifecycle design approach for renewable-battery-consumer energy systems, optimizing the photovoltaic capacity and battery storage to achieve a higher net present value and lower carbon intensity, thereby promoting sustainable development and carbon neutrality. In the context of distributed energy management systems, ref. [11] leverages edge computing and machine learning to improve energy allocation, reduce energy waste by 18%, and enhance the system response speed by 30%. This approach highlights the importance of the real-time data processing and dynamic scheduling of distributed energy resources. Additionally, ref. [12] proposes a low-carbon planning method for flexible distribution networks, incorporating EV orderly charging and discharging strategies to minimize comprehensive operating costs and improve grid stability. Similarly, ref. [13] addresses power management challenges in Hybrid Microgrids (HMGs) by introducing a 3-D droop-based control scheme for bidirectional DC-DC converters, ensuring accurate power sharing and the efficient operation of EVs within HMGs. The development of DES has steadily evolved over the past decade, driven by the imperative to create sustainable, efficient, and resilient energy networks. Early foundational work in 2014 laid the groundwork for integrating RES into distributed networks, focusing on optimizing energy flows to minimize losses and address the inherent variability of RES. These studies emphasized hierarchical control frameworks and robust optimization methods to manage uncertainties in distributed energy environments, marking a significant step toward modern energy management systems [1]. This early emphasis on robustness and efficiency provided a blueprint for future innovations. In subsequent years, the concept of MAS emerged as a transformative approach to managing distributed resources. Research in 2017 demonstrated how MAS frameworks could enable decentralized coordination and dynamic energy trading in P2P systems. This work highlighted MAS's ability to balance supply and demand autonomously while addressing scalability challenges in increasingly complex energy networks [14]. Around the same time, blockchain technology began to be explored as a solution to enhance the security and transparency of energy transactions. A notable study in 2017 introduced blockchain-based systems for P2P energy trading, emphasizing the elimination of intermediaries and the secure automation of transactions, thus addressing critical concerns of trust and efficiency in decentralized markets [15]. By 2019, optimization models had evolved to address the variability and unpredictability inherent in RES. One prominent study employed a hybrid robust Conditional Value at Risk (CVaR) framework to optimize energy allocation and reduce operational costs under uncertain conditions. This approach demonstrated the potential of stochastic optimization in balancing financial and operational risks, paving the way for more sophisticated energy management models [16]. Simultaneously, MAS applications were refined to improve their scalability and adaptability.