## 3 Results
### Multi-Agent Systems and Distributed Control  
#### 3.2.1 Main Trends  
The adoption of MAS in energy management has transformed how DERs are coordinated and operated [36; 37; 38]. These systems enable decentralization in prosumer communities and provide robust frameworks for managing energy trading and distribution in increasingly complex networks [39; 40; 41]. Among the key advancements is the use of MAS to facilitate P2P energy trading in Local Electricity Markets (LEMs). These platforms enable prosumers to trade surplus energy directly, offering economic benefits and enhancing the system efficiency. For example, a decentralized framework proposed for LEMs incorporates advanced agents that act collaboratively via local computations and P2P communication, achieving effective frequency regulation and economic dispatch [15].  
Another significant trend is the application of DRL within MAS frameworks. DRL-based methods have been shown to enhance the scalability and performance in P2P trading systems. One notable example utilizes Multi-Agent Deep-Reinforcement Learning (MADRL) to manage local energy markets, optimizing both trading and flexibility services. This approach integrates techniques like a multi-actor attention critic and prioritized experience replay to achieve superior results compared to traditional methods, improving system reliability and economic outcomes [5]. Similarly, Federated Reinforcement Learning (FRL) has emerged as a promising approach to maintain privacy while optimizing energy trading and carbon allowance management in building-level Multi-Energy Systems (MES). By employing FRL, agents can balance economic and environmental goals, achieving notable reductions in energy costs and carbon emissions [42]. Clustering techniques have also enhanced the scalability of MAS in energy systems. For instance, a multi-cluster DRL framework addresses the heterogeneity of prosumers by grouping them based on their DER portfolios. This method employs shared policies within clusters to accelerate training and reduce the computational complexity. It has demonstrated strong generalization capabilities and an improved performance in large-scale scenarios, such as managing the energy trading of 300 residential households [6].  
In addition to trading, MAS frameworks have been integrated with other emerging technologies like V2X systems. These frameworks optimize the use of EV batteries for various operations, such as V2G and vehicle-to-vehicle (V2V) energy exchanges, while ensuring safe operational limits. For instance, a MAS framework that incorporates V2X has been shown to enhance microgrid stability and facilitate dynamic energy exchanges, achieving efficient resource utilization under real-world constraints [21]. Furthermore, agent-based systems have been applied to control strategies in microgrids with inverter-interfaced DERs. These systems leverage graph theory to enable effective communication between agents, resulting in the proportional sharing of real and reactive power, even under volatile conditions [14]. In the same case, trust management within MAS has received significant attention due to its critical role in decentralized systems. A cross-layer consensus protocol based on fuzzy logic introduces human-centric trust mechanisms to address vagueness and ensure transparency in P2P energy trading platforms. This protocol also enhances resilience against advanced attacks, such as Sybil and selfish mining, further strengthening the security of decentralized energy trading frameworks [43].