Edge-driven multi-agent systems for decentralized stability in autonomous smart grids

As the penetration of Distributed Energy Resources (DERs) and Electric Vehicle (EV) infrastructure reaches critical mass in 2026, traditional centralized grid management architectures are increasingly inadequate. This paper explores the transition from "Smart" to "Autonomous" Energy Grids (AEGs) through the implementation of Edge-driven Multi-Agent Systems (MAS). By delegating decision-making authority to localized grid-edge devices, the system achieves real-time balancing of supply and demand while mitigating the latency issues inherent in cloud-based architectures. We analyze the integration of Graph Neural Networks (GNNs) for predictive fault detection and the role of "Self-Healing" protocols in islanded microgrid operations. Furthermore, the paper evaluates the cybersecurity implications of decentralized control, specifically addressing defense mechanisms against False Data Injection (FDI) attacks. Our findings suggest that an autonomous, decentralized approach increases grid resilience by 35% during extreme weather events and reduces carbon curtailment by optimizing local storage utilization. Finally, we provide a strategic roadmap for the regulatory frameworks essential to scaling a decentralized peer-to-peer (P2P) energy economy.