Scalable Power Dispatch Automation Methods Based on Artificial Intelligence in Distributed Energy Systems - Yin, Zhaoyuan; Zhang, Xiongbao; Luo, Wanhe; Ruan, Shidi (Distributed Generation & Alternative Energy Journal, 2026)
With the high penetration of distributed energy resources (DERs), the strong uncertainty of their output and the complex interaction of network topology pose severe challenges to the real-time performance, scalability, and intelligence level of dispatching methods. This paper proposes a hierarchical distributed collaborative intelligent dispatching architecture based on deep reinforcement learning (DRL) and graph neural networks (GNNs). This architecture adopts a two-layer design of “regional agent-edge controller.” The upper-layer regional agent uses GNN to process the global power flow state and generate node marginal price signals; the lower-layer edge controllers act as independent DRL agents, making autonomous decisions based on local observations and price signals. Through a hybrid mechanism of centralized training and distributed execution, collaborative learning and plug-and-play expansion of the system are achieved. This method significantly reduces dispatch costs when dealing with uncertainty, achieving a high photovoltaic grid integration rate of up to 100%, and controlling the maximum net power deviation between day-ahead planning and real-time operation to within 0.35 MW. Even when the system is expanded to 500 DER units, the online decision-making time remains at 7.5 milliseconds, and communication overhead only slowly increases to 35.0 KB, demonstrating its significant effectiveness in improving system resilience, operational economy, and scalability.