The rapid integration of high-proportion renewable energy and the escalating complexity of modern power systems have exposed critical limitations in conventional grid dispatching automation systems

particularly in their ability to achieve real-time responsiveness

adaptive coordination

and collaborative decision-making. To address these challenges

this paper presents a novel next-generation dispatching automation framework that synergistically integrates large language models (LLMs)

knowledge graphs (KGs)

and AI agent technologies. The proposed system harnesses LLMs for advanced natural language processing and semantic reasoning

employs KGs to construct a structured

multidimensional knowledge representation of grid entities and their interdependencies

and deploys AI agents for autonomous decision-making and dynamic operational optimization. Collectively

these components establish a closed-loop intelligent architecture encompassing “perception-cognition-decision-execution”

enhancing system-wide situational awareness and adaptive control. This study proposes a unified integration methodology for LLMs

KGs

and AI agents in power system dispatching with modularized functional implementations designed for scalable deployment. Furthermore

it provides comprehensive multi-scenario analyses to verify operational efficacy. The proposed system offers a transformative technical pathway for advancing intelligent dispatching in next-generation power grids by resolving key bottlenecks in data interoperability

decision transparency

and dynamic adaptability.