Large Language Model-Driven Power Dispatch Decision-Making Teaching Assistant: Reinforcement Learning Optimization and Interpretable Knowledge Graph Fusion Method

The increasing complexity of modern power systems, characterized by the high penetration of renewable energy sources, distributed generation, and highly dynamic load profiles, necessitates the development of advanced computational methods for effective and reliable dispatch decision-making. Traditional optimization techniques, while theoretically sound, often struggle to handle the temporal coupling of system states, stochastic renewable inputs, and multi-level hierarchical control structures that are intrinsic to modern grids. These limitations frequently result in suboptimal operational performance, reduced system resilience, and limited adaptability to unexpected disturbances. To address these pressing challenges, we propose a novel framework that integrates reinforcement learning-based optimization with interpretable knowledge graph fusion, which emphasizes cutting-edge methods in computational intelligence and data-driven modeling. Our approach introduces SynDispatchNet, a physics-integrated dispatch representation that combines domain-specific inductive biases with a learnable policy architecture, enabling both rapid inference and strong generalization across a wide range of dispatch scenarios and grid conditions. Complementing this core architecture, we develop a Knowledge-Aware Trajectory Refinement strategy that leverages historical dispatch trajectories, expert-annotated operating protocols, and real-time feedback from the power system to facilitate continual policy improvement and robust adaptation to dynamic operating environments. Extensive experimental evaluations demonstrate that our proposed method consistently outperforms conventional optimization-based dispatch schemes in terms of feasibility, robustness, and interpretability, while ensuring compliance with physical system constraints. This work represents a significant contribution to the advancement of intelligent and adaptive energy management systems, supporting the development of scalable, data-driven solutions that address the evolving needs of modern power grids and the broader field of computer science.