Task-Decoupled and Multi-Task Synergistic LLM-MoE Method for Power System Operation Simulation

With the increasing integration of high-penetration renewable energy and emerging loads, power system operation simulation faces two major challenges, namely strong uncertainty and significant heterogeneity in the output characteristics of multiple generator types. Traditional mathematical programming methods struggle to effectively handle uncertainty while meeting real-time computational requirements. Existing deep learning approaches fail to decouple the heterogeneous output characteristics of different generator types, which limits their ability to achieve coordinated operation. To address these issues, this paper proposes a task-decoupled and multi-task synergistic LLM-MoE method for power system operation simulation. First, a feature encoder based on Residual-Gated Linear Units is constructed to perform deep filtering and efficient representation of multi-source heterogeneous data. Second, a pre-trained large language model is employed as a temporal feature extractor to enhance temporal modeling capability and cross-scenario generalization. Finally, a customized gating-controlled mixture-of-experts decoder is developed. It dynamically coordinates task-specific and shared experts, which enables unified modeling of task decoupling, cross-task information sharing, and system physical constraints. Simulation results based on a provincial-level power grid in China demonstrate that the proposed method achieves high-accuracy and high-efficiency operation simulation while ensuring physical consistency.