大语言模型推理动作范式驱动的梯级水电优化调度方法OA

Artificial intelligence large model technology is profoundly transforming various domains.Leveraging their powerful reasoning capabilities to comprehensively enhance the capability of hydropower scheduling has become a critical challenge in the hydropower field.To address this,this paper introduces large language model(LLM)into the field of hydropower scheduling and proposes a cascade hydropower optimal scheduling method driven by reasoning-acting(ReAct)paradigm.The method adopts the progressive optimality algorithm as the framework to transform complex,high-dimensional,multi-stage problems into a series of two-stage optimization problems.By leveraging constraint internalization and modular decoupling,it effectively decouples hydropower domain knowledge from scheduling strategies,thereby creating favorable conditions for LLM integration.Building upon this foundation,a strategy intelligence generation system and a strategy evaluation system driven by the ReAct paradigm are constructed.Through an iterative mechanism of action execution and evaluation,the intelligent formulation and autonomous optimization of scheduling strategies are promoted.A multi-scenario verification is conducted using a cascade hydropower system in a southwestern province of China as the case study.The results indicate that the proposed method can intelligently construct scheduling strategies that significantly outperform traditional methods under various system scales and inflow conditions.The sensitivity analysis of the non-dominated intelligent strategies demonstrates that in the two-reservoir system,the average power generation and computational efficiency can be increased by up to 171 million kW·h and 41.4 times,respectively.In the four-reservoir system,the power generation and efficiency can be increased by up to 135 million kW·h and 23.6 times,respectively.Through the analysis of scheduling results under multiple inflow conditions,the rationality of the generated strategies is further validated.As the reasoning capabilities of LLMs continue to improve,the proposed method is expected to demonstrate even more significant advantages in the field of hydropower scheduling,offering new technical pathways and theoretical support for intelligent hydropower scheduling.