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.