Random environment simulation-based multi-stage reinforcement learning for short-term scheduling of cascade hydropower stations

As a clean and flexible approach to energy utilization, hydropower is playing an increasingly vital role in power systems. However, runoff uncertainty significantly impacts the scheduling effectiveness of cascade hydropower stations. To address this, this paper constructs a short-term optimization scheduling model for cascade hydropower stations and proposes a novel Random Environment Simulation-based Multi-stage Reinforcement Learning (RESMRL) algorithm. The proposed algorithm effectively handles runoff uncertainty while overcoming the limitations of conventional deep reinforcement learning in terms of generalization and safety. RESMRL employs Random Environment Simulation (RES) to introduce a wide range of diverse scenarios into the agent's training environment, enhancing its alignment with complex and dynamic real-world scheduling conditions. Furthermore, RESMRL adopts a multi-stage training strategy that decomposes the short-term scheduling task into multiple learning stages with varying levels of difficulty. This enables the agent to progressively acquire scheduling experience, with each stage focusing on distinct objectives and thereby enhancing its adaptability to complex and diverse RES environments, starting from easier to more challenging scenarios. Additionally, a novel hybrid constraint processing framework is proposed. Integrating the physical characteristics of cascade hydropower stations, this framework accurately and efficiently manages the numerous constraints inherent in cascade hydropower scheduling, thereby ensuring the safety of scheduling decisions made by the agent. Case studies conducted on the cascade hydropower stations in the lower Jinsha River in China demonstrate the effectiveness and superiority of the proposed methodology compared to other benchmark approaches.