DQN-based real-time optimized load distribution of cascade hydropower stations

Abstract

[Objective] With the continuous expansion of cascade hydropower systems and increasingly complex operational environments, traditional optimized scheduling method struggle to meet the complex and diverse regulation requirements of river basins, while their decision-making accuracy and computational efficiency remain limited. [Methods] To address these limitations, an optimized scheduling model was established for cascade hydropower systems that considered power generation scheduling and water resource regulation, with minimum water consumption as the primary objective. Additionally, an efficient solution method based on deep reinforcement learning(Deep Q-Network, DQN) was developed. Using the cascade hydropower system in the middle reaches of the Dadu River as a case study, three operating conditions(medium, low, and high load) were established. The model was trained using actual operational data and evaluated through water consumption and water level processes. [Results] The result showed that the DQN algorithm reduced computational time by approximately 41.37 times compared to conventional method. Furthermore, DQN effectively balanced conflicting water regulation demands(e.g., water level stability and flow control), achieving an average reduction of 0.058 m/min in the water level fluctuation index and a total water consumption decrease of 11.58 million m³ compared to pre-optimization. Notably, the proposed model exhibited good stability across diverse operating conditions. [Conclusion] The findings indicate that the DQN-based load distribution method enhances system operational stability and safety while achieving breakthroughs in both scientific scheduling and computational efficiency. By dynamically optimizing real-time power output distribution among stations, the intelligent decision-making framework significantly mitigates water level fluctuations and reduces water consumption in power generation, thereby validating the feasibility of coordinated power-water optimization. This method provides a novel technical approach for intelligent scheduling of cascade hydropower systems and their optimized regulation under complex operational scenarios in the new era.

Keywords

load distribution / cascade hydropower / deep reinforcement learning / real-time scheduling / dynamic programming / influencing factors