Optimizing Reservoir Operations with Reinforcement Learning: A Data-Driven Framework

Effective reservoir management demands adaptive, data-driven strategies to optimize storage and release decisions while balancing multiple, often competing, operational objectives. This study investigates the application of Q-Learning, a model-free reinforcement learning (RL) algorithm, for optimizing reservoir releases under dynamic and uncertain hydrological conditions. Unlike conventional rule-based or offline optimization methods, the proposed RL approach continuously refines its release policy by learning from environmental feedback and observed states, enabling real-time adaptation without the need for a predefined system model. The framework is tested on the Dez Reservoir in Iran, a real-world case study characterized by significant inflow variability and seasonal water demand. Simulation results demonstrate that Q-Learning effectively manages operational complexity, maintaining storage within prescribed bounds and delivering release patterns closely aligned with demand. To benchmark performance, a simplified Ant Colony Optimization (ACO) model is implemented for comparison. While ACO shows moderate capability in deficit reduction, Q-Learning outperforms it in terms of constraint satisfaction and long-term feasibility. Findings highlight the strong potential of reinforcement learning to support intelligent, scalable, and robust decision-making in modern reservoir operation systems under uncertainty.

Keywords:

Reservoir Operation , Reinforcement Learning , Dez Dam , Machine learning