Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation

Optimal operation of hydropower reservoir systems is a classical optimization problem high dimensionality and stochastic nature. A key challenge lies in improving the interpretability strategies, i.e., cause–effect relationship between system outputs (or actions) contributing variables such as states inputs. This paper reports for first time new deep reinforcement learning (DRL) framework optimal based on Q-networks (DQNs), which provides significant advance understanding performance operations. DQN combines Q-learning two artificial neural networks (ANNs), acts agent to interact with through its providing actions. Three knowledge forms considering states, actions, rewards were constructed improve strategies. The impacts these DRL parameters analyzed. was tested Huanren China, using 400-year synthetic flow data training 30-year observed verification. discretization levels water level energy output yield contrasting effects: finer improved terms annual generated production reliability; however, can reduce search efficiency, thus resulting performance. Compared benchmark algorithms including dynamic programming, decision tree, proposed approach effectively factor future inflow uncertainties when determining operations generate markedly higher hydropower. study context characteristics input features, shows promise potentially being implemented practice derive policies that be updated automatically by from data.