Demonstrates improved load distribution in thermal power plants, suggesting deep reinforcement learning offers significant advantages over traditional methods.
Key Points
To develop a model for optimizing load distribution in thermal power plants using deep reinforcement learning techniques.
Analyzed objectives and constraints of load distribution in thermal power plants.
Constructed a deep reinforcement learning model for load optimization.
Defined overall architecture, state space, action space, and reward function for the model.
Compared the DDPG algorithm implementation with traditional algorithms in simulations.
The deep reinforcement learning model outperformed traditional algorithms in real-time responsiveness.
Demonstrated significant advantages in handling dynamic working conditions.
Achieved efficient and rational load distribution for power plant units.