Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

2017 2nd International Seminar on Advances in Materials Science and Engineering

Li, Ze^1

International Education Institute, North China Electric Power University, Baoding, China^1

关键词: Difference values;    Optimal control strategy;    Power tracking controls;    Q-learning algorithms;    Reinforcement Learning theories;    Simulation example;    Wind electricity;    Wind generator systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/231/1/012037/pdf
DOI  :  10.1088/1757-899X/231/1/012037

来源: IOP

【 摘 要 】

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.