Reinforcement learning based CPS self-tuning control methodology for interconnected power systems

The automatic generation control (AGC) problem is a stochastic multistage decision problem, which can be modeled as a Markovian Decision Process (MDP). The paper introduces the Q-learning method as the core algorithm of reinforcement learning (RL), and regards the CPS values as the rewards from the interconnected power systems. By regulating a closed-loop CPS control rule to maximize the total reward in the procedure of on-line learning, the optimal CPS control strategy can be gradually obtained. The case study shows that after adding the RL control, the robustness and adaptability of AGC system is enhanced obviously and the CPS compliance is ensured.