Q-learning based dynamic optimal CPS control methodology for interconnected power systems

The NERC's control performance standard (CPS) based automatic generation control (AGC) problem is a stochastic multistage decision problem, which can be suitably modeled as a reinforcement learning (RL) problem based on Markov decision process (MDP) theory. The paper chose the Q-learning method as the RL algorithm regarding 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. An applicable semi-supervisory pre-learning method was introduced to enhance the stability and convergence ability of Q-learning controllers. Two cases show that the proposed controllers can obviously enhance the robustness and adaptability of AGC systems while the CPS compliances are ensured. Soc. for Elec. Eng.