Hybrid Iteration-Based Reinforcement Learning Scheme for Markov Jump Multiarea Interconnected Power Systems

This article presents a novel reinforcement learning (RL)-based load frequency control (LFC) algorithm designed for Markov jump multiarea interconnected power systems (MJMIPSs). Existing LFC methods often require precise dynamic information of the power system, which is challenging to acquire accurately due to the the system complexity, stochastic disturbances, and high modeling costs. Such limitations make it difficult to implement effective control strategies in real-world applications. In response to these challenges, we propose a decentralized hybrid iteration (HI) algorithm that combines the RL scheme with a decentralized control technique to address the LFC problem for MJMIPSs. In contrast to conventional RL schemes, such as policy iteration (PI) and value iteration (VI), the proposed algorithm achieves the controller design without subsystem decomposition (SD) by employing mixed-mode data acquisition and mode-related data classification, while eliminating the requirements on exact system dynamics and initial admissible control policies. Finally, we verify the effectiveness of the proposed method through the power systems.