Renewable Energy Integrated Power System Load Frequency Control Based on Multi-Agent Actor-Double-Critic Deep Reinforcement Learning

To achieve optimal performance of load frequency control (LFC), a data-driven scheme is proposed for renewable power systems in this paper. A multi-agent Actor-Double-Critic deep reinforcement learning approach is developed to ensure real-time scheduling that complies with system safety operation constraints within the multi-area LFC power system. For implementation, each individual controller only needs local information in its control area to deliver optimal control signals. A Self-Critic and Cons-Critic network is employed to improve the convergence speed during the multi-agent training process. Simulations on two-area and three-area LFC power systems are performed to verify and validate the analytical results. Comparisons with conventional PI and fuzzy PI controllers demonstrate that the presented approach effectively reduces training difficulties, guarantees the satisfaction of system safety constraints, and significantly improves the dynamic frequency regulation performance of the power system.