Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization

Abstract

Wind power is becoming an increasingly vital source of renewable energy worldwide. However, controlling power generation in wind farms faces significant challenges due to the inherent complexity of these systems. To address this issue and maximize power output, we propose a novel communication-based multi-agent deep reinforcement learning approach for large-scale wind farm control. We introduce a multivariate power model for wind farms to analyze the impact of wake effects on power output. This model considers controllable variables such as axial induction factor, yaw angle, and tilt angle. To coordinate the continuous controls in large-scale wind farms, we propose the hierarchical communication multi-agent proximal policy optimization (HCMAPPO) algorithm. The wind farm is divided into multiple wind turbine aggregators (WTAs), and neighboring WTAs can exchange information through hierarchical communication to optimize power output. Our simulation results demonstrate that the multivariate HCMAPPO approach significantly increases wind farm power output compared to traditional PID control, coordinated model-based predictive control, and the multi-agent deep deterministic policy gradient algorithm. The HCMAPPO algorithm can be trained using a thirteen-turbine wind farm environment and effectively applied to large-scale wind farms. Importantly, as the wind farm scale increases, there is no significant increase in wind turbine blade fatigue damage from wake control. This multivariate HCMAPPO control strategy enables the collective maximization of power output in large-scale wind farms.