Reinforcement learning for wind-farm flow control: Current state and future actions

Abstract

Wind-farm flow control stands at the forefront of grand challenges in wind-energy science. The central issue is that current algorithms are based on simplified models and, thus, fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions. Reinforcement learning (RL), as a subset of machine learning, has demonstrated its effectiveness in solving high-dimensional problems in various domains, and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control. This review has two main objectives. Firstly, it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods. By examining the latest research in this area, the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques. Secondly, it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL. By highlighting these challenges, the review aims to identify areas requiring further exploration and potential opportunities for future research.