Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind

In this paper, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

Automated summary

This paper presents a proof-of-concept distributed reinforcement learning framework for maximizing wind farm energy capture, utilizing Q-learning in a wake-delayed wind farm environment with time-varying wind conditions. The proposed algorithm modifications create the GARLIC framework for optimizing wind farm energy capture in time-varying conditions, which is compared to the static lookup table wind farm controller baseline FLORIS.