Harvesting energy from turbulent winds with Reinforcement Learning
This work addresses the challenge of generalizing control for airborne wind energy in unpredictable environments, offering a robust alternative to traditional methods, though it appears incremental as it applies an existing RL approach to a specific domain.
The paper tackled the problem of controlling airborne wind energy systems in turbulent conditions by replacing model-dependent optimal control with reinforcement learning, resulting in agents that effectively extract energy from turbulent flows using minimal local information.
Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical energy into electrical energy by means of a generator. Such systems are usually controlled by manoeuvering the kite so as to follow a predefined path prescribed by optimal control techniques, such as model-predictive control. These methods are strongly dependent on the specific model at use and difficult to generalize, especially in unpredictable conditions such as the turbulent atmospheric boundary layer. Our aim is to explore the possibility of replacing these techniques with an approach based on Reinforcement Learning (RL). Unlike traditional methods, RL does not require a predefined model, making it robust to variability and uncertainty. Our experimental results in complex simulated environments demonstrate that AWE agents trained with RL can effectively extract energy from turbulent flows, relying on minimal local information about the kite orientation and speed relative to the wind.