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Harvesting energy from turbulent winds with Reinforcement Learning

arXiv 2412.13961 ↗First posted 18 Dec 2024cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 32 references that are in the map. First posted on arXiv 18 Dec 2024.

Builds on

7 of 32 references.

References · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  5. 2017A Deeper Look at Experience Replaycited by 105
  6. 2022Optimizing Airborne Wind Energy with Reinforcement Learningcited by 2
  7. 2021An optimal reeling control strategy for pumping airborne wind energy systems without wind speed feedbackcited by 1

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  4. 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
  5. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
  6. 2024TEeVTOL: Balancing Energy and Time Efficiency in eVTOL Aircraft Path Planning Across City-Scale Wind Fields ↗
  7. 2026Learning step-level dynamic soaring in shear flow ↗
  8. 2026Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions