Combining machine learning and computational fluid dynamics for solar panel tilt angle optimization in extreme winds

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Although a key driver for green energy development, solar photovoltaic power plants face the major risk of severe wind damages, as there is currently no best practice on how to best stow the panels under strong wind conditions. In this research, an out-of-the-box numerical framework is introduced to inform the discussion around panel design and recommended stow positions, one that leverages the increasing use of solar tracker actuators, that allows panels to set an optimal angle relative to the sun to maximize power output, and incidentally offer a great potential for optimal safeguarding through individual panel piloting. The task of concurrently optimizing multiple panel tilts in a turbulent atmospheric boundary layer wind flow is modeled as a Markov decision process and solved with a single-step deep reinforcement learning algorithm, intended for situations where the optimal policy to be learnt by a neural network does not depend on state. The numerical reward fed to the neural network is computed from high-fidelity numerical simulations combining variational multiscale modeling of the Navier–Stokes equations and anisotropic boundary layer mesh adaptation, to accurately represent critical flow features at affordable computational costs, regardless of the panel tilts chosen by the learning agent. A range of experiments is performed across various learning objectives accounting for different possible causes of breakage (such as tear, vibrations, and fatigue), for which the proposed approach successfully minimizes the aerodynamic efforts on two-dimensional and three-dimensional arrangements of six ground-mounted panels under an incident wind speed of 50 km/h, while outperforming baseline safeguarding practices considered in the literature by several dozen per cent. This gives hope that, by interacting with its computational fluid dynamics environment in a trial-and-error manner, a deep reinforcement learning agent can learn unexpected solutions to this complex decision-making problem and come up with innovative, feasible solutions capable of managing utility-scale solar assets during high-wind events while efficiently complementing engineering intuition and practical experience.