Preprints
https://doi.org/10.5194/wes-2022-104
https://doi.org/10.5194/wes-2022-104
 
09 Nov 2022
09 Nov 2022
Status: this preprint is currently under review for the journal WES.

Stochastic Gradient Descent for Wind Farm Optimization

Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, and Rafael Valotta Rodrigues Julian Quick et al.
  • Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract. It is important to optimize wind turbine positions to mitigate potential wake losses. To perform this optimization, atmospheric conditions, such as the inflow speed and direction, are assigned probability distributions according to measured data, and these conditions are propagated through engineering wake models to estimate the annual energy production (AEP). This study presents stochastic gradient descent (SGD) for wind farm optimization, which is an approach that estimates the gradient of the AEP using Monte Carlo simulation, allowing for the consideration of an arbitrarily large number of atmospheric conditions. This method does not require that the atmospheric conditions be discretized, in contrast to the typical rectangular quadrature approximation of AEP. SGD is demonstrated using wind farms with square boundaries, considering cases with 25, 64, and 100 turbines, and the results are compared to a deterministic optimization approach. It is shown that SGD finds a larger optimal AEP in substantially less time than the deterministic counterpart as the number of wind turbines is increased.

Julian Quick et al.

Status: open (until 21 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Julian Quick et al.

Julian Quick et al.

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Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.