Presentations

Abstract

One of the challenges in digitizing the wind energy sector is to have reliable, accurate and efficient data assessment and prediction tools. We believe that accurate surrogates are key towards improving efficiency and reliability for informed decision making. For a quantitative definition of site complexity and assessment, one would require multiple scenarios/case for a CFD simulator to incorporate the influence of a parameter on variability of wind speed and energy production. These simulations are computationally expensive, and the industry is on a look out for more efficient methods. Towards this end, we propose using a polynomial surrogate based on Polynomial Chaos Expansion (PCE) for uncertainty quantification that requires on a few CFD simulations to assess the variability of wind speed and annual energy production with respect to variability in an input parameter. The variability estimates are validated against the standard Monte Carlo method on simple benchmarks and then tested for complex terrains. The polynomial surrogate relies on a limited set of input variations of the forest height provided variability distributions for wind speed and AEP locally (for specified points of interests) and has proven to be a low-cost and accurate method for estimating uncertainty. The work has further led to informed ways of correlating variability with terrain complexity. We will discuss results towards understanding how the canopy foliage impacts the variability in wind speed and annual energy production (AEP) in complex terrains.