Wind energy is a field with a large number of uncertainties. The random nature of the weather conditions, including wind speed, wind direction, and turbulence intensity, influences the energy output and the structural safety of a wind farm, making its performance fluctuate and difficult to predict. The uncertainties presented in the energy output and structure lifetime lead to increased investment risk. There are possibilities to reduce the risk associated with these uncertainties by optimizing the design of the farm or the wind turbine, with respect to the stochastic parameters. The goal of this project is to improve the wind farm optimization problem by providing accurate and computationally efficient annual energy production (AEP) estimates, which is a uncertainty quantification that is required in every optimization step.

Uncertainty quantification has been recognized as a challenge in the wind energy industry, as the chaotic nature of the weather condition complicates the prediction of energy production. High-fidelity wind farm models usually employ advanced models like Large Eddy Simulation or Reynolds averaged Navier-Stokes equation for better accuracy. However, the prolonged computation time of these high-fidelity models make the traditional uncertainty quantification approach like the Monte-Carlo simulation or other integration techniques infeasible for larger wind farms. 

To overcome this limitation, the report proposes the use of generalized polynomial chaos expansion (PCE) to characterize the AEP as a function of wind speed and wind direction. PCE is a technique that approximates a random variable using a series of orthogonal polynomials, the polynomials are chosen based on the target distribution. This report explains how a surrogate model of the AEP can be constructed using PCE, which can be used in optimization or model analysis.

The objective of the thesis work is to minimize the number of model evaluations required for obtaining an accurate energy response surface. Different ideas of non-intrusive PCE are implemented and explored in this project. The report demonstrates that, the multi-element polynomial chaos fitted by point collocation, with a dependent polynomial basis, is not only able to make accurate and stable (with respect to the placement of the measurements) energy predictions, but also produces realistic energy response surface.

Vindkraft är en bransch med många osäkerheter, där väderförhållandena påverkar energiproduktionen och strukturens livslängd. Denna osäkerhet ökar investeringsrisken, men kan minskas genom optimering av vindkraftverkets design med hänsyn till de stokastiska parametrarna. Syftet med denna rapport är att förbättra optimeringsproblemet för vindkraftverk genom att ge noggranna och effektiva årliga energiproduktionsberäkningar (AEP), vilket krävs vid varje optimeringssteg. I rapporten används polynomial chaos expansion (PCE) för att approximera AEP och minska antalet nödvändiga modellutvärderingar. Resultaten visar att PCE är en effektiv metod för att göra energiprognoser.