Quantitative analysis of phenomena affecting seasonal bias in pre-construction energy yield models

Description

M.Sci. student: Gabriel Antonio Gonzalez Crisostomo(220293)
Thesis is confidential due to Ørsted, may be available upon request.
"Pre-construction Energy Yield models, such as that currently used by Ørsted, can exhibit seasonal biases. Significant biases have been observed in resource predictions for summer months at locations in the northern Atlantic, which causes issues in revenue and power forecasting. The aim of this thesis is to be able to analyse and quantify the effect of phenomena that have significant impact on the resource prediction offshore. Short-listed metrics characterizing relevant phenomena include statistics related to wind shear and long-term correction; measurements and models contributing to these will be assessed using data from front row wind turbines in wind farms with long measurement campaigns, to capture long term phenomena while avoiding modelling complexities that arise with turbine wakes. The primary method of bias and uncertainty quantification will involve conditional statistics, including comparison and separation of the contributions from different phenomena to biases. Further, time permitting, a model to capture the seasonality of the most impactful phenomena will be attempted."

Period	29 Jan 2024 → 29 Jun 2024