Posters

Abstract

The successful design of FOWTs depends heavily on accurate metocean data and incomplete or inaccurate data introduces greater uncertainty into the design process. As resource assessment is the first step in the energy conversion process, uncertainty in metocean data affects all subsequent stages, from predicting system performance to estimating final energy output. The uncertainty is particularly large when using climate model or re-analysis metocean datasets, such as ERA5. Therefore, this uncertainty, if not adequately treated, can become dangerous in the design process, resulting in poorly designed technologies. The present study analyses the potential impact of this uncertainty and suggests a bias correction technique to reduce the uncertainty. On the one hand, results show that the inaccuracy of the raw ERA5 metocean data is significant and inconsistent, since the wave resource is underestimated while overestimating the wind resource. consequently, this misestimations result in over 40% overestimation of the annual mean power production, while the design point for the structural integrity analysis of the floating offshore wind turbine is underestimated by almost 30%. In addition, the underestimation of the wave height also leads to underestimating the waiting time for the maintenance operations, which can have an important impact on the estimated availability of the wind farm. On the other hand, the bias correction technique shows the capacity to improve the metocean dataset and, as a consequence, the estimation of the different design paramteres. Except for the waiting time, all the analysed design parameters are approached to the values obtained based on the observation datasets, providing misestimations below 5%. In the case of the waiting time, the underestimation based on the corrected dataset is above that 5% threshold, but is reduced from the 50% obtained with the raw ERA5 dataset to slightly above 10%.