Characterisation of offshore winds for energy applications

Abstract

Offshore wind energy is regarded as one of the main ingredients of a sustainable energy system. A popular scenario for 2050 describes a stunning fleet of twenty-five thousand wind turbines that is to be installed in the North Sea. Obviously, the realisation of such a grand vision requires realistic estimates of the expected energy production, optimal wind farm layouts, and lifetime of various turbine designs. A wide range of computer models is used to simulate, for example, the interaction between the wind and the turbines. As inputs, they require (climatological) information about the wind field. An inherent property of model simulations and associated predictions is that they come with uncertainties. There is always a chance that reality will deviate from the anticipated course. Since offshore wind projects require large investments, these uncertainties naturally transform into financial risks. Therefore, a growing interest emerges in uncertainty quantification and reduction. This thesis addresses uncertainties that originate from the representation of the offshore wind field in the aforementioned engineering models. This goal is partly achieved through crossvalidation between available observations, output from meteorological models, and combined products of observations and meteorological model simulations that are known as reanalysis datasets. Additionally, the thesis reflects on the impact of (over)simplifications that are commonly used to reduce computational demands. Standard inflow specifications for engineering models have been built around highly idealized descriptions of the wind, such as the Weibull distribution, the logarithmic wind profile, and the 'wind rose'. With the growth of individual wind turbines, offshore wind projects, and the corresponding investments, these simplified descriptions are becoming inadequate. Moreover, contemporary design standards consider conditions of extreme winds, extreme shear, wind gusts, etcetera in an abstract statistical fashion that does not reflect the physical nature of these events. Chapter 1 further elaborates on the different models, data sources, assumptions and simplifications, and uncertainties.

Clustering wind profile shapes to estimate airborne wind energy production

“…Furthermore, additional observations from the KNMI's network of automated weather stations, satellite retrievals (ASCAT), and aircraft sensors (MODE-S EHS) have been assimilated by the HARMONIE-AROME model. Kalverla (2019) shows that DOWA improves on ERA5 in terms of wind speed, wind shear, and directional accuracy as well as the representation of anomalous events such as lowlevel jets.…”

Clustering wind profile shapes to estimate airborne wind energy production

“…Additionally, Kalverla (2019) show that it outperforms ERA5 in terms of wind speed and directional accuracy, as well as the representation of anomalous events such as low-level jets and wind shear. 4 https://doi.org/10.5194/wes-2019-108 Preprint.…”