| dc.description.abstract | Renewable energy is ever increasing in the modern energy grid with a growing portion from wind. Arctic communities are still largely dependent on fossil fuels, with up to 79% entirely dependent on diesel. Therein lies irony as arctic regions experience the effects of climate change up to 7 times greater than worldwide. Wind turbine design requires knowledge of horizontal wind speed at the hub height to estimate annual energy production. Such wind profiles can be studied using LiDAR data. However, LiDAR experiments are expensive to conduct. To cut costs and rapidly evaluate potential wind energy generation sites, extrapolation methods are applied to pre-existing wind speed measurements taken at a standard 10 m height. Wind speed profiles are constructed from measurements taken in Adventdalen on Svalbard during the summer of 2022 using an automatic weather station (AWS) and LiDAR. The LiDAR constructed profiles are a baseline for comparison while the AWS data is used solely to construct model profiles. Four models are evaluated: the power law following IEC-64100-3 standard, a power law variation proposed by Sedefian, the log law, and log law with stability correction. The models are found to be dependent on stability as previously found in other regions. However, the LiDAR baseline shows a decrease in wind speed with height making all models tested difficult to use. It is also found that the IEC method largely overestimates wind speed. The power law proposed by Sedefian is found to perform similarly to the log law with stability correction. The log law with stability correction is more sensitive to input parameters and therefore less robust against less sophisticated instrumentation. The Sedefian variation is found to be the least sensitive to wind speed errors. | |
