Abstract
A large challenge with numerically modeling wave energy converter (WEC) systems is the computational cost. Often models must choose between complexity and computational time. Additionally, the objective of many numerical simulations does not provide standardized measurements that follow power system requirements. These requirements need forecasted data with high sampling frequency for accurate energy predictions. Looking specifically at resource characterization, high temporal resolution datasets are not publicly available or do not exist for many coastal locations. Open-source data, like the NOAA Buoy Data Center and the DOE’s Water Power Technology Office’s US wave datasets, have low sample frequency which takes samples every 30-minutes to an hour. Due to data availability, these low temporal resolution datasets are being used in a majority of studies to generate representative wave conditions as inputs to numerical simulations. Representative wave conditions are used to generate wave spectrums. The issue with this practice is that spectrums are then used to predict the efficiency of systems that will not accurately capture the variability of waves in short timeframes. Creating a standardized methodology to increase the temporal resolution of metaocean conditions to inform model development can provide better forecasting of power production. Upsampling the datasets with low sample frequencies could provide a way to improve the forecasting without the need for high temporal datasets. In Robertson et al., an upsampling methodology was proposed and used to analyze WEC agnostic performance. This methodology demonstrated an increase in temporal variability of wave parameters. The annual mean of the system was found to also match the total energy from low temporal data while increasing the values of short-term wave parameters [1]. To expand on the research conducted by Robertson et al., there will be further investigation into resource upsampling. We will investigate the performance of Oregon State University’s Laboratory Upgrade Point Absorber (LUPA) by upsampling data with low temporal resolution. The performance of LUPA with different upsampled resource inputs will be compared against the performance of LUPA with high temporal resolution. Our objective is to determine if temporal upsampling improves the short-term wave predictions while accurately representing the power production of WECs. A better understanding of the upsampling accuracy will provide a better understanding of the direction the standardization methodology needs to take for resource modeling.