Wave Data Assimilation in Support of Wave Energy Converter Power Prediction: Yakutat, Alaska Case Study
- Ann Dallman (Sandia National Laboratories) | Mohammad Khalil (Sandia National Laboratories) | Kaus Raghukumar (Integral Consulting, Inc.) | Craig Jones (Integral Consulting, Inc.) | Jeremy Kasper (University of Alaska Fairbanks) | Christopher Flanary (Integral Consulting, Inc.) | Grace Chang (Integral Consulting, Inc.) | Jesse Roberts (Sandia National Laboratories)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- wave energy, data assimilation, wave power forecasting
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- 38 since 2007
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Integration of renewable power sources into grids remains an active research and development area, particularly for less developed renewable energy technologies such as wave energy converters (WECs). WECs are projected to have strong early market penetration for remote communities, which serve as natural microgrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgrids is especially important. Recently developed, low-cost wave measurement buoys allow for operational assimilation of wave data at remote locations where real-time data have previously been unavailable.
This work includes the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. The availability of real-time wave spectral components from low-cost wave measurement buoys allows for operational data assimilation with the Ensemble Kalman filter technique, whereby measured wave conditions within the numerical wave forecast model domain are assimilated onto the combined set of internal and boundary grid points while taking into account model and observation error covariances. The updated model state and boundary conditions allow for more accurate wave characteristic predictions at the locations of interest.
Initial deployment data indicated that measured wave data from one buoy that were assimilated into the wave modeling framework resulted in improved forecast skill for a case where a traditional numerical forecast model (e.g., Simulating WAves Nearshore; SWAN) did not well represent the measured conditions. On average, the wave power forecast error was reduced from 73% to 43% using the data assimilation modeling with real-time wave observations.
|File Size||651 KB||Number of Pages||7|
Raghukumar, K., G. Chang, F. Spada, C. Jones, W. Gans, and T. Janssen. 2019. Performance characteristic of Spotter, a newly developed real-time wave measurement buoy. J. Atmos. Ocean. Tech. http://dx.doi.org/doi:10.1175/JTECH-D-18-0151.1.