Improved Wave Energy Production Forecasts for Smart Grid Integration
Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC a...
Main Authors: | , , , , , |
---|---|
Language: | unknown |
Published: |
2020
|
Subjects: | |
Online Access: | http://www.osti.gov/servlets/purl/1531318 https://www.osti.gov/biblio/1531318 https://doi.org/10.2172/1531318 |
id |
ftosti:oai:osti.gov:1531318 |
---|---|
record_format |
openpolar |
spelling |
ftosti:oai:osti.gov:1531318 2023-07-30T04:07:26+02:00 Improved Wave Energy Production Forecasts for Smart Grid Integration Dallman, Ann Renee Khalil, Mohammad Raghukumar, Kaus Kasper, Jeremy Jones, Craig Roberts, Jesse D. 2020-01-13 application/pdf http://www.osti.gov/servlets/purl/1531318 https://www.osti.gov/biblio/1531318 https://doi.org/10.2172/1531318 unknown http://www.osti.gov/servlets/purl/1531318 https://www.osti.gov/biblio/1531318 https://doi.org/10.2172/1531318 doi:10.2172/1531318 2020 ftosti https://doi.org/10.2172/1531318 2023-07-11T09:34:34Z Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC array) lease area are needed to improve WEC power prediction and to facilitate grid integration, particularly for microgrid locations. The availability of high quality measurement data from recently developed low-cost buoys allows for operational assimilation of wave data into forecast models 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. Spoondrift wave measurement buoys were deployed off the coast of Yakutat, Alaska, a microgrid site with high wave energy resource potential. A wave modeling framework with data assimilation was developed and assessed, which was most effective when the incoming forecasted boundary conditions did not represent the observations well. For that case, assimilation of the wave height data using the ensemble Kalman filter resulted in a reduction of wave height forecast normalized root mean square error from 27% to an average of 16% over a 12-hour period. This results in reduction of wave power forecast error from 73% to 43%. In summary, the use of the low-cost wave buoy data assimilated into the wave modeling framework improved the forecast skill and will provide a useful development tool for the integration of WECs into electrical grids. Other/Unknown Material Yakutat Alaska SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
institution |
Open Polar |
collection |
SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
op_collection_id |
ftosti |
language |
unknown |
description |
Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC array) lease area are needed to improve WEC power prediction and to facilitate grid integration, particularly for microgrid locations. The availability of high quality measurement data from recently developed low-cost buoys allows for operational assimilation of wave data into forecast models 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. Spoondrift wave measurement buoys were deployed off the coast of Yakutat, Alaska, a microgrid site with high wave energy resource potential. A wave modeling framework with data assimilation was developed and assessed, which was most effective when the incoming forecasted boundary conditions did not represent the observations well. For that case, assimilation of the wave height data using the ensemble Kalman filter resulted in a reduction of wave height forecast normalized root mean square error from 27% to an average of 16% over a 12-hour period. This results in reduction of wave power forecast error from 73% to 43%. In summary, the use of the low-cost wave buoy data assimilated into the wave modeling framework improved the forecast skill and will provide a useful development tool for the integration of WECs into electrical grids. |
author |
Dallman, Ann Renee Khalil, Mohammad Raghukumar, Kaus Kasper, Jeremy Jones, Craig Roberts, Jesse D. |
spellingShingle |
Dallman, Ann Renee Khalil, Mohammad Raghukumar, Kaus Kasper, Jeremy Jones, Craig Roberts, Jesse D. Improved Wave Energy Production Forecasts for Smart Grid Integration |
author_facet |
Dallman, Ann Renee Khalil, Mohammad Raghukumar, Kaus Kasper, Jeremy Jones, Craig Roberts, Jesse D. |
author_sort |
Dallman, Ann Renee |
title |
Improved Wave Energy Production Forecasts for Smart Grid Integration |
title_short |
Improved Wave Energy Production Forecasts for Smart Grid Integration |
title_full |
Improved Wave Energy Production Forecasts for Smart Grid Integration |
title_fullStr |
Improved Wave Energy Production Forecasts for Smart Grid Integration |
title_full_unstemmed |
Improved Wave Energy Production Forecasts for Smart Grid Integration |
title_sort |
improved wave energy production forecasts for smart grid integration |
publishDate |
2020 |
url |
http://www.osti.gov/servlets/purl/1531318 https://www.osti.gov/biblio/1531318 https://doi.org/10.2172/1531318 |
genre |
Yakutat Alaska |
genre_facet |
Yakutat Alaska |
op_relation |
http://www.osti.gov/servlets/purl/1531318 https://www.osti.gov/biblio/1531318 https://doi.org/10.2172/1531318 doi:10.2172/1531318 |
op_doi |
https://doi.org/10.2172/1531318 |
_version_ |
1772820740973789184 |