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...

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Main Authors: Dallman, Ann Renee, Khalil, Mohammad, Raghukumar, Kaus, Kasper, Jeremy, Jones, Craig, Roberts, Jesse D.
Language:unknown
Published: 2018
Subjects:
Online Access:http://www.osti.gov/servlets/purl/1481640
https://www.osti.gov/biblio/1481640
https://doi.org/10.2172/1481640
id ftosti:oai:osti.gov:1481640
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spelling ftosti:oai:osti.gov:1481640 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. 2018-12-07 application/pdf http://www.osti.gov/servlets/purl/1481640 https://www.osti.gov/biblio/1481640 https://doi.org/10.2172/1481640 unknown http://www.osti.gov/servlets/purl/1481640 https://www.osti.gov/biblio/1481640 https://doi.org/10.2172/1481640 doi:10.2172/1481640 32 ENERGY CONSERVATION CONSUMPTION AND UTILIZATION 2018 ftosti https://doi.org/10.2172/1481640 2023-07-11T09:29:59Z 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
topic 32 ENERGY CONSERVATION
CONSUMPTION
AND UTILIZATION
spellingShingle 32 ENERGY CONSERVATION
CONSUMPTION
AND UTILIZATION
Dallman, Ann Renee
Khalil, Mohammad
Raghukumar, Kaus
Kasper, Jeremy
Jones, Craig
Roberts, Jesse D.
Improved Wave Energy Production Forecasts for Smart Grid Integration
topic_facet 32 ENERGY CONSERVATION
CONSUMPTION
AND UTILIZATION
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.
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 2018
url http://www.osti.gov/servlets/purl/1481640
https://www.osti.gov/biblio/1481640
https://doi.org/10.2172/1481640
genre Yakutat
Alaska
genre_facet Yakutat
Alaska
op_relation http://www.osti.gov/servlets/purl/1481640
https://www.osti.gov/biblio/1481640
https://doi.org/10.2172/1481640
doi:10.2172/1481640
op_doi https://doi.org/10.2172/1481640
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