NCAR's Recent Advances in Wind Power Forecasting

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dc.contributorNational Center for Atmospheric Research (U.S.). Applications Laboratoryen
dc.contributorVirginia Tech. Aerospace and Ocean Engineering Departmenten
dc.contributor.authorHaupt, Sueen
dc.contributor.authorKosovic, Brankoen
dc.contributor.authorWiener, Gerryen
dc.contributor.authorDelle Monache, Lucaen
dc.contributor.authorLiu, Yubaoen
dc.contributor.authorLinden, Sethen
dc.contributor.authorPolitovich, Marciaen
dc.contributor.authorSun, Jennyen
dc.date.accessioned2015-07-28T18:27:12Zen
dc.date.available2015-07-28T18:27:12Zen
dc.date.issued2015-06-11en
dc.description.abstractThe National Center for Atmospheric Research (NCAR) has been developing and enhancing a Wind Power Forecasting System in partnership with Xcel Energy that integrates high resolution and publically available modeling capabilities with artificial intelligence methods. This forecasting system has recently been updated to include specific technologies for uncertainty quantification, short-term detection of wind power ramps by blending the Variational Doppler Radar Analysis System with an Expert System, enhanced wind-to-power conversion techniques, and prediction of icing and snow conditions. The system ingests external, publicly available weather model data and observations. In order to provide information specific to Xcel Energy's region, high resolution numerical weather prediction (NWP) simulations assimilate specific local weather observations. The weather observations range from routine meteorological surface and upper air data to data from the wind farms, including wind speed data from the Nacelles. Finally, to optimize estimates of short-term changes in wind power requires nowcasting technologies such as the Variational Doppler Radar Analysis System (VDRAS) that is blended with an observation-based Expert System. The strength of the wind power forecasting system lies in blending the various components to produce a power forecast that can be used by Xcel Energy grid operators and energy traders. That blending is accomplished with the Dynamical Integrated Forecast (DICast) System. The wind speeds predicted by DICast must then be translated into power using NCAR's empirical power conversion algorithms. A quantile approach is used to quality control the observations. The uncertainty of these predictions can then be quantified using an Analog Ensemble (AnEn) approach. Finally, warnings of potential icing and heavy snow are provided.en
dc.description.notesSession 6B - Atmospheric Science of Wind Characterization, and Forecastingen
dc.format.extent22 pagesen
dc.format.mimetypeapplication/vnd.ms-powerpointen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHaupt, S., Kosovic, B., Wiener, G., Delle Monache, L., Liu, Y., Linden, S., Politovich, M., & Sun, J. (2015, June). Ncar's recent advances in wind power forecasting. Paper presented at the North American Wind Energy Academy 2015 Symposium, Blacksburg, VA.en
dc.identifier.urihttp://hdl.handle.net/10919/54686en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.relation.ispartofNorth American Wind Energy Academy 2015 Symposiumen
dc.rightsIn Copyrighten
dc.rights.holderHaupt, Sueen
dc.rights.holderKosovic, Brankoen
dc.rights.holderWiener, Gerryen
dc.rights.holderDelle Monache, Lucaen
dc.rights.holderLiu, Yubaoen
dc.rights.holderLinden, Sethen
dc.rights.holderPolitovich, Marciaen
dc.rights.holderSun, Jennyen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleNCAR's Recent Advances in Wind Power Forecastingen
dc.typePresentationen
dc.type.dcmitypeTexten

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