Grid-scale fluctuations and forecast error in wind power
Wind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2016-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/18/2/023015 |
| _version_ | 1797750832776609792 |
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| author | G Bel C P Connaughton M Toots M M Bandi |
| author_facet | G Bel C P Connaughton M Toots M M Bandi |
| author_sort | G Bel |
| collection | DOAJ |
| description | Wind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid. We find that not only are grid-scale fluctuations temporally correlated up to a day, but they possess a self-similar structure—a signature of long-range correlations in atmospheric turbulence affecting wind power. Using the statistical structure of temporal correlations in fluctuations for generated and forecast power time series, we quantify two types of forecast error: a timescale error ( ${e}_{\tau }$ ) that quantifies deviations between the high frequency components of the forecast and generated time series, and a scaling error ( ${e}_{\zeta }$ ) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations for generated power. With no a priori knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ( ${e}_{\tau }$ ) and the scaling error ( ${e}_{\zeta }$ ). |
| first_indexed | 2024-03-12T16:38:36Z |
| format | Article |
| id | doaj.art-806d0ccd903d4e46a0a51b5e81fda2e5 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:38:36Z |
| publishDate | 2016-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-806d0ccd903d4e46a0a51b5e81fda2e52023-08-08T14:37:03ZengIOP PublishingNew Journal of Physics1367-26302016-01-0118202301510.1088/1367-2630/18/2/023015Grid-scale fluctuations and forecast error in wind powerG Bel0C P Connaughton1M Toots2M M Bandi3Department of Solar Energy and Environmental Physics, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev , Sede Boqer Campus 84990, IsraelCentre for Complexity Science, University of Warwick , Coventry CV4 7AL, UKCollective Interactions Unit, Okinawa Institute of Science and Technology Onna, Okinawa, 9040495, JapanCollective Interactions Unit, Okinawa Institute of Science and Technology Onna, Okinawa, 9040495, JapanWind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid. We find that not only are grid-scale fluctuations temporally correlated up to a day, but they possess a self-similar structure—a signature of long-range correlations in atmospheric turbulence affecting wind power. Using the statistical structure of temporal correlations in fluctuations for generated and forecast power time series, we quantify two types of forecast error: a timescale error ( ${e}_{\tau }$ ) that quantifies deviations between the high frequency components of the forecast and generated time series, and a scaling error ( ${e}_{\zeta }$ ) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations for generated power. With no a priori knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ( ${e}_{\tau }$ ) and the scaling error ( ${e}_{\zeta }$ ).https://doi.org/10.1088/1367-2630/18/2/023015wind powercorrelationsturbulence |
| spellingShingle | G Bel C P Connaughton M Toots M M Bandi Grid-scale fluctuations and forecast error in wind power New Journal of Physics wind power correlations turbulence |
| title | Grid-scale fluctuations and forecast error in wind power |
| title_full | Grid-scale fluctuations and forecast error in wind power |
| title_fullStr | Grid-scale fluctuations and forecast error in wind power |
| title_full_unstemmed | Grid-scale fluctuations and forecast error in wind power |
| title_short | Grid-scale fluctuations and forecast error in wind power |
| title_sort | grid scale fluctuations and forecast error in wind power |
| topic | wind power correlations turbulence |
| url | https://doi.org/10.1088/1367-2630/18/2/023015 |
| work_keys_str_mv | AT gbel gridscalefluctuationsandforecasterrorinwindpower AT cpconnaughton gridscalefluctuationsandforecasterrorinwindpower AT mtoots gridscalefluctuationsandforecasterrorinwindpower AT mmbandi gridscalefluctuationsandforecasterrorinwindpower |