Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms
Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing
2024
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| Online Access: | https://hdl.handle.net/1721.1/156911 |
| _version_ | 1811070475967660032 |
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| author | Klemmer, Kerry S Condon, Emily P Howland, Michael F |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Klemmer, Kerry S Condon, Emily P Howland, Michael F |
| author_sort | Klemmer, Kerry S |
| collection | MIT |
| description | Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability. |
| first_indexed | 2024-09-23T08:36:38Z |
| format | Article |
| id | mit-1721.1/156911 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:36:38Z |
| publishDate | 2024 |
| publisher | AIP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1569112024-09-20T04:00:13Z Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms Klemmer, Kerry S Condon, Emily P Howland, Michael F Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability. 2024-09-19T20:49:49Z 2024-09-19T20:49:49Z 2024-01-01 2024-09-19T20:43:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/156911 Kerry S. Klemmer, Emily P. Condon, Michael F. Howland; Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms. J. Renewable Sustainable Energy 1 January 2024; 16 (1): 013302. en 10.1063/5.0166830 Journal of Renewable and Sustainable Energy Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf AIP Publishing AIP Publishing |
| spellingShingle | Klemmer, Kerry S Condon, Emily P Howland, Michael F Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title | Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title_full | Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title_fullStr | Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title_full_unstemmed | Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title_short | Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| title_sort | evaluation of wind resource uncertainty on energy production estimates for offshore wind farms |
| url | https://hdl.handle.net/1721.1/156911 |
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