Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data
Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-11-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/10/4229/2017/gmd-10-4229-2017.pdf |
| _version_ | 1818948841335947264 |
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| author | J. C. Y. Lee J. C. Y. Lee J. K. Lundquist J. K. Lundquist |
| author_facet | J. C. Y. Lee J. C. Y. Lee J. K. Lundquist J. K. Lundquist |
| author_sort | J. C. Y. Lee |
| collection | DOAJ |
| description | Forecasts of wind-power
production are necessary to facilitate the integration of wind energy into
power grids, and these forecasts should incorporate the impact of
wind-turbine wakes. This paper focuses on a case study of four diurnal cycles
with significant power production, and assesses the skill of the wind farm
parameterization (WFP) distributed with the Weather Research and Forecasting
(WRF) model version 3.8.1, as well as its sensitivity to model configuration.
After validating the simulated ambient flow with observations, we quantify
the value of the WFP as it accounts for wake impacts on power production of
downwind turbines. We also illustrate with statistical significance that a
vertical grid with approximately 12 m
vertical resolution is necessary for reproducing the observed power
production. Further, the WFP overestimates wake effects and hence
underestimates downwind power production during high wind speed, highly
stable, and low turbulence conditions. We also find the WFP performance is
independent of the number of wind turbines per model grid cell and the
upwind–downwind position of turbines. Rather, the ability of the WFP to
predict power production is most dependent on the skill of the WRF model in
simulating the ambient wind speed. |
| first_indexed | 2024-12-20T08:53:12Z |
| format | Article |
| id | doaj.art-67e9a1b8a5e447c482186fe6c7e88f24 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-20T08:53:12Z |
| publishDate | 2017-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-67e9a1b8a5e447c482186fe6c7e88f242022-12-21T19:46:05ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032017-11-01104229424410.5194/gmd-10-4229-2017Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power dataJ. C. Y. Lee0J. C. Y. Lee1J. K. Lundquist2J. K. Lundquist3Department of Atmospheric and Oceanic Sciences, University of Colorado, UCB 311, Boulder, CO 80309, USANational Renewable Energy Laboratory, Golden, CO, USADepartment of Atmospheric and Oceanic Sciences, University of Colorado, UCB 311, Boulder, CO 80309, USANational Renewable Energy Laboratory, Golden, CO, USAForecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.https://www.geosci-model-dev.net/10/4229/2017/gmd-10-4229-2017.pdf |
| spellingShingle | J. C. Y. Lee J. C. Y. Lee J. K. Lundquist J. K. Lundquist Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data Geoscientific Model Development |
| title | Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data |
| title_full | Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data |
| title_fullStr | Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data |
| title_full_unstemmed | Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data |
| title_short | Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data |
| title_sort | evaluation of the wind farm parameterization in the weather research and forecasting model version 3 8 1 with meteorological and turbine power data |
| url | https://www.geosci-model-dev.net/10/4229/2017/gmd-10-4229-2017.pdf |
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