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Title: Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

Abstract

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. Our 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.

Authors:
 [1]; ORCiD logo [1]
  1. Univ. of Colorado, Boulder, CO (United States). Dept. of Atmospheric and Oceanic Sciences; National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Science Foundation (NSF)
OSTI Identifier:
1414070
Report Number(s):
NREL/JA-5000-70672
Journal ID: ISSN 1991-9603
Grant/Contract Number:  
AC36-08GO28308; 1413980
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 10; Journal Issue: 11; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; wind farm parameterization; wind power production; grid integration

Citation Formats

Lee, Joseph C. Y., and Lundquist, Julie K. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data. United States: N. p., 2017. Web. doi:10.5194/gmd-10-4229-2017.
Lee, Joseph C. Y., & Lundquist, Julie K. Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data. United States. doi:10.5194/gmd-10-4229-2017.
Lee, Joseph C. Y., and Lundquist, Julie K. Thu . "Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data". United States. doi:10.5194/gmd-10-4229-2017. https://www.osti.gov/servlets/purl/1414070.
@article{osti_1414070,
title = {Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data},
author = {Lee, Joseph C. Y. and Lundquist, Julie K.},
abstractNote = {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. Our 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.},
doi = {10.5194/gmd-10-4229-2017},
journal = {Geoscientific Model Development (Online)},
number = 11,
volume = 10,
place = {United States},
year = {2017},
month = {11}
}

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