Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-128
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model evaluation paper
08 Jun 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data
Joseph C. Y. Lee1,2 and Julie K. Lundquist1,2 1Department of Atmospheric and Oceanic Sciences, University of Colorado, UCB 311, Boulder, CO 80309, USA
2National Renewable Energy Laboratory, Golden, CO, USA
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. 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 that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, 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.

Citation: Lee, J. C. Y. and Lundquist, J. K.: Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-128, in review, 2017.
Joseph C. Y. Lee and Julie K. Lundquist

Data sets

PSU generic 1.5 MW turbine
S. Schmitz
https://doi.org/10.13140/RG.2.2.22492.18567

Model code and software

The WRF ARW
W. C. Skamarock, J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers
https://doi.org/10.5065/D6MK6B4K
User input for the WFP of the WRF model (v 3.8.1)
J. C. Y. Lee and J. K. Lundquist
https://doi.org/10.5281/zenodo.437166
Joseph C. Y. Lee and Julie K. Lundquist

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Short summary
We evaluate the the wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model, a powerful tool to simulate wind farms and their meteorological impacts numerically. In our case study, the WFP-simulations with fine vertical grid resolution are skillful in matching the winds and the power production to the observations. Moreover, the WFP tends to underestimate power in windy conditions. We also illustrate the modeled wind speed is the critical factor influencing the WFP.
We evaluate the the wind farm parameterization (WFP) in the Weather Research and Forecasting...
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