Abstract
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 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.
Highlights
In recent years, numerical weather prediction (NWP) models have become an indispensable tool in the wind-energy industry, in day-to-day wind-energy production forecasts (Wilczak et al, 2015), and to support wide-scale windpower penetration (Marquis et al, 2011) and wind resource assessment
Whereas wind farm parameterization (WFP) predictions have been compared to power production of offshore wind farms for a limited set of wind speed (WS) (Jiménez et al, 2015), here we explore a range of WSs, wind direction (WD), turbulence, and atmospheric stability conditions
The Weather Research and Forecasting (WRF) model simulations without the WFP simulate accurate ambient winds compared to the lidar measurements
Summary
Numerical weather prediction (NWP) models have become an indispensable tool in the wind-energy industry, in day-to-day wind-energy production forecasts (Wilczak et al, 2015), and to support wide-scale windpower penetration (Marquis et al, 2011) and wind resource assessment. To forecast power production accurately at wind farms, the simulation tools should resolve all physical processes relevant to the wind field, including possible impacts of the wind turbines themselves. Including the meteorological effects of wind farms in NWP models can improve power-production forecasts. Simulating wind turbines and their effects in LESs is, while useful, computationally expensive, making wind-farm-scale simulations unreasonable in an operational setting
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