Utility-scale large wind farms are rapidly growing in size and numbers all over the world. Data from a meteorological field campaign show that such wind farms can significantly affect near-surface air temperatures. These effects result from enhanced vertical mixing due to turbulence generated by wind turbine rotors. The impacts of wind farms on local weather can be minimized by changing rotor design or by siting wind farms in regions with high natural turbulence. Using a 25-y-long climate dataset, we identified such regions in the world. Many of these regions, such as the Midwest and Great Plains in the United States, are also rich in wind resources, making them ideal candidates for low-impact wind farms.impact assessment | regional climate model | sustainable energy | wind energy | wind power potential W ind power is one of the fastest growing energy sources in the world. Most of this growth is in the industrial sector based on large utility-scale wind farms (1). Recent studies have investigated the possible impacts of such wind farms on global and local weather and climate. Although debates exist regarding the global-scale effects of wind farms (2-5), modeling studies agree that wind farms can significantly affect local-scale meteorology (6, 7). However, these studies are based only on model simulations and are not validated against observational evidence. In this paper, we used field data and numerical experiments with a regional climate model to answer the following critical questions arising from the prior studies:i. Does observational evidence show that wind farms affect nearsurface air temperatures? ii. Can atmospheric models replicate the observed patterns of near-surface air temperatures within wind farms? iii. How can these impacts be minimized to ensure long-term sustainability of wind power? Observed Impacts of Wind FarmsAlthough observed data on wind speed and turbulence in and around operational wind farms are readily available, information on other meteorological variables do not exist in the public domain. The only available information is temperature data from a wind farm at San Gorgonio, California, collected during June 18-August 9, 1989 (Fig. 1). To the best of our knowledge, this is the only meteorological field campaign conducted in an operational wind farm. The wind farm consisted of 23-m-tall turbines with 8.5-m-long rotor blades arranged in 41 rows that were spaced 120 m apart. Data from the field campaign show that near-surface air temperatures downwind of the wind farm are higher than upwind regions during night and early morning hours, whereas the reverse holds true for the rest of the day (Fig. 2A). Thus, this wind farm has a warming effect during the night and a cooling effect during the day. The observed temperature signal is statistically significant for most of the day according to the results of a Mann-Whitney Rank Sum Test (Table 1).A possible explanation for this phenomenon can be drawn from the hypothesis proposed by Baidya Roy et al. that turbulence generated in the wake of ...
Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy's (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in windspeed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.iii
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