![]() ![]() Wind power forecasting, turbine micrositing, and turbine design require high-resolution simulations of atmospheric flow. Enhanced sophistication of microphysics parameterizations also helps increase resolved wind speeds, improve storm timing and structure and resolve higher values of turbulent kinetic energy in the lowest 1 km of the atmosphere. Enhanced horizontal resolution with sim-plistic parameterization helps increase resolved wind speeds and ramp intensity. Results are also compared with the Wind Integration National Dataset, which utilizes data assimilation and an extensive continental domain. Parameterizations of the planetary boundary layer and microphysics processes are evaluated based on their impact on storm dynamics and the low-level wind field. This work addresses model spatial resolution versus parameteri-zation complexity. Thus, Weather Research and Forecasting model simulations of the event are carried out that consider current and anticipated future operational model setups. Even with these datasets, assessing wind speeds around turbine rotors is difficult. Storm reports from the Storm Prediction Center and damage surveys provided additional insight to the in situ measurements. The observational network included NEXRAD radars, automated surface observation stations and a wind profiler. In this study, we attempt to characterize meteorological conditions over the Buffalo Ridge Wind Farm area during this event. At this southwestern Minnesota site, blades from multiple turbines broke away and a tower buckled in the intense winds. They can also cause extreme structural damage to turbines as was seen in the severe storm event over the Buffalo Ridge Wind Farm on July 1, 2011. ![]() They can cause significant power ramps and disruption in energy production. Severe winds from thunderstorm outflows pose a challenge to wind turbine arrays. Obvious cases of non-weather curtailments and shutdowns were excluded.Ģ) Examined available meteorological records for the same periods and categorized the events by different meteorological causes.ģ) Analyzed significant 2005-2006 weather events identified by ERCOT and determined which of those were associated with large changes in generation.Ĥ) Analyzed the event of 24 February 2007 and established the cause for the decrease in energy production.įrom the results of the above analysis, AWS Truewind estimated the maximum likely change in a 30-minute period for the 15,000 MW scenario defined in the Ancillary Services study. To identify and classify events, AWS Truewind:ġ) Examined two years of one-minute plant output data provided by ERCOT and identified periods in which theĪggregate wind generation increased or decreased by more than 200 MW in a 30-minute time frame (out of a total MW of 976 rated capacity). The purpose of this report is to document weather-related causes in sudden excursions in wind power output from 14 interconnection points in the Electric Reliability Council of Texas (ERCOT) domain in 20 as well as a singular event in 2007, and to attempt to extrapolate the findings to a much larger deployment of wind energy in Texas. ![]()
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