Simulation of the Atmospheric Boundary Layer for Wind Energy Applications

Energy production from wind is an increasingly important component of overallglobal power generation, and will likely continue to gain an even greater shareof electricity production as world governments attempt to mitigate climatechange and wind energy production costs decrease. Wind energy generationdepends on wind speed, which is greatly influenced by local and synopticenvironmental forcings. Synoptic forcing, such as a cold frontal passage,exists on a large spatial scale while local forcing manifests itself on a muchsmaller scale and could result from topographic effects or land-surface heatfluxes. Synoptic forcing, if strong enough, may suppress the effects ofgenerally weaker local forcing. At the even smaller scale of a wind farm,upstream turbines generate wakes that decrease the wind speed and increase theatmospheric turbulence at the downwind turbines, thereby reducing powerproduction and increasing fatigue loading that may damage turbine components,respectively. Simulation of atmospheric processes that span a considerablerange of spatial and temporal scales is essential to improve wind energyforecasting, wind turbine siting, turbine maintenance scheduling, and windturbine design.Mesoscale atmospheric models predict atmospheric conditions using observeddata, for a wide range of meteorological applications across scales fromthousands of kilometers to hundreds of meters. Mesoscale models includeparameterizations for the major atmospheric physical processes that modulatewind speed and turbulence dynamics, such as cloud evolution andsurface-atmosphere interactions. The Weather Research and Forecasting (WRF)model is used in this dissertation to investigate the effects of modelparameters on wind energy forecasting. WRF is used for case study simulationsat two West Coast North American wind farms, one with simple and one withcomplex terrain, during both synoptically and locally-driven weather events.The model's performance with different grid nesting configurations, turbulenceclosures, and grid resolutions is evaluated by comparison to observation data.Improvement to simulation results from the use of more computationallyexpensive high resolution simulations is only found for the complex terrainsimulation during the locally-driven event. Physical parameters, such as soilmoisture, have a large effect on locally-forced events, and prognosticturbulence kinetic energy (TKE) schemes are found to perform better thannon-local eddy viscosity turbulence closure schemes.Mesoscale models, however, do not resolve turbulence directly, which isimportant at finer grid resolutions capable of resolving wind turbinecomponents and their interactions with atmospheric turbulence. Large-eddysimulation (LES) is a numerical approach that resolves the largest scales ofturbulence directly by separating large-scale, energetically important eddiesfrom smaller scales with the application of a spatial filter. LES allowshigher fidelity representation of the wind speed and turbulence intensity atthe scale of a wind turbine which parameterizations have difficultyrepresenting. Use of high-resolution LES enables the implementation of moresophisticated wind turbine parameterizations to create a robust model for windenergy applications using grid spacing small enough to resolve individualelements of a turbine such as its rotor blades or rotation area. Generalized actuator disk (GAD) and line (GAL) parameterizations are integratedinto WRF to complement its real-world weather modeling capabilities and betterrepresent wind turbine airflow interactions, including wake effects. The GADparameterization represents the wind turbine as a two-dimensional diskresulting from the rotation of the turbine blades. Forces on the atmosphere arecomputed along each blade and distributed over rotating, annular ringsintersecting the disk. While typical LES resolution (10-20 m) is normallysufficient to resolve the GAD, the GAL parameterization requires significantlyhigher resolution (1-3 m) as it does not distribute the forces from the bladesover annular elements, but applies them along lines representing individualblades. In this dissertation, the GAL is implemented into WRF and evaluated against theGAD parameterization from two field campaigns that measured the inflow andnear-wake regions of a single turbine. The data-sets are chosen to allowvalidation under the weakly convective and weakly stable conditionscharacterizing most turbine operations. The parameterizations are evaluatedwith respect to their ability to represent wake wind speed, variance, andvorticity by comparing fine-resolution GAD and GAL simulations along withcoarse-resolution GAD simulations. Coarse-resolution GAD simulations produceaggregated wake characteristics similar to both GAD and GAL simulations (savingon computational cost), while the GAL parameterization enables resolution ofnear wake physics (such as vorticity shedding and wake expansion) for highfidelity applications. For the first time, to our knowledge, this dissertation combines thecapabilities of a mesoscale weather prediction model, LES, and high-resolutionwind turbine parameterizations into one model capable of simulating a realarray of wind turbines at a wind farm. WRF is used due to its sophisticatedenvironmental physics models, frequent use in the atmospheric modelingcommunity, and grid nesting with LES capabilities. Grid nesting is feedinglateral boundary condition data from a coarse resolution simulation to a finerresolution simulation contained within the coarse resolution simulation'sdomain. WRF allows the development of a grid nesting strategy fromsynoptic-scale to microscale LES relevant for wind farm simulations; this isdone by building on the results from the investigation of model parameters forwind energy forecasting and the implementation of the GAD and GAL wind turbineparameterizations. The nesting strategy is coupled with a GAD parameterizationto model the effects of wind turbine wakes on downstream turbines at autility-scale Oklahoma wind farm. Simulation results are compared todual-Doppler measurements that provide three-dimensional fields of horizontalwind speed and direction. The nesting strategy is able to produce realisticturbine wake effects, while differences with the measurements can mostly beattributed to the quality of the available weather input data