UC Berkeley

Energy production from wind is an increasingly important component of overall

global power generation, and will likely continue to gain an even greater share

of electricity production as world governments attempt to mitigate climate

change and wind energy production costs decrease. Wind energy generation

depends on wind speed, which is greatly influenced by local and synoptic

environmental forcings. Synoptic forcing, such as a cold frontal passage,

exists on a large spatial scale while local forcing manifests itself on a much

smaller scale and could result from topographic effects or land-surface heat

fluxes. Synoptic forcing, if strong enough, may suppress the effects of

generally weaker local forcing. At the even smaller scale of a wind farm,

upstream turbines generate wakes that decrease the wind speed and increase the

atmospheric turbulence at the downwind turbines, thereby reducing power

production and increasing fatigue loading that may damage turbine components,

respectively. Simulation of atmospheric processes that span a considerable

range of spatial and temporal scales is essential to improve wind energy

forecasting, wind turbine siting, turbine maintenance scheduling, and wind

turbine design.

Mesoscale atmospheric models predict atmospheric conditions using observed

data, for a wide range of meteorological applications across scales from

thousands of kilometers to hundreds of meters. Mesoscale models include

parameterizations for the major atmospheric physical processes that modulate

wind speed and turbulence dynamics, such as cloud evolution and

surface-atmosphere interactions. The Weather Research and Forecasting (WRF)

model is used in this dissertation to investigate the effects of model

parameters on wind energy forecasting. WRF is used for case study simulations

at two West Coast North American wind farms, one with simple and one with

complex terrain, during both synoptically and locally-driven weather events.

The model's performance with different grid nesting configurations, turbulence

closures, and grid resolutions is evaluated by comparison to observation data.

Improvement to simulation results from the use of more computationally

expensive high resolution simulations is only found for the complex terrain

simulation during the locally-driven event. Physical parameters, such as soil

moisture, have a large effect on locally-forced events, and prognostic

turbulence kinetic energy (TKE) schemes are found to perform better than

non-local eddy viscosity turbulence closure schemes.

Mesoscale models, however, do not resolve turbulence directly, which is

important at finer grid resolutions capable of resolving wind turbine

components and their interactions with atmospheric turbulence. Large-eddy

simulation (LES) is a numerical approach that resolves the largest scales of

turbulence directly by separating large-scale, energetically important eddies

from smaller scales with the application of a spatial filter. LES allows

higher fidelity representation of the wind speed and turbulence intensity at

the scale of a wind turbine which parameterizations have difficulty

representing. Use of high-resolution LES enables the implementation of more

sophisticated wind turbine parameterizations to create a robust model for wind

energy applications using grid spacing small enough to resolve individual

elements of a turbine such as its rotor blades or rotation area.

Generalized actuator disk (GAD) and line (GAL) parameterizations are integrated

into WRF to complement its real-world weather modeling capabilities and better

represent wind turbine airflow interactions, including wake effects. The GAD

parameterization represents the wind turbine as a two-dimensional disk

resulting from the rotation of the turbine blades. Forces on the atmosphere are

computed along each blade and distributed over rotating, annular rings

intersecting the disk. While typical LES resolution (10-20 m) is normally

sufficient to resolve the GAD, the GAL parameterization requires significantly

higher resolution (1-3 m) as it does not distribute the forces from the blades

over annular elements, but applies them along lines representing individual

blades.

In this dissertation, the GAL is implemented into WRF and evaluated against the

GAD parameterization from two field campaigns that measured the inflow and

near-wake regions of a single turbine. The data-sets are chosen to allow

validation under the weakly convective and weakly stable conditions

characterizing most turbine operations. The parameterizations are evaluated

with respect to their ability to represent wake wind speed, variance, and

vorticity by comparing fine-resolution GAD and GAL simulations along with

coarse-resolution GAD simulations. Coarse-resolution GAD simulations produce

aggregated wake characteristics similar to both GAD and GAL simulations (saving

on computational cost), while the GAL parameterization enables resolution of

near wake physics (such as vorticity shedding and wake expansion) for high

fidelity applications.

For the first time, to our knowledge, this dissertation combines the

capabilities of a mesoscale weather prediction model, LES, and high-resolution

wind turbine parameterizations into one model capable of simulating a real

array of wind turbines at a wind farm. WRF is used due to its sophisticated

environmental physics models, frequent use in the atmospheric modeling

community, and grid nesting with LES capabilities. Grid nesting is feeding

lateral boundary condition data from a coarse resolution simulation to a finer

resolution simulation contained within the coarse resolution simulation's

domain. WRF allows the development of a grid nesting strategy from

synoptic-scale to microscale LES relevant for wind farm simulations; this is

done by building on the results from the investigation of model parameters for

wind energy forecasting and the implementation of the GAD and GAL wind turbine

parameterizations. The nesting strategy is coupled with a GAD parameterization

to model the effects of wind turbine wakes on downstream turbines at a

utility-scale Oklahoma wind farm. Simulation results are compared to

dual-Doppler measurements that provide three-dimensional fields of horizontal

wind speed and direction. The nesting strategy is able to produce realistic

turbine wake effects, while differences with the measurements can mostly be

attributed to the quality of the available weather input data.