Interaction Between the Atmospheric Boundary Layer and Wind Energy: from Continental-Scale to Turbine-Scale Written by Clara Mae St
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INTERACTION BETWEEN THE ATMOSPHERIC BOUNDARY LAYER AND WIND ENERGY: FROM CONTINENTAL-SCALE TO TURBINE-SCALE by CLARA MAE ST. MARTIN B.S., the Pennsylvania State University, 2012 M.S., University of Colorado, 2015 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of Doctor of Philosophy Department of Atmospheric and Oceanic Sciences 2017 This thesis entitled: Interaction between the atmospheric boundary layer and wind energy: from continental-scale to turbine-scale written by Clara Mae St. Martin has been approved for the Department of Atmospheric and Oceanic Sciences Prof. Julie Lundquist Prof. Jeffrey Weiss Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. St. Martin, Clara Mae (PhD., Atmospheric and Oceanic Sciences) Interaction between the atmospheric boundary layer and wind energy: from continental-scale to turbine-scale Thesis directed by Associate Professor Julie K. Lundquist Abstract Wind turbines and groups of wind turbines, or “wind plants”, interact with the complex and heterogeneous boundary layer of the atmosphere. We define the boundary layer as the portion of the atmosphere directly influenced by the surface, and this layer exhibits variability on a range of temporal and spatial scales. While early developments in wind energy could ignore some of this variability, recent work demonstrates that improved understanding of atmosphere- turbine interactions leads to the discovery of new ways to approach turbine technology development as well as processes such as performance validation and turbine operations. This interaction with the atmosphere occurs at several spatial and temporal scales from continental- scale to turbine-scale. Understanding atmospheric variability over continental-scales and across plants can facilitate reliance on wind energy as a baseload energy source on the electrical grid. On turbine scales, understanding the atmosphere’s contribution to the variability in power production can improve the accuracy of power production estimates as we continue to implement more wind energy onto the grid. Wind speed and directional variability within a plant will affect wind turbine wakes within the plants and among neighboring plants, and a deeper knowledge of these variations can help mitigate effects of wakes and possibly even allow the manipulation of these wakes for increased production. Herein, I present the extent of my PhD work, in which I studied outstanding questions at these scales at the intersections of wind energy and atmospheric science. iii My work consists of four distinct projects. At the coarsest scales, I analyze the separation between wind plant sites needed for statistical independence in order to reduce variability for grid-integration of wind. Site data from three datasets spanning continents, durations and time resolution include 45 years of hourly wind speed data from over 100 sites in Canada, 4 years of five-minute wind speed data from 14 sites in the US Pacific Northwest, and one year of five- minute wind power generation data from 29 wind farms in southeastern Australia. I find similarities between these datasets in which correlations that fall to zero with increasing station separation distance, and the higher the high-pass cut-off frequency, the smaller the station separation required to achieve de-correlation. Shifting to atmospheric interaction on turbine- scales, I use 2.5 months of upwind tower and turbine data to understand how power production varies with different atmospheric stability and turbulence regimes. At lower wind speeds, periods of unstable and more turbulent conditions produce more power than periods of stable and less turbulent conditions, while at wind speeds closer to rated wind speed, periods of unstable and more turbulent conditions produce less power than periods of stable and less turbulent conditions. Using these new, stability- and turbulence-specific power curves to calculate annual energy production (AEP) estimates results in smaller AEPs than if calculated using no stability and turbulence filters, which could have implications for manufacturers and operators. In my third project, I address the problem of expensive power production validation. Rather than erecting towers to provide upwind wind measurements, I explore the utility of using nacelle- mounted anemometers for power curve verification studies. I calculate empirical nacelle transfer functions (NTFs) with upwind tower and turbine measurements. The fifth-order and second- order NTFs show a linear relationship between upwind wind speed and nacelle wind speed at wind speeds less than about 9 m s–1, but this relationship becomes non-linear at wind speeds iv higher than about 9 m s–1. The use of NTFs results in AEPs within 1 % of an AEP using upwind wind speeds. Additionally, during periods of unstable conditions as well as during more turbulent conditions, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of stable conditions and less turbulence conditions at some wind speed bins below rated speed. Finally, in my fourth project, I consider spatial scales on the order of a wind plant. Using power production data from over 300 turbines from four neighboring wind farms in the western US along with simulations using the Weather Research and Forecasting model’s Wind Farm Parameterization (WRF-WFP), I investigate the advantage of using the WFP to simulate wakes. The case simulated in this study focuses on the nighttime stable hours during the early morning of 30 October 2011 into the daytime unstable hours of 30 October 2011. During this case, winds from the west and north-northwest range from about 5 to 11 m s–1. A down-ramp occurs in this case study, which WRF predicts too early. The early prediction of the down-ramp likely affects the error in WRF-predicted power, the results of which show exaggerated wake effects. Variable differences in hub-height winds, surface winds, and surface temperature throughout the innermost domain are revealed, and likely caused by some instability upwind of the farms in the simulations or due to horizontal resolution as suggested by Rai et al. (2017). While these projects span a range of spatio-temporal scales, a unifying theme is the important aspect of atmospheric variation on wind power production, wind power production estimates, and means for facilitating the integration of wind-generated electricity into power grids. Future work, such as universal NTFs for sites with similar characteristics, NTFs for waked turbines, or the deployment of lidars on turbine nacelles for operation purposes, should continue to study the mutually-important interconnections between these two fields. v ACKNOWLEDGEMENTS Overall, grad school was a great experience in which I learned a lot. I would first like to thank m y undergraduate advisors at Penn State, Ken Davis and Susan Stewart, who indulged my request to somehow merge my interests of meteorology and wind energy. I also want to thank my graduate advisor, Julie Lundquist, for providing the right amount of guidance while also allowing me to be independent. My first year of graduate school was a steep learning curve and Mike Rhodes, Matt Aitken and Brian Vanderwende gave me guidance on improving my coding and data analysis skills. I would like to thank my research committee and co-authors, for their input and direction, and for taking the time for the many emails and drafts of papers I sent them along the way. I also want to thank Laurie Conway, for all the amazing work she does for ATOC. I want to thank my family, for bearing with me as I moved halfway across the country for my career. My Aunt Fran provided me a home in Colorado and weekend get-aways when I needed them. I am glad I was fortunate enough to spend those last few years with you. Finally, I would like to thank my roommates, Chris Maloney, Torie Hartwick and Josh Pettit, for their friendship. I appreciate you letting me bounce ideas off you and for teaching me about high-altitude clouds, high-energy electrons, and dust storms on Mars. Movie nights and those late night talks (often sitting on the floor in the kitchen) got me through grad school. vi CONTENTS I. INTRODUCTION ....................................................................................................................... 1 “Clean” energy sources ............................................................................................................... 2 Current and projected energy market in the US .......................................................................... 3 Motivation ................................................................................................................................... 7 Background ................................................................................................................................. 8 Scope ......................................................................................................................................... 10 II. WIND VARIABILITY AND INTERCONNECTING PLANTS ............................................ 12 Abstract ..................................................................................................................................... 13 Introduction ..............................................................................................................................