Leveraging Newly-Developed Algorithms to Investigate the Impacts of Emerging Wind Farms on Boundary-Layer Features

Abstract

Wind farms are steadily increasing around the world, and they will continue to grow in size and number as global efforts shift toward a carbon-neutral future harnessing more renewable energy. Previous observational studies found that the wakes wind farms produce can influence physical and meteorological processes in the atmospheric boundary layer (ABL). However, even high-resolution ABL measurements have data gaps that make the lower atmosphere difficult to characterize, leaving an incomplete picture of how wind farms can modulate it. As wind farms expand, it is essential to determine their implications on an ABL represented to the best ability that current tools can offer, providing knowledge on wind farm modifications that can aid in planning field studies, numerical modeling, and weather forecasting. This dissertation analyzes the impacts of northern Oklahoma wind farms on ABL properties. These goals are accomplished using two newly developed algorithms geared toward improving ABL data analysis where limitations have traditionally existed: the first yields deeper ABL wind profiles using an optimal estimation approach, and the second estimates ABL depth throughout the full 24-hour cycle by compiling kinematic and thermodynamic profiles in a novel, synergistic framework. The wind retrieval algorithm increases coverage of ABL wind profiles, helping to resolve the full shape of northern Oklahoma’s frequently occurring nocturnal low-level jet (NLLJ) even when the range of the collected wind data is too shallow to do so. In turn, this information helps accurately classify NLLJs of varying characteristics to identify any turbulence they produce that may coincide with wind farm mixing. The fuzzy-logic ABL height algorithm is then applied to determine whether such low-level mixing influences ABL depth and, by extension, the ABL as a whole. Given the novel nature of these two algorithms, the first component of this dissertation examines their usability for wind farm research applications. This is done by testing their efficiency with past high-resolution field observations collected in northern and central Oklahoma prior to substantial wind farm development. The optimal estimation wind retrieval algorithm was found to adequately resolve ABL winds, including NLLJs that traditional retrieval methods cannot always completely resolve. The fuzzy-logic ABL height algorithm performed well provided recent precipitation had not depleted the aerosol content necessary for lidar observations. With both algorithms validated, the instruments used to measure wind farm effects were also evaluated and found to perform sufficiently in their kinematic and thermodynamic capabilities. The second section identifies low-level mixing effects from wind farms during warm and cold months while accounting for other potential mixing sources in the region, namely the NLLJ. To do this analysis, environments with and without nearby wind farms were compared using 1) long-term datasets from a climate research site that has been active before and after the construction of a nearby wind farm, and 2) observations from a recent field campaign where one instrument platform was placed close to wind farms and another still located in the campaign domain but far from the suspected influence of wakes. Rotor-level mixing from both sets of comparisons was evident near wind farms, though it was often overshadowed by mixing from strong NLLJs. To account for this, we employ a method geared at determining the threshold wind speed at which near-surface turbulence develops. Identifying this threshold revealed that wind farm signals are most distinguishable under NLLJs of moderate strengths before mixing induced from the jet obscures wind-farm-driven mixing, affirming the presence of low-level wakes and the optimal periods for identifying them under NLLJ conditions. Following the establishment of low-level mixing effects, it was necessary to determine if they extended throughout the rest of the ABL. The final component of this dissertation examines this by applying the fuzzy-logic ABL height algorithm across all observations of interest. Doing so expands upon recent literature that also measured ABL height but using methods from individual instruments instead of a unification of datasets, which are prone to limitations associated with those individual methods that this fuzzy-logic height method helps mitigate. Consistent with low-level findings, the fuzzy-logic algorithm showed environments near wind farms to exhibit increased depth of the ABL. These findings agree with previous methods examining wind-farm-ABL interactions, but this first implementation of the fuzzy-logic ABL height approach for wind farm studies demonstrated that the method produces the most realistic estimates with reduced sensitivity compared to previously tested methods. Such effects were reinforced when testing different flow directions and occasions when turbines were not expected to be operational. These findings demonstrate that wind farms impact not only the low-levels, but the entire ABL, and the fuzzy-logic algorithm can accurately characterize these occurrences.