Employing environmental turbulence measurements for wind energy and snow transport models

Mohsen Vahidzadeh

doi:10.17077/etd.005659

This thesis introduces new approaches for employing environmental turbulence measurements into modeling. Specifically, field measurements of environmental variables at high resolutions are used along with the momentum equation to model different physical phenomena. The momentum equation is an extended version of Newton's second law, that can be used to describe characteristics of fluid flows around different structures such as propellers, pumps, wind turbines, snow fences, etc. In this thesis, we focus on the application of the momentum equation in modeling wind energy generation and snow transport.The first part of the thesis is focused on wind energy generation. As renewable energy becomes more prevalent, its integration into the power grid and the prediction of its energy contribution to the grid becomes more important. In the case of wind energy, prediction of power production is even more challenging due to its dependence on not only wind speed, but also meteorological conditions. Therefore, in this work we investigate the implementation of meteorological measurements in modeling wind power in order to improve the accuracy of power forecasting. A meteorological evaluation tower (MET) is instrumented to measure a number of different atmospheric variables and characterize the atmospheric boundary layer (ABL). The data from the tower are used to develop two modified power curves, a power surface, and an induction curve, all of which are models for wind power forecasting. The proposed models improve upon the accuracy of existing industry standard models, by implementing multiple atmospheric variables including wind shear, wind veer, turbulence intensity, air density, and thermal stability. All four models are developed with the goal of increasing accuracy of power prediction from wind turbines, while keeping them simple and easily applicable to industry needs such as wind resource assessment, wind farm operations, supply and demand balance, and energy market adjustment and pricing. The results from the proposed models suggest that they can improve the accuracy of wind power forecasting compared to current industry standard models, especially the induction curve model which can result in an improvement of 51\% compared to existing models. The second part of the thesis implements the same approach for modeling a different phenomenon, which is snow transport. Understanding snow accumulation on the surface and snow redistribution due to wind is crucial in cold and snowy regions such as the state of Iowa. Such areas experience large amounts of snow transport during winter seasons, resulting in major safety and convenience issues, including decreasing safety and efficiency of transportation on roadways, incurring maintenance costs, and blocking doors and exhaust vents in exposed structures. In order to mitigate these adverse effects of snowdrifts, we first need to understand how it works. Therefore, in this work a two-year long experimental campaign is carried out to understand the dynamics of snow transport in northern Iowa. Two snow fences are chosen for this study, a structural fence and a living fence. Each snow fence is monitored by several web-based cameras, to observe the occurrence of snow transport events and the evolution of snow deposit behind each fence. Furthermore, each site is equipped with weather stations to characterize the atmospheric conditions during snow transport events. The data from the weather stations in combination with the analysis of the momentum equation on the control volume containing each snow fence is used to develop a new physics-based model for snow transport. Unlike existing models which relate the amount of snow transport to wind speed only, the proposed model takes into account the effect of turbulence intensity and air density as well. The results from the proposed model are promising and suggest that the new model has the potential to improve our understanding of snow transport.

Employing environmental turbulence measurements for wind energy and snow transport models

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