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Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis examines using thermal energy storage as a demand side management tool for air-conditioning loads with the goal of increasing photovoltaic penetration. It uses Arizona State University (ASU) as a case study. The analysis is completed with a modeling approach using typical meteorological year (TMY) data, along with ASU’s historical load data. Sustainability, greenhouse gas emissions, carbon neutrality, and photovoltaic (PV) penetration are all considered along with potential economic impacts.
By extrapolating the air-conditioning load profile from the existing data sets, it can be ensured that cooling demands can be met at all times under the new management method. Using this cooling demand data, it is possible to determine how much energy is required to meet these needs. Then, modeling the PV arrays, the thermal energy storage (TES), and the chillers, the maximum PV penetration in the future state can be determined.
Using this approach, it has been determined that ASU can increase their solar PV resources by a factor of 3.460, which would amount to a PV penetration of approximately 48%.
By extrapolating the air-conditioning load profile from the existing data sets, it can be ensured that cooling demands can be met at all times under the new management method. Using this cooling demand data, it is possible to determine how much energy is required to meet these needs. Then, modeling the PV arrays, the thermal energy storage (TES), and the chillers, the maximum PV penetration in the future state can be determined.
Using this approach, it has been determined that ASU can increase their solar PV resources by a factor of 3.460, which would amount to a PV penetration of approximately 48%.
ContributorsRouthier, Alexander F (Author) / Honsberg, Christiana (Thesis advisor) / Fraser, Matthew (Committee member) / Bowden, Stuart (Committee member) / Arizona State University (Publisher)
Created2016