Uncertainty quantification methods applied to wind energy systems

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Wind energy is one of the most promising renewable energy sources given order of magnitude energy requirements and resource availability. Wind resources are widely distributed globally, while weather forecasting with demand response and storage technologies are enabling grid integration at-scale. Wind energy generators can be mass produced and installed incrementally. Technology progression in conventional `Danish concept' 3-bladed turbine design has enabled 10+ MW machines to be commercially viable. GW-scale arrays of bottom-mounted offshore wind turbines are now industrial practice. A floating offshore wind turbine array has just been commercially deployed for the first time, opening up additional wind resources globally in deeper waters. Nascent airborne wind energy systems (AWES) that fly kites/gliders on tethers to capture wind energy are also being developed, to pursue a step reduction in material requirements for wind energy converters. Large-scale AWES, if successful, will logically be deployed offshore. At the heart of all these systems is the wind itself, inherently turbulent and imposing stochastic loading on the structures and causing variable power output. Offshore, wave forcing is an additional stochastic forcing. The ability to quickly and accurately analyze stochastic loading is key to exploring new design concepts and improving performance. The proposed research program will build on the PI's research group's large body of work on a range of stochastic methods applied to energy systems, including fusing together different simulators (aerodynamics, structures) with heterogeneous levels of fidelity to affect robust optimization of the system parameters and configurations. The stochastic approaches to be developed will be capable of analyzing wind energy systems and directly computing uncertain measures of their performance and life time, rather than relying on simplified approximations of steady state performance. The proposed research will focus specifically on developing and applying stochastic methods for uncertainty quantification of floating conventional and AWES, to then enable design and optimization studies of alternative concepts. The first application is to wind farm design, requiring fast modeling of fatigue loading for turbine arrays based on local unsteady flows. The second application is inside a single machine design optimization loop. Wind turbine operation is never steady, hence optimization of a steady model is not optimal or efficient. The ability to accurately, efficiently and directly compute the stochastic response to unsteady wind and wave inputs opens up the possibility of using more efficient optimization methods to exploring and optimize a wide range of wind generation system designs.