Uncertainty quantification methods applied to wind energy systems

考虑到能源需求和资源的可获得性，风能是最有前途的可再生能源之一。风能资源在全球范围内分布广泛，而具有需求响应和存储技术的天气预报正在实现大规模的电网整合。风能发电机可以批量生产并逐步安装。丹麦传统概念三叶式涡轮机设计的技术进步使10兆瓦的机组具有商业可行性。GW规模的底部安装的海上风力涡轮机阵列现在是工业实践。漂浮式海上风力涡轮机阵列刚刚首次商业化部署，在全球更深的水域开辟了更多的风能资源。新生的机载风能系统(AWE)也在开发中，这种系统将风筝/滑翔机系在系绳上以捕捉风能，以逐步减少对风能转换器的材料需求。如果成功，从逻辑上讲，大规模AWE将部署到海外。 所有这些系统的核心是风本身，风本身就是湍流的，它对结构施加随机载荷，并导致可变的功率输出。在近海，波浪强迫是一种额外的随机强迫。快速、准确地分析随机载荷的能力是探索新的设计概念和提高性能的关键。拟议的研究计划将建立在PI研究小组关于应用于能源系统的一系列随机方法的大量工作的基础上，包括将具有不同保真度的不同模拟器(空气动力学、结构)融合在一起，以影响系统参数和配置的稳健优化。将要开发的随机方法将能够分析风能系统，并直接计算其性能和寿命的不确定度量，而不是依赖于稳态性能的简化近似。 拟议的研究将特别侧重于开发和应用随机方法来量化浮动常规和AWE的不确定性，以便能够对替代概念进行设计和优化研究。第一个应用是风电场设计，需要基于局部非定常流动对涡轮阵列的疲劳载荷进行快速建模。第二个应用程序位于单个机器设计优化循环中。风力发电机组的运行从来不是稳定的，因此对稳定模型的优化并不是最优的或有效的。准确、高效和直接计算非定常风浪输入的随机响应的能力为使用更有效的优化方法探索和优化广泛的风力发电系统设计提供了可能性。

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.