Multiscale Modeling of Wind Turbine Wake Effects on Short-term Wind Power Forecasting and Wind Farm Layout Planning

由于对人类活动引起的温室气体(如二氧化碳、甲烷)浓度增加而引起的全球变暖日益关注，以及对能源能力的需求迅速增长，迫切需要开发新的资源。在这方面，环境问题有利于可再生和清洁能源，如风能(使用风力涡轮机提取)。由于风能是最发达和最具成本效益的可再生能源，预计它将在全世界的电力生产中发挥越来越大的作用。然而，改善目前风力发电场(风力涡轮机集群)的电力生产需要预测工具，但不幸的是，目前的科学状况不允许开发基于适当物理或理论的工具。这并不令人惊讶，因为风力涡轮机的空气动力学极其复杂，当风电场(或公园)中的多台风力涡轮机彼此靠近时，这种复杂性会进一步放大。 为了解决这一不足，本方案的目标是开发一个高分辨率的多尺度数值模拟框架，其中将包括风切变、大气湍流和层化以及复杂地形的影响，并且还将使用新的气动弹性驱动器线模式来考虑风力机尾迹的影响。建模系统的输出将用于风电场的短期(未来48~72小时)风电功率预测。此外，该软件系统还可以用于通过优化的风电场布局设计(包括噪声、阴影闪烁和湍流强度等各种因素对环境的影响)最大限度地提高发电量(或能量捕获)，并减少实际大气条件下风电场中风力涡轮机组件的疲劳负荷。 它将展示如何利用根据项目成果汇编的基础知识库来改进风力涡轮机和风力发电场的总体设计，我们预计这将对未来风能的开发和利用具有重要意义。此外，风能利用率的增加影响了其他发电资产(如核能、水电、天然气)的使用，要妥善管理发电资产组合，将需要更详细、准确和及时的风能信息和预测。

Owing to growing concerns over global warming caused by increasing concentrations of greenhouse gases (e.g., carbon dioxide, methane) resulting from human activity and to rapidly growing demand in energy capacity, there is an urgent need to exploit new resources. In this respect, environmental concerns favour renewable and clean energy sources such as wind energy (extracted using wind turbines). Wind energy is expected to play a significantly increasing role in the generation of electrical power worldwide owing to the fact that it is the most developed and cost effective of the renewable energy sources. However, improving the current power production from wind farms (clusters of wind turbines) requires predictive tools, but unfortunately the current state-of-the-science does not allow tools based on proper physics or theory to be developed. This should not be surprising because the aerodynamics of a wind turbine is extremely complicated, and this complication is further amplified when a number of wind turbines are located close to each other in a wind farm (or, park). To address this deficiency, the objective of this proposal is to develop a high-resolution multiscale numerical modeling framework, in which effects arising from wind shear, from atmospheric turbulence and stratification, and from complex terrain will be included, and effects from wind turbine wakes will also be accounted for by using a new aeroelastic actuator line model. The outputs of the modeling system will be used for short-term (the next 48~72 hours) wind power forecasting for wind farms. In addition, the software system can also be used to maximize power production (or, energy capture) through an optimal wind farm layout design, including environmental impacts from various factors such as noise, shadow flicker and turbulence intensity, and reduce fatigue loading of components of wind turbines in a wind farm under realistic atmospheric conditions. It will be demonstrated how the foundational knowledge base compiled from results produced in the project can be utilized to improve the overall design of wind turbines and wind farms, which we anticipate will be of importance for the future development and utilization of wind energy. In addition, the increase in wind energy utilization impacts the use of other power generation assets (e.g., nuclear, hydro, natural gas) and to properly manage the portfolio of electricity generation assets will require more detailed, accurate and timely information and prediction of wind energy.