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

由于对温室气体浓度增加引起的全球变暖的日益关注（例如，由于人类活动产生的二氧化碳、甲烷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，对环境的关注有利于可再生能源和清洁能源，如风能（利用风力涡轮机提取）。由于风能是最发达和最具成本效益的可再生能源，预计风能在全世界的发电中将发挥越来越大的作用。然而，提高风力发电场（风力涡轮机集群）的当前发电量需要预测工具，但不幸的是，目前的科学水平不允许开发基于适当物理或理论的工具。这不应该令人惊讶，因为风力涡轮机的空气动力学极其复杂，并且当多个风力涡轮机在风电场（或风电场）中彼此靠近定位时，这种复杂性进一步放大。 为了解决这一不足，本建议的目标是开发一个高分辨率的多尺度数值模拟框架，其中所产生的风切变的影响，从大气湍流和分层，并从复杂的地形将被包括在内，风涡轮机尾流的影响也将占通过使用一个新的气动弹性致动器线模型。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，该软件系统还可用于通过最佳风力发电场布局设计（包括来自各种因素（诸如噪声、阴影闪烁和湍流强度）的环境影响）来最大化电力生产（或能量捕获），并减少风力发电场中的风力涡轮机的部件在实际大气条件下的疲劳载荷。 它将展示如何从项目中产生的结果编译的基础知识库可以用来改善风力涡轮机和风力发电场的整体设计，我们预计这将是重要的未来开发和利用风能。此外，风能利用的增加影响了其他发电资产的使用（例如，核能、水电、天然气）和适当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.