Reduced-Order Models of Wind Farm Blockage and Far-Field Wake Recovery

风电场阻塞和远场尾流恢复的降阶模型

• 批准号: 556326-2020
• 负责人: Brinkerhoff, JoshuaJR
• 金额: $ 2.23万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761717
• 关键词: Reduced; Order; Models; Wind; Farm

Wind energy is Canada's fastest-growing form of renewable energy, with annual investment in Canada exceeding one billion dollars in 2019. Still, achieving national renewable energy targets will require Canada's wind capacity to be significantly increased in the coming decades. New projects require accurate prediction of the energy production potential of a planned wind farm. Such predictions are made using wind farm design tools such as OpenWind®, an industry-leading software developed by UL LLC for layout planning and resource estimation of proposed wind energy sites. To yield accurate predictions, wind farm design software require well-validated but simultaneously low-cost models that capture the aeolian and aerodynamic processes within the wind farm. While such models have been used for hundreds of wind projects, recent studies have shown that the recovery of the low-energy wake downwind and the blockage effect upwind of the wind farm are poorly predicted. Far-field recovery of the wake behind a wind farm is very important for assessing the impact of neighboring wind farms on the overall generation potential. Blockage effects, where the oncoming wind slows down in response to the presence of the wind farm, were historically assumed to be negligible. Recent studies, however, have shown that ignoring blockage yields over-prediction of the wind farm's generation potential. In collaboration with UL, a large series of high-fidelity computational fluid dynamic simulations will be conducted of virtual wind farms, from which improved reduced-order models for wind farm blockage and far-field wake recovery will be developed and validated against field measurements of real wind farms. The project promises to increase the accuracy of power forecasting for new wind projects and promote investment in Canada's wind-rich coastal and northern regions, accelerating Canada's transition to clean energy sources.

风能是加拿大可再生能源的形式，在加拿大的年度投资在2019年占据了一项比尔由LLC进行布局计划和提议的风能的资源估算，以产生准确的预测，风场设计软件需要验证良好的，但同时使用的低成本模型Theolian和Aernodanic Process与印度人使用。最新的研究恢复了低能唤醒，风电场的阻塞效应很差。为了对风电场的回应，历史悠久的障碍物可以忽略不计。从中，将开发出经过验证的真实风电场的验证验证的现场测量值。