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, Joshua
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721516
• 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增长最快的形式，在加拿大的年度投资在2019年超过10亿美元。尽管如此，实现国家可再生能源目标仍将要求加拿大的风能在未来几十年中显着提高。新项目需要准确预测计划中的风电场的能源生产潜力。这样的预测是使用风场设计工具（例如OpenWind®）做出的，这是由UL LLC开发的行业领先软件，用于对拟议的风能站点的布局计划和资源估算。为了产生准确的预测，风电场设计软件需要经过良好的验证，但仅仅是低成本模型，这些模型捕获了风电场内的风力和空气动力学过程。尽管此类模型已用于数百个风项目，但最近的研究表明，低能唤醒顺风的恢复和风电场的封锁效果的预测很差。风电场后面尾流的远场恢复对于评估邻近风电场对整体发电潜力的影响非常重要。在历史上认为，迎面而来的风响应于风电场的存在而减慢的阻塞效应被认为可以忽略不计。然而，最近的研究表明，忽略块对风电场的发电潜力的预测过高。与UL合作，将对虚拟风电场进行一系列高保真计算流体动力学模拟，从中，将开发并针对真实风电场的现场测量来开发并验证远场唤醒恢复的减少阶层。该项目有望提高对新风项目的电力预测准确性，并促进加拿大风能丰富的沿海和北部地区的投资，从而加速加拿大向清洁能源的过渡。