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.

风能是加拿大增长最快的可再生能源，2019年加拿大的年投资额超过10亿美元。然而，要实现国家可再生能源目标，加拿大的风力发电能力需要在未来几十年大幅提高。新项目需要对计划中的风电场的能源生产潜力进行准确的预测。这样的预测是使用风电场设计工具，如OpenWind®，UL LLC开发的行业领先的软件，用于布局规划和拟议的风能站点的资源估计。为了做出准确的预测，风电场设计软件需要经过充分验证但同时成本较低的模型来捕捉风电场内的风成和空气动力学过程。虽然这样的模型已经用于数百个风力发电项目，但最近的研究表明，低能量尾流的恢复以及风电场逆风的阻塞效应预测得很差。风电场后尾迹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。阻塞效应，即迎面而来的风由于风力发电场的存在而减速，在历史上被认为是可以忽略不计的。然而，最近的研究表明，忽视堵塞会导致对风力发电场发电潜力的过度预测。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，从中开发改进的风电场阻塞和远场尾流恢复的降阶模型，并根据实际风电场的现场测量进行验证。该项目有望提高新风项目电力预测的准确性，促进对加拿大风能丰富的沿海和北部地区的投资，加速加拿大向清洁能源的过渡。