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.

风能是加拿大增长最快的可再生能源形式，2019年加拿大的年度投资超过10亿美元。不过，要实现国家可再生能源目标，加拿大的风电装机容量需要在未来几十年内大幅增加，新项目需要准确预测。此类预测是使用 OpenWind® 等风电场设计工具进行的，OpenWind® 是由 UL LLC 开发的行业领先软件，用于拟议风能站点的布局规划和资源估算。 、风电场设计软件需要经过充分验证但成本低廉的模型来捕获风电场内的风和空气动力学过程，虽然此类模型已用于数百个风电项目，但最近的研究表明，顺风和低能量尾流的恢复。风电场逆风处的阻塞效应预测不佳 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，因为风电场迎面而来的风会减慢。对存在的响应然而，最近的研究表明，忽略堵塞产量会过度预测风电场的发电潜力，将与 UL 合作进行一系列高保真计算流体动力学模拟。该项目有望提高新风电场功率预测的准确性，并根据实际风电场的现场测量来开发和验证风电场阻塞和远场尾流恢复的改进降阶模型。促进投资加拿大沿海和北部地区风力资源丰富，加速了加拿大向清洁能源的转型。