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

风能是加拿大增长最快的可再生能源形式，2019年加拿大的年投资超过10亿美元。尽管如此，实现国家可再生能源目标将需要加拿大的风力发电能力在未来几十年显着增加。新项目需要准确预测计划风电场的能源生产潜力。这些预测是使用风力发电场设计工具进行的，例如OpenWind®，这是由UL LLC开发的行业领先的软件，用于拟议风能站点的布局规划和资源估计。为了产生准确的预测，风电场设计软件需要经过充分验证，但同时低成本的模型，捕捉风力发电场内的风和空气动力学过程。虽然这些模型已被用于数百个风力项目，但最近的研究表明，对风力发电场下风处低能量尾流的恢复和上风处的阻塞效应的预测很差。风电场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。阻塞效应，即迎面而来的风因风电场的存在而减慢，在历史上被认为是可以忽略不计的。然而，最近的研究表明，忽略阻塞会导致对风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，根据这些模拟，将开发风电场阻塞和远场尾流恢复的改进降阶模型，并根据真实的风电场的现场测量结果进行验证。该项目有望提高新风电项目电力预测的准确性，并促进对加拿大风能资源丰富的沿海和北方地区的投资，加速加拿大向清洁能源的过渡。

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.