A multiscale modeling framework for short-term wind power forecasting

短期风电预测的多尺度建模框架

• 批准号: 462600-2014
• 负责人: Chen, Yongsheng
• 金额: $ 1.82万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=557898
• 关键词: multiscale; modeling; framework; short; term

Wind energy industry greatly relies on accurate wind forecasts. Since high-resolution wind forecasts are generally not available for most locations or, even if available, the required accuracy at wind turbine hub heights is not always achievable due to the stochastic and uncontrollable nature of the wind. A need for seeking accurate wind forecast with high spatial resolution to support the rapid growth of wind power becomes even more pressing. Many approaches and methods have been developed to overcome the unavailability and inaccuracy of wind forecast. Physically-based numerical weather prediction (NWP) models are more promising to either predict wind power directly or to provide necessary meteorological information for other special methods such as artificial neural networks (ANNs). However, NWP models are in most cases not accurate in local wind prediction over terrains with complex topography due to its inability to capture micro-scale phenomena. Waterloo CFD Engineering Consulting Inc. (WATCFD) - the supporting company of this Engage proposal - has developed very advanced computational fluid dynamics (CFD) methods that resolve complex topography and air/surface interactions at very high-spatial resolution (applicable to urban scales). WATCFD is keen to deliver the most accurate and reliable wind power forecasting tool by coupling its microscale CFD code urbanSTREAM with the mesoscale Weather Research and Forecasting (WRF) model, and by using the outputs of the multiscale model to train ANN to forecast wind power for the next 0~72 hours.

风能产业在很大程度上依赖于准确的风力预报。由于大多数地区通常无法获得高分辨率的风力预报，或者即使可以获得，由于风的随机性和不可控性，风力涡轮机轮毂高度所需的精度并不总是可以实现。为支持风电的快速发展，寻求高空间分辨率的准确风力预报的需求变得更加迫切。为了克服风力预报的不可获得性和不准确性，已经发展了许多方法和方法。基于物理的数值天气预报（NWP）模式更有希望直接预测风力或为其他特殊方法（如人工神经网络（ann））提供必要的气象信息。然而，由于NWP模式无法捕捉微尺度现象，在复杂地形的局部风预报中，多数情况下并不准确。滑铁卢CFD工程咨询公司（WATCFD）是该Engage提案的支持公司，该公司开发了非常先进的计算流体动力学（CFD）方法，以非常高的空间分辨率（适用于城市尺度）解决复杂的地形和空气/地面相互作用。通过将其微尺度CFD代码urbanSTREAM与中尺度天气研究与预报（WRF）模型相结合，并使用多尺度模型的输出来训练人工神经网络来预测未来0~72小时的风力，WATCFD热衷于提供最准确、最可靠的风力预报工具。