| 基于CFD动力降尺度的复杂地形风电场风电功率短期预测方法研究 |
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| 引用本文： | 马文通,朱蓉,李泽椿,龚玺.基于CFD动力降尺度的复杂地形风电场风电功率短期预测方法研究[J].气象学报,2016,74(1):89-102. |
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| 作者姓名： | 马文通 朱蓉 李泽椿 龚玺 |
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| 作者单位： | 1.国家气象中心, 北京, 100081 |
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| 基金项目： | 公益性行业(气象)科研专项(GYHY201206026);国家科技支撑计划资助项目(2015BAA06B04)。 |
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| 摘 要： | 复杂地形导致近地层风场时空变化大,是影响风电场短期风电功率预测准确率的重要因素。为此,基于中尺度数值预报模式和微尺度计算流体力学模式,建立了风电场短期风电功率动力降尺度预测系统。该系统由中尺度数值预报模式、微尺度风场基础数据库、风电功率预测集成系统组成,能够预测复杂地形风电场中每台风电机组未来72 h逐15 min的发电量。提高了复杂地形风场发电功率预测准确率,同时还可以在上报电网的风电功率预测结果中考虑运行维护计划和限电等因素对实际并网功率的影响。2014年7月-2015年1月的业务预测试验表明,风电场短期风电功率动力降尺度预测系统的月预测相对误差均小于0.2,满足中国国家电网对风电功率预测误差和时效性的业务要求。动力降尺度技术不受具体项目地形复杂程度和历史观测数据样本量的限制,可以在新建风电场中推广应用,具备实际的可操作性。
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| 关 键 词： | 短期风电功率预测 中尺度数值预报模式 计算流体力学(CFD) 大气稳定度 |
| 收稿时间： | 5/8/2015 12:00:00 AM |
| 修稿时间： | 2015/10/23 0:00:00 |
Study of the short-term wind power forecasting method for complex terrain wind farm based on the CFD dynamical downscalling |
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| MA Wentong,ZHU Rong,LI Zechun and GONG Xi.Study of the short-term wind power forecasting method for complex terrain wind farm based on the CFD dynamical downscalling[J].Acta Meteorologica Sinica,2016,74(1):89-102. |
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| Authors: | MA Wentong ZHU Rong LI Zechun and GONG Xi |
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| Institution: | 1.National Meteorological Center, Beijing 100081, China2.National Climate Center, Beijing 100081, China3.Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | The principal factors for short-term wind farm power forecasting accuracy are terrain complexity which will cause large variability in the wind field in space and time. Based on the combination of mesoscale numerical weather prediction model, and microscale Computational Fluid Dynamics (CFD) model, a short-term wind power dynamical downscaling forecast system is therefore presented. The system is composed of three parts: mesoscale numerical weather prediction model, microscale wind farm characteristic database and wind power forecasting integrated system. This system is able to provide the power forecasting of every turbine in the wind farm in the next 72 hours with 15-minute time step. The influence of turbine maintenance schedule and grid limitation is considered in the prediction result. A test from July 2014 to January 2015 indicates the monthly wind power forecasting error is less than 20%, which conforms to the requirement of the standards. The dynamical downscaling forecast method is not constrained by the terrain complexity or quantity of wind farm histoical data, which means that the system is also applicable on newly-built wind farm. |
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| Keywords: | Short-term wind power forecasting Mesoscale numerical weather prediction model Computational Fluid Dynamics (CFD) Atmospheric stability |
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