中尺度WRF模式在风电功率预测中的应用研究
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摘要
低层风场具有随机变化的特征,导致风电场输出功率具有波动性、间歇性、随机性的特点,故对风电场输出功率进行预测是增加电网调峰容量、提高电网接纳风电能力、改善电力系统运行安全性与经济性最有效、最经济的手段之一。风电功率预测以数值天气预报(Numerical Weather Prediction, NWP)的风速、风向等要素为输入数据,通过预测算法将NWP的气象要素转换为风电场的输出功率。由于数值天气预报存在着诸多不确定性,因此NWP数据的准确性显著影响并制约着风电功率预测的精度。
本文以提高NWP数据的准确性和可靠性为总体研究目标,基于WRF(Weather Research and Forecasting model)模式,选择北方干旱区4个地点的风电场,开展了一系列模拟分析研究：分析得到模式的最佳参数配置；分析了模式模拟误差在季、月、日、时尺度上以及不同天气背景下的分布特征,探讨了造成模拟误差的可能原因;尝试应用四维数据同化、快速更新循环以及集合概率预报等技术来提高NWP数据的精度,采用类比卡曼滤波误差订正技术对预报序列进行误差订正,均取得了较好的效果,并以研究结果建设了实时运行的业务平台。
通过对WRF模式不同边界层方案在我国西北地区复杂地形条件下四季低层风场的模拟性能的分析研究,得到以下结论：(1)不同季节模拟效果最优的边界层方案有所不同。就新疆地区而言,春季YSU方案最优：夏季MYNN2.5方案最优；秋冬两季QNSE方案最优。(2)风场的模拟性能具有季节性特点。总体来说,在所有季节,基本都可以成功地再现风向：春夏两季模拟的风速平均值及标准差都偏小,秋季均显著高于观测值,冬季平均风速偏高而标准差偏低。(3)四个季节都存在观测风速及模拟风速的“赶超”现象——风速大时低估风速小时高估。春、秋、冬观测值分别为<4m/s、<6m/s、<8m/s时模拟高估,春、夏、秋、冬观测值分别为>4m/s、>4m/s、6-10m/s、8-12m/s时模拟低估。
利用实测风场数据(新疆和内蒙)检验了西北地区复杂地形条件下WRF模式不同空间分辨率对风速的模拟能力,并分析得到不同等级风速下的模拟误差特征。得到以下结论：(1)不同空间分辨率下,风速和风向的验证结果一致表明,模式分辨率为3km×3km时模拟效果优于分辨率更高的1km×lkm,表明我们对较小尺度上大气物理过程尚不清楚,另外地形数据分辨率不足也影响着低层风场模拟的精度。(2)不同等级风速检验结果：低风速区(0-3m/s)的模拟效果最差,次之为高风速区(≥25m/s)。对有效风速区(3-12m/s)及满发风速区(12-25m/s)模拟效果较为理想,不同地区略有差异。在新疆地区,对满发风速区的模拟效果最好,而内蒙地区模拟效果最好的为有效风速区。这是由新疆和内蒙在地形上的差异造成的,新疆风电场处于两山之间的山谷地带,“狭管”效应显著；而内蒙风电场地形恰好与新疆相反,地处山脉海拔最高,南北为平原海拔较低。
基于最佳的模式参数配置,利用WRF模式分析了中国西北典型干旱地区某风电场低层风场的模拟误差及其发生的条件和可能原因。得到以下结论：(1)观测风速和模拟误差之间存在“赶超”现象——当风速较小时模拟误差大,当风速较大时模拟误差较小。(2)日、时尺度以及不同天气背景下的误差特征。在日尺度上,当平均风速小于2.5m/s或者风速的变化幅度超过15m/s时,模式模拟效果较差；每日在12-17h模拟效果最好,在7-9h模拟效果最差,原因为在12-17h大气多处于不稳定层结,在7-9h多为中性层结,而模式对大气不稳定层结的模拟较中性层结准确；对阴天的模拟效果好于晴天的。(3)风速模拟误差与稳定度参数模拟不准确密切相关。WRF模式的QNSE方案对大气稳定度参数的模拟偏低,模式对稳定度参数模拟偏小的主要原因为模式中的M-O (Monin-Obukhov)长度大于真实值。
利用四维数据同化、快速更新循环技术和类比卡曼滤波误差订正技术改进了风场的预报误差,在此基础上利用集合概率预报方法建成了实时业务运行平台。得到以下结论：(1)基于WRF模式,将实时四维数据同化、快速更新循环模拟技术应用于风电功率预测的数值天气预报业务平台,采用类比卡曼滤波误差订正技术对预报序列进行误差订正。对2012年甘肃某风电场低层风场进行回算,四维数据同化技术的应用使得均方根误差(RMSE)降低了17%,平均绝对误差(MAE)降低了20%,误差也降低了11%；模式热启动后使得RMSE由3.63减小为3.03,MAE由2.86减小为2.23,相关系数从0.65增至0.78；误差订正后预报序列RMSE由2.93减小为2.42,MAE由2.28减至1.82,相关系由0.80增大为0.84.表明四维数据同化、快速更新循环和类比卡曼滤波误差订正技术可有效提高风电场的预报精度。(2)在模式预报性能检验的基础上,采用多种背景场数据和多种物理过程参数化方案组合的方法,建立了含有26个预报成员的集合概率预报系统,将其成功应用于风电场预报中,并得到初步的概率预报产品,为电力调度和决策提供更丰富的气象信息。
Due to random of wind speed and direction, the wind farm output power has the characteristics of volatility, intermittent and randomness. And forecast of wind power output is considered to be one of the most effective and economic ways to increase in power grid peak shaving capacity, improve the ability of acceptance of wind power, improve power system security and economy. Wind power prediction uses numerical weather prediction (NWP) and input variables of wind speed, wind direction. Then with prediction algorithm NWP variables are transformed into wind farm output power. Since there are many uncertainty in numerical weather perdiction, the accuracy of the NWP data significantly influences and restricts the wind power prediction accuracy.
In this paper, we focus on to improve the accuracy and reliability of the NWP data, based on the WRF model, selected four areas in northern arid zone, a series of research is carried out:the optimum parameter configuration of the WRF model is got; based on this, got the distribution characteristics of model prediction error on the seasonal, monthly, daily and hourly time scales as well as under different weather background, the possible causes is also investigated; Finally, we try to apply the four dimension data assimilation, rapid update cycle and ensemble probability forecast technology to improve the accuracy of NWP data, and use the analog-based Kalman filter bias correction to correct the prediction error. We achieved good results in these works, and these techniques become the foundation of a real-time running business platform.
Firstly, by using different boundary layer scheme of WRF model, we predicted the low level wind field in the four seasons over northwest China. The results show that:(1) the optimal solution of boundary layer in different seasons is different. Over Xinjiang, YSU scheme is the best in spring, MYNN2.5scheme is the best in summer; QNSE scheme is the best in fall and winter.(2) The performances of prediction vary in different seasons. Overall, the forecast of wind direction is very good; while for wind speed forecasting, it is better in spring and summer, the values is too large in fall and too small in winter.(3) For the four seasons the predicted wind speed is always smaller than the observed wind speed when it is large while larger when it is small. When the predicted values are too large in spring, fall and winter, the range of the observed values are<4m/s、<6m/s、<8m/s respectively; when the predicted values are too small in spring, summer, fall and winter the range of the observed values are>4m/s、>4m/s、6-10m/s、8-12m/s respectively.
Secondly, by using the observed wind data over Xinjiang and Inner Mongolia, the performance of the WRF model in predicting the wind speed was tested in complex terrains. Results show that:(1) the prediction is better under the resolution of3km×3km than1km×1km.(2) slow wind region (0-3m/s), the prediction is the worst and it is just a little better in rapid wind region (≥25m/s). The prediction is good over the region where the wind speed is between3m/s and25m/s. Over Xinjiang the prediction is the best when wind speed is between12m/s and25m/s, while for Inner Mongolia it is best between3m/s and12m/s.
Thirdly, by using WRF model we studied wind power forecast error and the condition of low level wind field and the possible causes over the arid region of northwest China. The results show that (1) the forecast error is always large when the observed wind speed is small while small when it is large.(2) On the daily scale, when the mean wind speed is less than2.5m/s or the anomaly is higher than15m/s, the model performance is worst; the prediction is the best between12:00-17:00and the worst between7:00-9:00; the prediction is better in cloudy day than in clear day.(3) The prediction error is closely related to the stability parameters. It is found that the simulation of wind shear is too small, which may be due to the simulation error in the high level wind speed. That the M-O length in the model is larger than the real one may be the source of the errors.
In the end, by using the four dimension data assimilation, rapid update cycle and analog-based Kalman filter bias correction technology we decreased the prediction error of wind field. Based on this, the ensemble probability forecast is adopted and built a real-time business platform. Results as follows:(1) based on WRF model, the four dimension data assimilation, rapid update cycle and analog-based Kalman filter bias correction technology are applied to the numerical weather forecast platform and built the numerical weather forecast platform. The case of2012in Gansu Province shows that the application of the four dimension data assimilation can decrease the RMSE by17%and MAE by20%; rapid update cycle technology can decrease the MAE from3.63to3.03and increase the correlation coefficients from0.65to0.78; analog-based Kalman filter bias correction can decrease the RMSE from2.93to2.42and the MAE from2.28to1.82and increase the correlation coefficients from0.80to0.84.(2) Based on the improvements of the prediction error, considering the difference of the surface condition, an ensemble probability forecast system is established which contains26prediction member, by using the multi-boundary condition and multi-physical parameterization schemes. The preliminary probability forecast products is generated, which provide more accurate meteorological information for the electric power dispatching and decision-making.
本文以提高NWP数据的准确性和可靠性为总体研究目标,基于WRF(Weather Research and Forecasting model)模式,选择北方干旱区4个地点的风电场,开展了一系列模拟分析研究：分析得到模式的最佳参数配置；分析了模式模拟误差在季、月、日、时尺度上以及不同天气背景下的分布特征,探讨了造成模拟误差的可能原因;尝试应用四维数据同化、快速更新循环以及集合概率预报等技术来提高NWP数据的精度,采用类比卡曼滤波误差订正技术对预报序列进行误差订正,均取得了较好的效果,并以研究结果建设了实时运行的业务平台。
通过对WRF模式不同边界层方案在我国西北地区复杂地形条件下四季低层风场的模拟性能的分析研究,得到以下结论：(1)不同季节模拟效果最优的边界层方案有所不同。就新疆地区而言,春季YSU方案最优：夏季MYNN2.5方案最优；秋冬两季QNSE方案最优。(2)风场的模拟性能具有季节性特点。总体来说,在所有季节,基本都可以成功地再现风向：春夏两季模拟的风速平均值及标准差都偏小,秋季均显著高于观测值,冬季平均风速偏高而标准差偏低。(3)四个季节都存在观测风速及模拟风速的“赶超”现象——风速大时低估风速小时高估。春、秋、冬观测值分别为<4m/s、<6m/s、<8m/s时模拟高估,春、夏、秋、冬观测值分别为>4m/s、>4m/s、6-10m/s、8-12m/s时模拟低估。
利用实测风场数据(新疆和内蒙)检验了西北地区复杂地形条件下WRF模式不同空间分辨率对风速的模拟能力,并分析得到不同等级风速下的模拟误差特征。得到以下结论：(1)不同空间分辨率下,风速和风向的验证结果一致表明,模式分辨率为3km×3km时模拟效果优于分辨率更高的1km×lkm,表明我们对较小尺度上大气物理过程尚不清楚,另外地形数据分辨率不足也影响着低层风场模拟的精度。(2)不同等级风速检验结果：低风速区(0-3m/s)的模拟效果最差,次之为高风速区(≥25m/s)。对有效风速区(3-12m/s)及满发风速区(12-25m/s)模拟效果较为理想,不同地区略有差异。在新疆地区,对满发风速区的模拟效果最好,而内蒙地区模拟效果最好的为有效风速区。这是由新疆和内蒙在地形上的差异造成的,新疆风电场处于两山之间的山谷地带,“狭管”效应显著；而内蒙风电场地形恰好与新疆相反,地处山脉海拔最高,南北为平原海拔较低。
基于最佳的模式参数配置,利用WRF模式分析了中国西北典型干旱地区某风电场低层风场的模拟误差及其发生的条件和可能原因。得到以下结论：(1)观测风速和模拟误差之间存在“赶超”现象——当风速较小时模拟误差大,当风速较大时模拟误差较小。(2)日、时尺度以及不同天气背景下的误差特征。在日尺度上,当平均风速小于2.5m/s或者风速的变化幅度超过15m/s时,模式模拟效果较差；每日在12-17h模拟效果最好,在7-9h模拟效果最差,原因为在12-17h大气多处于不稳定层结,在7-9h多为中性层结,而模式对大气不稳定层结的模拟较中性层结准确；对阴天的模拟效果好于晴天的。(3)风速模拟误差与稳定度参数模拟不准确密切相关。WRF模式的QNSE方案对大气稳定度参数的模拟偏低,模式对稳定度参数模拟偏小的主要原因为模式中的M-O (Monin-Obukhov)长度大于真实值。
利用四维数据同化、快速更新循环技术和类比卡曼滤波误差订正技术改进了风场的预报误差,在此基础上利用集合概率预报方法建成了实时业务运行平台。得到以下结论：(1)基于WRF模式,将实时四维数据同化、快速更新循环模拟技术应用于风电功率预测的数值天气预报业务平台,采用类比卡曼滤波误差订正技术对预报序列进行误差订正。对2012年甘肃某风电场低层风场进行回算,四维数据同化技术的应用使得均方根误差(RMSE)降低了17%,平均绝对误差(MAE)降低了20%,误差也降低了11%；模式热启动后使得RMSE由3.63减小为3.03,MAE由2.86减小为2.23,相关系数从0.65增至0.78；误差订正后预报序列RMSE由2.93减小为2.42,MAE由2.28减至1.82,相关系由0.80增大为0.84.表明四维数据同化、快速更新循环和类比卡曼滤波误差订正技术可有效提高风电场的预报精度。(2)在模式预报性能检验的基础上,采用多种背景场数据和多种物理过程参数化方案组合的方法,建立了含有26个预报成员的集合概率预报系统,将其成功应用于风电场预报中,并得到初步的概率预报产品,为电力调度和决策提供更丰富的气象信息。
Due to random of wind speed and direction, the wind farm output power has the characteristics of volatility, intermittent and randomness. And forecast of wind power output is considered to be one of the most effective and economic ways to increase in power grid peak shaving capacity, improve the ability of acceptance of wind power, improve power system security and economy. Wind power prediction uses numerical weather prediction (NWP) and input variables of wind speed, wind direction. Then with prediction algorithm NWP variables are transformed into wind farm output power. Since there are many uncertainty in numerical weather perdiction, the accuracy of the NWP data significantly influences and restricts the wind power prediction accuracy.
In this paper, we focus on to improve the accuracy and reliability of the NWP data, based on the WRF model, selected four areas in northern arid zone, a series of research is carried out:the optimum parameter configuration of the WRF model is got; based on this, got the distribution characteristics of model prediction error on the seasonal, monthly, daily and hourly time scales as well as under different weather background, the possible causes is also investigated; Finally, we try to apply the four dimension data assimilation, rapid update cycle and ensemble probability forecast technology to improve the accuracy of NWP data, and use the analog-based Kalman filter bias correction to correct the prediction error. We achieved good results in these works, and these techniques become the foundation of a real-time running business platform.
Firstly, by using different boundary layer scheme of WRF model, we predicted the low level wind field in the four seasons over northwest China. The results show that:(1) the optimal solution of boundary layer in different seasons is different. Over Xinjiang, YSU scheme is the best in spring, MYNN2.5scheme is the best in summer; QNSE scheme is the best in fall and winter.(2) The performances of prediction vary in different seasons. Overall, the forecast of wind direction is very good; while for wind speed forecasting, it is better in spring and summer, the values is too large in fall and too small in winter.(3) For the four seasons the predicted wind speed is always smaller than the observed wind speed when it is large while larger when it is small. When the predicted values are too large in spring, fall and winter, the range of the observed values are<4m/s、<6m/s、<8m/s respectively; when the predicted values are too small in spring, summer, fall and winter the range of the observed values are>4m/s、>4m/s、6-10m/s、8-12m/s respectively.
Secondly, by using the observed wind data over Xinjiang and Inner Mongolia, the performance of the WRF model in predicting the wind speed was tested in complex terrains. Results show that:(1) the prediction is better under the resolution of3km×3km than1km×1km.(2) slow wind region (0-3m/s), the prediction is the worst and it is just a little better in rapid wind region (≥25m/s). The prediction is good over the region where the wind speed is between3m/s and25m/s. Over Xinjiang the prediction is the best when wind speed is between12m/s and25m/s, while for Inner Mongolia it is best between3m/s and12m/s.
Thirdly, by using WRF model we studied wind power forecast error and the condition of low level wind field and the possible causes over the arid region of northwest China. The results show that (1) the forecast error is always large when the observed wind speed is small while small when it is large.(2) On the daily scale, when the mean wind speed is less than2.5m/s or the anomaly is higher than15m/s, the model performance is worst; the prediction is the best between12:00-17:00and the worst between7:00-9:00; the prediction is better in cloudy day than in clear day.(3) The prediction error is closely related to the stability parameters. It is found that the simulation of wind shear is too small, which may be due to the simulation error in the high level wind speed. That the M-O length in the model is larger than the real one may be the source of the errors.
In the end, by using the four dimension data assimilation, rapid update cycle and analog-based Kalman filter bias correction technology we decreased the prediction error of wind field. Based on this, the ensemble probability forecast is adopted and built a real-time business platform. Results as follows:(1) based on WRF model, the four dimension data assimilation, rapid update cycle and analog-based Kalman filter bias correction technology are applied to the numerical weather forecast platform and built the numerical weather forecast platform. The case of2012in Gansu Province shows that the application of the four dimension data assimilation can decrease the RMSE by17%and MAE by20%; rapid update cycle technology can decrease the MAE from3.63to3.03and increase the correlation coefficients from0.65to0.78; analog-based Kalman filter bias correction can decrease the RMSE from2.93to2.42and the MAE from2.28to1.82and increase the correlation coefficients from0.80to0.84.(2) Based on the improvements of the prediction error, considering the difference of the surface condition, an ensemble probability forecast system is established which contains26prediction member, by using the multi-boundary condition and multi-physical parameterization schemes. The preliminary probability forecast products is generated, which provide more accurate meteorological information for the electric power dispatching and decision-making.
引文
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