Comparison of Short-term Forecast Method of Wind Power in Wind Farm

Xu Yang; Chen Zhenghong; Yang Hongqing; Wang Lin; Cheng Chi; Xu Peihua

Vol.24, NO.5, 2013

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Article Navigation > Journal of Applied Meteorological Science > 2013 > 24(5): 625-630

Xu Yang, Chen Zhenghong, Yang Hongqing, et al. Comparison of short-term forecast method of wind power in wind farm. J Appl Meteor Sci, 2013, 24(5): 625-630.

Citation:

Xu Yang, Chen Zhenghong, Yang Hongqing, et al. Comparison of short-term forecast method of wind power in wind farm. J Appl Meteor Sci, 2013, 24(5): 625-630.

Xu Yang, Chen Zhenghong, Yang Hongqing, et al. Comparison of short-term forecast method of wind power in wind farm. J Appl Meteor Sci, 2013, 24(5): 625-630.

Citation:

Xu Yang, Chen Zhenghong, Yang Hongqing, et al. Comparison of short-term forecast method of wind power in wind farm. J Appl Meteor Sci, 2013, 24(5): 625-630.

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Comparison of Short-term Forecast Method of Wind Power in Wind Farm

Hubei Provincial Meteorological Service Center, Wuhan 430074

Received Date: 2012-09-29
Rev Recd Date: 2013-07-02
Publish Date: 2013-10-31

Abstract

Abstract

To further improve the accuracy of wind energy forecast and provide more valuable service, several wind energy forecast methods are studied comparatively in Jiugongshan Wind Farm. Based on the wind speed simulation results by the aggregative model CALMET coupled with MM5 (the model resolution is 200 m), the principle method and dynamic-statistical method are used to discuss 24-h forecast effect, with the temporal resolution set to 15 minutes. There are three kinds of principle method to discuss the effect of simulated wind speed correcting and the wind energy forecast model based on observations. The dynamic-statistical method forecast by establishing a rolling model using the simulated data of last period every day.The fine-scale simulation can obviously forecast the variation trend of wind speed. The correcting of simulated wind speed can effectively reduce the wind speed error and improve the forecasting accuracy, but it is difficult to revise the changing trend of simulated wind speed.The dynamic-statistical method is much more suitable for the complex topography mountainous terrain, and the monthly relative mean square root error is 14%—26% from July to December in 2011, which might be the result of its spontaneous adaption for terrain conditions.The wind energy forecast model based on observations is better than those based on theoretical model and can effectively reduce the forecasting error, because the wind farm environment has unique effects on the output power of fan.Furthermore, it is discovered that the wind energy forecast in southern mountain area is much more difficult than in north area. The fine-scale simulation should be used to reduce the infection of terrain; the method of simulated wind speed correcting must consider the different situation of the wind farm; the extreme weather events must be considered, and effects of different weather especially meteorological disaster such as ice-coating and thunderstorm should be deeply studied. These results enhance the service effect at Jiugongshan Wind Farm in Hubei Province, and more research should be carried out to improve the forecast accuracy.
- wind power;
- numerical simulation;
- short-term forecast;
- correcting of simulated wind speed

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References(17)

Figures and Tables

Fig. 1 The flow chart of wind energy short-term forecast using principle method

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Fig. 2 The fitting curve of wind energy forecast model using observations

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Fig. 3 The relative mean square root error of wind energy forecast at Jiugongshan Wind Farm from July to December in 2011

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Table 1 The correcting model of simulated wind speed

订正条件	订正模型
υ_模拟 < 3 m·s^-1	y=x(不订正)
υ_模拟≥3 m·s^-1	y=-0.0015x⁴+0.0544x³-0.6438x²+3.5233x-2.201
注：x为模拟风速，单位：m·s^-1; y为订正风速，单位：m·s^-1。

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Table 2 The correcting effect test of simulated wind speed

月份	条件	相关系数	均方根误差/(m·s^-1)	平均绝对误差/(m·s^-1)
7	实测与模拟	0.584	3.22	2.5
7	实测与订正	0.583	2.90	2.3
8	实测与模拟	0.653	3.00	2.4
8	实测与订正	0.633	2.85	2.3
9	实测与模拟	0.365	3.77	3.0
9	实测与订正	0.381	3.12	2.5
10	实测与模拟	0.230	4.01	3.2
10	实测与订正	0.207	3.70	3.0
11	实测与模拟	0.496	3.24	2.9
11	实测与订正	0.471	2.95	2.6
12	实测与模拟	0.412	2.38	1.8
12	实测与订正	0.396	2.38	1.8

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Table 3 The wind energy forecast model

项目	风速分段/(m·s^-1)	风电功率预报方程
	x < 3	W=0
理论风电功率预报模型	3≤x < 12	W=-3.5293x³+90.842x²-626.38x+1385.9
	x≥12	W=850
	x < 3	W=0
实际风电功率预报模型	3≤x < 13	W=-0.0621x⁴+0.7055x³+11.477x²-80.801x+148.97
	x≥13	W=850
注：x为预报风速，单位：m·s^-1；W为风电功率，单位：kW。

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Table 4 The comparison of wind energy forecast by different methods

月份	方法	相关系数	相对均方根误差/%	平均绝对误差/kW	实测平均功率/kW	预报平均功率/kW
7	物理法1	0.593	30	2760.5	3579.4	4195.2
	物理法2	0.605	27	2385.7		3151.3
	物理法3	0.604	26	2344.2		2837.0
	动力统计法	0.644	24	2409.2		2984.1
8	物理法1	0.722	25	2427.4	4033.9	4045.1
	物理法2	0.706	25	2316.2		3100.8
	物理法3	0.699	25	2305.9		2859.9
	动力统计法	0.624	26	2703.2		3091.9
9	物理法1	0.436	33	2938.6	1368.7	3604.4
	物理法2	0.500	23	2028.7		2597.5
	物理法3	0.504	19	1783.4		2323.5
	动力统计法	0.414	18	1974.7		2731.1
10	物理法1	0.217	34	3007.6	2220.3	2501.6
	物理法2	0.228	30	2646.7		2091.3
	物理法3	0.227	28	2467.5		1890.8
	动力统计法	0.154	25	2492.6		2287.4
11	物理法1	0.480	37	3875.6	3781.2	648.4
	物理法2	0.473	36	3702.8		1030.0
	物理法3	0.558	33	3152.9		774.1
	动力统计法	0.683	24	2501.2		2205.9
12	物理法1	0.319	23	1817.3	1512.0	649.6
	物理法2	0.289	22	1796.5		1004.0
	物理法3	0.408	18	1291.9		573.6
	动力统计法	0.211	14	1417.3		1718.6

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