Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (6): 86-92.doi: 10.6040/j.issn.1672-3961.0.2019.117

• Electrical Engineering • Previous Articles     Next Articles

Analysis of wind power convergence trend quantitation based on sub-scene reconstruction

Yuanxi YAO()   

  1. Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan 250013, Shandong, China
  • Received:2019-03-25 Online:2019-12-20 Published:2019-12-17

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Abstract:

The output power of wind power was fluctuating. Due to the output of unit stabilized each other, with the increase of wind power cluster size, the fluctuation of wind power gradually decreased and the wind power showed "convergence effect". Grasping the trend of the convergence effect had an important guiding significance for transmission capacity configuration of large-scale wind power network. In this paper, the scene of wind power output was defined, and the convergence characteristics of wind power in each scene were analyzed. By fitting the continuous output curve in each scene, a scene reconstruction method based on convergence characteristics analysis was proposed. The validity of the method was verified by the measured. The example showed that the sub-scene reconstruction method analyzed the trend of convergence for wind power more accurately. The number of scenarios affected the fitting accuracy of continuous output curve, comparing to the scene divided by 5, 10 and 20, scene reconstruction in 10 scenes was more accurate for the description of wind convergence trend.

Key words: sub-scene reconstruction, convergence characteristics, continuous output curve, fitting

CLC Number: 

  • TP391.4

Fig.1

Wind power output scene division flow chart"

Fig.2

The continuous output curve of wind power in different clusters"

Fig.3

Continuous output power curves of the scene 1"

Fig.4

Continuous output power curves of the scene 7"

Table 1

The number of non-zero scenes in different cluster size"

场景 单场 两场 三场 四场 五场 六场 七场 八场 九场 十场
1 29 204 39 793 43 444 43 852 44 288 45 481 46 264 46 645 48 738 47 850
2 20 267 21 302 22 140 23 172 22 776 22 701 23 151 22 932 23 981 24 657
3 12 674 12 741 12 607 13 361 13 537 13 796 13 960 14 424 14 188 14 413
4 8 446 8 256 8 555 8 543 8 643 8 739 8 675 8 865 8 043 8 138
5 5 978 5 803 5 457 5 175 5 202 5 010 4 644 4 575 4 019 4 352
6 3 636 3 331 3 216 2 731 2 769 2 606 2 392 2 265 2 028 1 986
7 2 102 1 939 1 757 1 671 1 492 1 425 1 228 1 138 550 707
8 1 320 1 325 1 214 721 641 478 266 219 19 85
9 988 536 234 107 97 51 0 0 0 0
10 43 0 0 0 0 0 0 0 0 0

Table 2

The fitting function of duration in each scene"

场景 拟合函数 决定系数
1 -7.245×1018x-5.635 0+47 230 0.902 9
2 9 870x0.127 8 0.830 6
3 -2.726×106x-1.154 0+15 200 0.928 5
4 -0.343x+8 737 0.140 5
5 8.719×105x-0.965 5+3 294 0.946 3
6 4.200×106x-1.267 0+1 479 0.989 4
7 5.312×105x-0.920 3 0.932 3
8 7.628×106x-1.392 0-374.40 0.969 4
9 0
10 0

Table 3

The maximum output of each scene under different cluster size"

场景 单场 两场 三场 四场 五场 六场 七场 八场 九场 十场
1 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
2 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
3 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30
4 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
5 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
6 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60
7 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70
8 0.80 0.80 0.80 0.80 0.80 0.80 0.78 0.78 0.72 0.75
9 0.90 0.89 0.86 0.86 0.85 0.83 0 0 0 0
10 0.93 0 0 0 0 0 0 0 0 0

Table 4

The fitting function of continuous output power curves duration in scene one"

场数 拟合函数 确定系数
1 2.433×10-11x2-4.203×10-6x+0.099 55 0.999 0
2 5.325×10-11x2-4.770×10-6x+0.103 00 0.997 2
3 4.521×10-11x2-4.343×10-6x+0.101 70 0.998 4
4 4.069×10-11x2-4.159×10-6x+0.102 20 0.998 9
5 3.821×10-11x2-4.203×10-6x+0.102 30 0.998 8
6 3.686×10-11x2-3.969×10-6x+0.102 10 0.998 9
7 3.157×10-11x2-3.695×10-6x+0.100 90 0.999 1
8 3.022×10-11x2-3.637×10-6x+0.101 30 0.999 0
9 2.531×10-11x2-3.360×10-6x+0.100 80 0.999 1
10 2.631×10-11x2-3.425×10-6x+0.100 90 0.999 0

Table 5

Duration of reconstruction and actual scene"

场景 重构(时间点数) 实际(时间点数)
1 47 230 47 380
2 27 027 28 153
3 14 869 16 634
4 7 828 7 906
5 3 337 3 121
6 1 672 1 300
7 376 170
8 0 0
9 0 0
10 0 0
总计 102 339 101 869

Fig.5

Continuous output power curve of reconstruction and actual"

Table 6

The fitting function of duration in each scene"

ρspearman ρpearson D var
1 0.998 6 0.006 1 8.186×10-5

Fig.6

Continuous output curve of reconstruction and actual for five scenes"

Table 7

The indicator calculation of fitting(five scenes)"

ρspearman ρpearson D var
0.999 9 0.997 3 0.009 3 1.903×10-4

Fig.7

Continuous output curve of reconstruction and actual for twenty scenes"

Table 8

The indicator calculation of fitting(twenty scenes)"

ρspearman ρpearson D var
0.999 5 0.999 5 0.010 1 1.464×10-4
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