Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 82-94.doi: 10.6040/j.issn.1672-3961.0.2019.178

• Electrical Engineering • Previous Articles     Next Articles

Imprecise conditional probability prediction of wind power ramp events

Bo WANG1(),Buwei WANG1,Ming YANG2,*(),Yuanchun ZHAO3,Wenli ZHU2   

  1. 1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
    2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, Shandong, China
    3. tate Grid Jinan Power Company, Jinan 250012, Shandong, China
  • Received:2019-04-19 Online:2020-02-20 Published:2020-02-14
  • Contact: Ming YANG E-mail:wangbo@epri.sgcc.com.cn;myang@sdu.edu.cn
  • Supported by:
    国家电网公司科技项目(5215001600V4)

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Abstract:

Wind power ramp events (WPRE) could easily destroy the active power balance of the power system, as well as did harm to the frequency stability and power quality, which threatened the safe and stable operation of power grid. A novel imprecise conditional probability prediction approach was proposed based on the credal network (CN), which could provide the interval range of the occurrence probability of each WPRE state. The approach excavated the dependence relationships between WPRE and meteorological variables using the greedy search algorithm, and constructed a CN structure to express the relationships in an abstract way. The proposed approach extended the imprecise Dirichlet model (IDM) on hyperparameter settings to quantify the uncertain conditional dependences among the variables, thus to realize the parameter estimation of the CN. Based on the constructed CN model, a CN probability inference algorithm was employed to estimate the imprecise probability distribution of the multi-state WPRE. The case study with wind-farm operating measurements in Ningxia Province demonstrated that the proposed approach had excellent performance even under the prediction scenarios with insufficient samples.

Key words: imprecise probability, wind ramp, credal network, imprecise Dirichlet model

CLC Number: 

  • TM614

Fig.1

The flowchart of GS algorithm applied for CN structure learning"

Fig.2

The simple three-node network structure example"

Fig.3

Key steps of the WPRE imprecise probability distribution prediction"

Table 1

The division of the ramp states"

需满足的条件 爬坡状态
Pt_max-Pt_min>5.2 MW且t_max≥t_min 上爬坡
Pt_max-Pt_min>5.2 MW且t_max < t_min 下爬坡
|Pt_max-Pt_min|≤5.2 MW 不爬坡

Table 2

The division of the training set and validation set"

数据集名称 时间跨度 样本容量
训练集 2015年1月1日0:00—2015年12月31日23:45 34 516
验证集 2016年1月1日0:00—2017年12月31日23:45 66 626

Table 3

The division of the variable states in CN"

节点变量 风速最大波动量
V/(m·s-1)		 风速
S/(m·s-1)		 风向D/(°) 温度T/℃ 湿度h/% 爬坡事件H
(最大功率波动量)/MW
状态1 [-16, -1.77) [0, 3.09) [0, 144) [-22, 5.17) [9, 36.2) [-5.2, 5.2]
状态2 [-1.77, 1.48) [3.09, 5.41) [144, 252) [5.17, 16.77) [36.2, 59.93) (5.2, 36]
状态3 [1.48, 16.7) [5.41, 24) [252, 360) [16.77, 36.5) [59.93, 98.49) [-36, -5.2)

Fig.4

The optimal CN structure constructed by the GS algorithm"

Table 4

Prediction performance of the models using CWC index"

λ/% 模型 PICP/% PINAW CWC 宽度>10%的
区间占比/%		 宽度>20%的
区间占比/%		 宽度>30%的
区间占比/%		 宽度>50%的区间占比/%
90 CN 86.97 0.195 4 0.553 8 89.30 42.39 10.70 0.00
CLT 65.71 0.232 2 30.150 0 71.74 51.30 37.04 4.32
75 CN 73.39 0.144 4 0.343 8 73.66 17.70 3.29 0.00
CLT 57.06 0.162 4 6.029 1 62.83 38.68 18.79 0.00
60 CN 57.20 0.106 9 0.294 1 48.15 5.76 0.41 0.00
CLT 47.60 0.118 8 1.537 6 51.99 25.10 0.00 0.00

Fig.5

The imprecise probability distributions of WPRE predicted under 7 meteorological conditions"

Table 5

Prediction performance of the models using WCWS index"

wt1 类型 模型 PICP/% PINAW WCWS
0.3 冒险型 CN 67.08 0.127 0 0.112 3
CLT 56.38 0.157 9 0.058 6
0.5 中立型 CN 76.82 0.157 8 0.305 2
CLT 56.38 0.157 9 0.203 0
0.7 稳健型 CN 87.65 0.198 4 0.554 1
CLT 56.38 0.157 9 0.347 3

Table 6

The redefined states of variable D and T"

节点变量 风向D/(°) 温度T/℃
状态1 [0, 216) [-22, 16.77)
状态2 [216, 276) [16.77, 22.57)
状态3 [276, 360) [22.57, 36.5)

Fig.6

The CN structure constructed by the GS algorithm with the redefined variable states"

Table 7

The results predicted by CN model under the extreme meteorological conditions"

预测的非精确概率 真实概率
Pim(H1|C1)=[0.82, 0.97] P*(H1|C1)=1.00
Pim(H2|C1)=[0.01, 0.09] P*(H2|C1)=0.00
Pim(H3|C1)=[0.01, 0.10] P*(H3|C1)=0.00
Pim(H1|C2)=[0.18, 0.45] P*(H1|C2)=0.25
Pim(H2|C2)=[0.07, 0.27] P*(H2|C2)=0.25
Pim(H3|C2)=[0.38, 0.72] P*(H3|C2)=0.50
Pim(H1|C3)=[0.42, 0.72] P*(H1|C3)=0.67
Pim(H2|C3)=[0.23, 0.52] P*(H2|C3)=0.33
Pim(H3|C3)=[0.02, 0.11] P*(H3|C3)=0.00
Pim(H1|C4)=[0.41, 0.61] P*(H1|C4)=0.60
Pim(H2|C4)=[0.33, 0.53] P*(H2|C4)=0.40
Pim(H3|C4)=[0.02, 0.10] P*(H3|C4)=0.00

Fig.7

The imprecise probability distributions of WPRE predicted under extreme meteorological conditions"

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