PROBABILISTIC MODELING OF SHORT-TERM WIND POWER PREDICTION ERRORS AND OUTPUT FLUCTUATIONS

被引：0

作者：

Ma W. ^{[1
,2
]}

Xie L. ^{[1
]}

Ma L. ^{[1
]}

Ye J. ^{[1
]}

Bian Y. ^{[1
]}

Yang Y. ^{[1
,2
]}

机构：

[1] Engineering Research Center for Renewable Energy Power Generation and Grid Technology of Ministry of Education, Xinjiang University, Urumqi

[2] State Key Laboratory of Power System and Generation Equipment, Tsinghua University, Beijing

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 11期

关键词：

cloud model; mixed Gaussian distribution; probability density distribution; reverse cloud model; wind power;

D O I：

10.19912/j.0254-0096.tynxb.2022-1183

中图分类号：

学科分类号：

摘要：

Accurately depict the short-term wind power prediction error and the characteristics of regional wind power output fluctuation is solved，the basis of large-scale parallel operation problem of uncertainty in energy for accurate characterization of wind power output fluctuation and the prediction error and the error of meteorological correlation，Gaussian mixture distribution probability model is set up and use the cloud model and the error of the observation curve structures，Then based on the coupling of the normal cloud and mixture Gaussian distribution probability distribution model，finally using a variety of probability density distribution model of single wind power prediction error，the cluster wind power prediction errors，the region’s weather errors as well as different power range of northern Hebei Province weather prediction error of the prediction error and the corresponding statistical analysis of correlation. The simulation results show that the proposed model has the best fitting effect，which verifies the effectiveness of the probabilistic model based on the coupling of normal cloud and mixed Gaussian distribution. © 2023 Science Press. All rights reserved.

引用

页码：361 / 366

页数：5

共 18 条

[1] Research on the scenario and roadmap of high proportion renewable energy development in China 2050［R］, (2015)
[2] LU Z X, Et al., Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy［J］, Automation of electric power systems, 46, 16, pp. 3-16, (2022)
[3] QIAO Y, WU W Z., Day- ahead wind power probabilistic forecast considering conditional dependency and temporal correlation［J］, Power system technology, 44, 7, pp. 2529-2537, (2020)
[4] LIU Y Q，, SHI J，, YANG Y P，, Et al., Uncertainty analysis of short term wind power forecasting based on error characteristics statistics［J］, Acta energiae solaris sinica, 33, 12, pp. 2179-2184, (2012)
[5] YANG X Y, XING G T，, Et al., Method of estimating frequency regulation capacity of wind farm based on wind power probability prediction［J］, Acta energiae solaris sinica, 43, 7, pp. 409-416, (2022)
[6] LIU M Y，, YAO F, LI Z G，, Et al., Power model parameter identification and wind resource utilization evaluation of wind turbine［J］, Acta energiae solaris sinica, 41, 12, pp. 305-315, (2020)
[7] WANG Y M, SONG P，, Et al., Short- term power forecasting method of wind farm based on Gaussian mixture model clustering［J］, Automation of electric power systems, 45, 7, pp. 37-43, (2021)
[8] ZHANG J H, WANG C Q, ZHANG T，, Et al., Statistical analysis of wind power forecasting errors based on Gaussian mixture model［J］, Smart power, 48, 7, pp. 59-64, (2020)
[9] YANG M, DONG J C., Study on characteristics of wind power fluctuation based on mixed distribution model［J］, Proceedings of the CSEE, 36, S1, pp. 69-78, (2016)
[10] LI D Y, LIU C Y., Study on the universality of the normal cloud model ［J］, Engineering science, 6, 8, pp. 28-34, (2004)

← 1 2 →