Probabilistic load flow calculation of unbalanced distribution network based on linearized backward/forward sweep equations

被引：0
|
作者：
Fu Y. [1 ]
Liu H. [1 ]
Su X. [1 ]
Mi Y. [1 ]
Zheng S. [2 ]
机构：
[1] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai
[2] NARI Technology Co. Ltd., State Grid Electric Power Research Institute, Nanjing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 03期
关键词：
Distributed power generation; Distribution network; Linearization; Probability density function; Uncertainty analysis;
D O I：
10.19912/j.0254-0096.tynxb.2018-1050
中图分类号：
学科分类号：
摘要：
The traditional cumulant method uses Jacobian matrix to express the linear relationship between input and output random variables and assumes network is three-phase balanced. However, due to the high R/X of real distribution networks, its Jacobian matrix is often morbid, and distribution network is significantly unbalanced due to the high permeation of distributed photovoltaics (PVs) and the uneven distribution of loads. To solve the problems above, a probabilistic power flow (PLF) algorithm of unbalanced active distribution network based on linearized load flow equations is presented. The algorithm linearizes three-phase back/forward sweep equations with high accuracy, synthesizes the cumulant method and Gram-Charlier expansion theory to solve PLF. The proposed algorithm is validated in terms of accuracy, computational efficiency and robustness by simulating on a real Australian unbalanced LV network during 24 hours through Matlab, with two different cases selected and compared to demonstrate PVs integration into distribution network improves the operation performance of network. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：283 / 289
页数：6
相关论文
共 27 条
  • [1] WARREN J J, NEGNEVITSKY M, NGUYEN T., Probabilistic load flow analysis in distribution networks with distributed solar generation, IEEE Power and Energy Society General Meeting, (2016)
  • [2] DOPAZO J F, KLITINl O A, SASSON A M., Stochastic load flows, IEEE transactions on power apparatus & systems, 94, 2, pp. 299-309, (1975)
  • [3] DING M, LI S H, HUANG K., Probabilistic load flow calculation based on Monte Carlo simulation, Power system technology, 25, 11, pp. 10-14, (2001)
  • [4] CAI D F, SHI D Y, CHEN J F., Probabilistic load flow computation with polynomial normal transformation and Latin hypercube sampling, IET generation, transmission & distribution, 7, 5, pp. 474-482, (2013)
  • [5] SAUNDERS C S., Point estimate method addressing correlated wind power for probabilistic optimal flow, IEEE transactions on power systems, 29, 3, pp. 1045-1054, (2014)
  • [6] AI X M, WEN J Y, WU T, Et al., A practical algorithm based on point estimate method and Gram-Charlier expansion for probabilistic load flow calculation of power, Proceedings of the CSEE, 33, 16, pp. 16-23, (2013)
  • [7] LI X, LI Y Z, ZHANG S H., Analysis of probabilistic optimal power flow taking account of the variation of load power, IEEE transactions on power systems, 23, 3, pp. 992-999, (2008)
  • [8] WAN C, XU Z, DONG Z Y, Et al., Probabilistic load flow computation using first-order second-moment method, IEEE Power and Energy Society General Meeting, pp. 1-6, (2012)
  • [9] JULIER S J, UHLMANN J K., Unscented filtering and nonlinear estimation, Proceedings of the IEEE, 92, 3, pp. 401-422, (2004)
  • [10] KOLBA D, PARKS T., A prime factor FFT algorithm using high-speed convolution, IEEE transactions on acoustics speech & signal processing, 25, 4, pp. 281-294, (2003)
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