Distribution network voltage state assessment with distributed generation based on probabilistic power flow method

被引:0
作者
Mao R. [1 ]
Yuan K. [2 ]
Zhong J. [2 ]
Chen S. [2 ]
Lin C. [2 ]
机构
[1] Zhaoqing Power Supply Bureau of Guangdong Power Grid Corporation, Zhaoqing
[2] China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., Guangzhou
来源
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 02期
关键词
Distributed photovoltaic; Gram-Charlier series; Photovoltaic permeability; Probability assessment; Voltage quality;
D O I
10.7667/PSPC180055
中图分类号
学科分类号
摘要
To overcome the blind zone in the evaluation method of the operation status of the traditional distribution network caused by the high penetration of distributed power, this paper studies the state quantitative evaluation method of distribution network voltage quality which takes the random characteristics of distributed generation into account. In this paper, a method based on the invariant and Gram-Charlier series to calculate the probability power flow is introduced, and the evaluation system including the average over-limit probability of system voltage, the voltage confidence interval of node voltage and the maximum over-limit probability of node voltage is established. Then taking the distributed photovoltaic as the representative, the probabilistic model whose active output follows Beta distribution is proposed and an improved index of photovoltaic permeability that includes the photovoltaic fluctuation characteristics is defined so as to quantitatively evaluate the influence of different photovoltaic permeability on the voltage quality of the distribution network. Finally, the feasibility of this algorithm for the assessment of voltage quality status of distribution network is verified. © 2019, Power System Protection and Control Press. All right reserved.
引用
收藏
页码:123 / 130
页数:7
相关论文
共 25 条
[11]  
Liu Y., Gao S., Yang S., Et al., Review on algorithms for probabilistic load flow, Automation of Electric Power Systems, 38, 23, pp. 127-135, (2014)
[12]  
El-Khattam W., Hegazy Y.G., Salama M.M.A., Stochastic power flow analysis of electrical distributed generation systems, 2003 IEEE Power Engineering Society General Meeting, pp. 1141-1144, (2003)
[13]  
Duan Y., Gong Y., Tan X., Et al., Micro power network random power flow calculation method based on Monte Carlo simulation, Transactions of China Electrotechnical Society, 26, 1, pp. 274-278, (2011)
[14]  
Zhang J., Wang X., Jiang C., Et al., Probabilistic assessment of wind farm active power based on Monte-Carlo simulation, Power System Protection and Control, 42, 3, pp. 82-87, (2014)
[15]  
Fang S., Cheng H., Xu G., Et al., A Nataf transformation based on extended quasi Monte Carlo simulation method for solving probabilistic load flow problems with correlated random variables, Transactions of China Electrotechnical Society, 32, 2, pp. 255-263, (2017)
[16]  
Gao L., Power system risk assessment considering in intermittent of wind farms, (2012)
[17]  
Liu X., Zhao J., Luo W., Et al., A TPNT and cumulants based probabilistic load flow approach considering the correlation variables, Power System Protection and Control, 41, 22, pp. 13-18, (2013)
[18]  
Li Y., Li W., Yan W., Probabilistic power flow using the point estimate method considering bounded wind speeds, Proceedings of the CSEE, 35, 7, pp. 1606-1612, (2015)
[19]  
Zhou W., Jiang T., Hu S., Et al., Probabilistic assessment on voltage stability of AC/DC hybrid systems based on two-point estimate method, Power System Protection and Control, 43, 5, pp. 8-13, (2015)
[20]  
Pan W., Liu W., Yang Y., Point estimation method for probabilistically optimal power flow computation, Proceedings of the CSEE, 28, 16, pp. 28-33, (2008)