Three-Point Estimation Probability Power Flow Calculation Method for DC Distribution Network

E Lin, MA Zhen , XIAO Yu , ZHU Yongqiang

Distributed Energy ›› 2021, Vol. 6 ›› Issue (3) : 38-46.

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Distributed Energy ›› 2021, Vol. 6 ›› Issue (3) : 38-46. DOI: 10.16513/j.2096-2185.DE.2106507
Basic Research

Three-Point Estimation Probability Power Flow Calculation Method for DC Distribution Network

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Abstract

With the development of distributed power sources based on wind power and photovoltaics and the increase of DC loads, DC power grids have attracted more and more attention. Compared with the AC distribution network, the DC distribution network can save a lot of commutation links, reduce losses and improve economic benefits while consuming a large amount of renewable energy and DC loads. This paper combined the probabilistic power flow calculation method based on the three-point estimation method and Nataf inverse transform for AC power grids with the DC power flow model, and proposed a method suitable for the probabilistic power flow calculation of the DC distribution network with multiple distributed power sources. Comparing the method in this paper with the Monte Carlo method, when the new energy capacity increases, the calculation error of the three-point estimation method is similar to that of Monte Carlo, and the calculation speed is increased; at the same time, the DC power flow calculation and AC power flow calculation using the three-point estimation will be used. In contrast, when applied to the DC system, the error tends to be smaller than that of the AC system, which illustrates the accuracy and effectiveness of the proposed calculation method.

Key words

DC distribution network / distributed power generation / probabilistic power flow / three-point estimation method

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Lin E , Zhen MA , Yu XIAO , et al. Three-Point Estimation Probability Power Flow Calculation Method for DC Distribution Network[J]. Distributed Energy Resources. 2021, 6(3): 38-46 https://doi.org/10.16513/j.2096-2185.DE.2106507

References

[1]
江道灼,郑欢. 直流配电网研究现状与展望[J]. 电力系统自动化2012, 36(8): 98-104.
JIANG Daozhuo, ZHENG Huan. Research status and prospects of DC distribution network[J]. Automation of Electric Power Systems, 2012, 36(8): 98-104.
[2]
王丹. 直流配电网研究现状简述[J]. 电子测试2020(20): 94-95, 86.
WANG Dan. A brief introduction to the research status of DC distribution network[J]. Electronic Testing, 2020(20): 94-95, 86.
[3]
宋强,赵彪,刘文华,等. 智能直流配电网研究综述[J]. 中国电机工程学报2013, 33(25): 9-19.
SONG Qiang, ZHAO Biao, LIU Wenhua, et al. Summary of research on smart DC distribution network[J]. Proceedings of the CSEE, 2013, 33(25): 9-19.
[4]
王成山,李鹏. 分布式发电、微网与智能配电网的发展与挑战[J]. 电力系统自动化2010, 34(2): 10-14+23.
WANG Chengshan, LI Peng. Development and challenges of distributed generation, microgrid and smart distribution network[J]. Automation of Electric Power Systems, 2010, 34(2): 10-14, 23.
[5]
刘宇,高山,杨胜春,等. 电力系统概率潮流算法综述[J]. 电力系统自动化2014, 38(23): 127-135.
LIU Yu, GAO shan, YANG Shengchun, et al. Summary of probabilistic power flow algorithms in power system[J]. Automation of Electric Power Systems, 2014, 38(23): 127-135.
[6]
赵真,袁旭峰,熊炜,等. 基于柔性互联配电网的概率潮流算法综述[J]. 电力科学与工程2020, 36(6): 17-23.
ZHAO Zhen, YUAN Xufeng, XIONG Wei, et al. Summary of probabilistic power flow algorithms based on flexible interconnected distribution network[J]. Electric Power Science and Engineering, 2020, 36(6): 17-23.
[7]
丁明,李生虎,黄凯. 基于蒙特卡罗模拟的概率潮流计算[J]. 电网技术2001, 25(11): 10-14, 22
DING Ming, LI Shenghu, HUANG Kai. Probabilistic power flow calculation based on Monte Carlo simulation[J]. Power System Technology, 2001, 25(11): 10-14, 22
[8]
蒋程,王硕,王宝庆,等. 基于拉丁超立方采样的含风电电力系统的概率可靠性评估[J]. 电工技术学报2016, 31(10): 193-206.
JIANG Cheng, WANG Shuo, WANG Baoqing, et al. Probabilistic reliability assessment of power systems containing wind power based on Latin hypercube sampling[J]. Transactions of the Chinese Society of Electrical Engineering, 2016, 31(10): 193-206.
[9]
张建波,张忠伟,杨洋. 改进拉丁超立方蒙特卡洛模拟[J]. 吉林大学学报(信息科学版), 2018, 36(4): 452-458.
ZHANG Jianbo, ZHANG Zhongwei, YANG Yang. Improved latin hypercube Monte Carlo simulation[J]. Journal of Jilin University(Information Science Edition), 2018, 36(4): 452-458.
[10]
李聪聪,王彤,相禹维,等. 基于改进高斯混合模型的概率潮流解析方法[J]. 电力系统保护与控制2020, 48(10): 146-155.
LI Congcong, WANG Tong, XIANG Yuwei, et al. Probabilistic power flow analysis method based on improved Gaussian mixture model[J]. Power System Protection and Control, 2020, 48(10): 146-155.
[11]
孙蓉,吕振华,廖星星,等. 基于广义半不变量及最大熵法的电网概率潮流分析[J]. 电力建设2020, 41(7): 33-41
SUN Rong, LV Zhenhua, LIAO Xingxing, et al. Probabilistic power flow analysis of power grid based on generalized semi-invariant and maximum entropy method[J]. Electric Power Construction, 2020, 41(7): 33-41.
[12]
张立波,程浩忠,曾平良,等. 基于Nataf逆变换的概率潮流三点估计法[J]. 电工技术学报2016, 31(6): 187-194.
ZHANG Libo, CHENG Haozhong, ZENG Pingliang, et al. Probabilistic power flow three-point estimation method based on Nataf inverse transform[J]. Transactions of the Chinese Society of Electrical Engineering, 2016, 31(6): 187-194
[13]
CHE Y, WANG X, LV X, et al. Probabilistic load flow using improved three point estimate method[J]. International Journal of Electrical Power & Energy Systems, 2020, 117: 105618.1-105618.11.
[14]
苏晨博,刘崇茹,李至峪,等. 基于贝叶斯理论的考虑多维风速之间相关性的概率潮流计算[J]. 电力系统自动化2021, 45(3): 157-165.
SU Chenbo, LIU Chongru, LI Zhiyu, et al. Probabilistic power flow calculation involving the correlation between multi-dimensional wind speeds based on Bayesian theory[J]. Power System Automation, 2021, 45(3): 157-165.
[15]
RAMADHANI U H, SHEPERO M, MUNKHAMMAR J, et al. Review of probabilistic load flow approaches for power distribution systems with photovoltaic generation and electric vehicle charging[J]. International Journal of Electrical Power & Energy Systems, 2020, 120: 106003.
[16]
WANG C, LIU C, TANG F, et al. A scenario-based analytical method for probabilistic load flow analysis[J]. Electric Power Systems Research, 2020, 181: 106193.1-106193.9.
[17]
李韦姝. 直流配电网潮流计算模型及算法研究[D]. 北京:华北电力大学,2015.
LI Weishu. Research on power flow calculation model and algorithm of DC distribution network[D]. Beijing: North China Electric Power University, 2015.
[18]
任向阳,周攀,戴朝波. 适用于光伏发电直流并网的DC-DC变换器[J]. 分布式能源2020, 5(2): 27-34.
REN Xiangyang, ZHOU Pan, DAI Chaobo. DC-DC converter suitable for DC grid-connected photovoltaic power generation[J]. Distributed Energy, 2020, 5(2): 27-34.
[19]
雷婧婷,安婷,杜正春,等. 含直流配电网的交直流潮流计算[J]. 中国电机工程学报2016, 36(4): 911-918.
LEI Jingting, AN Ting, DU Zhengchun, et al. AC and DC power flow calculation of DC distribution network[J]. Proceedings of the CSEE, 2016, 36(4): 911-918.
[20]
张建波. 基于概率潮流的分布式电源优化配置的研究[D]. 大庆:东北石油大学,2019.
ZHANG Jianbo. Research on optimal configuration of distributed power generation based on probabilistic power flow [D]. Daqing: Northeast Petroleum University, 2019.
[21]
陆为华,李国庆,董存,等. 计及光伏出力与负荷相关性的电力系统概率潮流计算方法[J]. 分布式能源2019, 4(5): 1-9.
LU Weihua, LI Guoqing, DONG Cun, et al. Power system probabilistic power flow calculation method considering the correlation between photovoltaic output and load[J]. Distributed Energy, 2019, 4(5): 1-9.
[22]
韩宏志,杨洋,郜宁,等. 基于正态分布的典型负荷日拟合方法[J]. 分布式能源2020, 5(4): 69-73.
HAN Hongzhi, YANG Yang, GAO Ning, et al. The fitting method of typical load day based on normal distribution[J]. Distributed Energy, 2020, 5(4): 69-73.
[23]
戴小青. 电力系统概率潮流新算法及其应用[D]. 北京:华北电力大学,2006.
DAI Xiaoqing. New algorithm of probabilistic power flow in power system and its application[D]. Beijing: North China Electric Power University, 2006.
[24]
赵冠琨. 直流配电网潮流计算模型及算法[J]. 电力建设2016, 37(5): 109-117.
ZHAO Guankun. Power flow calculation model and algorithm for DC distribution network[J]. Electric Power Construction, 2016, 37(5): 109-117.
[25]
LIU P L, KIUREGHIAN A D. Multivariate distribution models with prescribed marginals and covariances[J]. Probabilistic Engineering Mechanics, 1986, 1(2): 105-112.

Funding

National Key Research and Development Program of China(2018YFB0904701)
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