考虑光伏不确定性因素的电力系统概率潮流三点估计法

doi:10.3969/j.issn.2097-0706.2022.09.001

综合智慧能源 ›› 2022, Vol. 44 ›› Issue (9): 1-10.doi: 10.3969/j.issn.2097-0706.2022.09.001

• 综合能源系统 • 下一篇

考虑光伏不确定性因素的电力系统概率潮流三点估计法

高明¹(), 陈家豪²^,^*(), 王丽晓¹, 唐武琛¹, 王智东³, 张紫凡¹, 马海霞¹, 冯瑞珏¹, 周长鹏¹

1.广州城市理工学院电气工程学院,广州 510800
2.宜通世纪物联网研究院（广州）有限公司,广州 510665
3.华南理工大学电力学院智慧能源工程技术研究中心,广州 510640

收稿日期:2022-06-20 修回日期:2022-08-30 出版日期:2022-09-25 发布日期:2022-09-26
通讯作者: 陈家豪
作者简介:高明（1978）,男,高级工程师,博士,从事综合能源及数字电网研究工作, gaoyugui0508@163.com。
基金资助:
中央高校基本科研业务费成果转化类项目重大产学研合作扶持专项(x2dlD2201310);广州市基础与应用基础研究项目(202102020643)

A three-point probabilistic load flow estimation algorithm for the power system considering photovoltaic uncertainties

GAO Ming¹(), CHEN Jiahao²^,^*(), WANG Lixiao¹, TANG Wuchen¹, WANG Zhidong³, ZHANG Zifan¹, MA Haixia¹, FENG Ruijue¹, ZHOU Changpeng¹

1. College of Electrical Engineering of Guangzhou City Institute of Technology,Guangzhou 510800,China
2. Eastone Century Internet of Things Research Institute（Guangzhou）Company Limited,Guangzhou 510665,China
3. Research Center of Smart Energy Technology,School of Electric Power,South China University of Technology,Guangzhou 510640,China

Received:2022-06-20 Revised:2022-08-30 Online:2022-09-25 Published:2022-09-26
Contact: CHEN Jiahao

摘要/Abstract

摘要：

随着可再生能源和可调负荷接入比例的上升,传统电力系统将面临更复杂的不确定性因素。为有效刻画不确定性因素对系统运行的影响,提出基于三点估计法（3PEM）的概率潮流计算方法,首先采用2m+1策略的三点估计概率采样,减少采样时间,结合半不变量和Cornish-Fisher级数展开理论对计及负荷和光伏发电不确定性的电力系统概率潮流进行定量描述。通过IEEE 33节点系统进行验证,以蒙特卡罗法为参照,结果证明该方法的准确性和快速性。进一步研究对比光伏发电接入系统前后对系统概率潮流的影响,结果表明离光伏发电接入点越近,影响越显著。

关键词: 可再生能源, 概率潮流, 三点估计法, 半不变量, Cornish-Fisher级数展开, 光伏

Abstract:

As the access proportions of renewable energy and adjustable load rise,the traditional power system faces increasingly complex uncertainties. In order to effectively describe the influence of uncertain factors on system operation,a probabilistic load flow calculation method based on three-point estimation method（3PEM）is proposed. Firstly,3PEM takes 2m+1 sampling strategy to reduce the sampling time. Combining cumulant method and Cornish-Fisher series,the probabilistic load flow of the power system taking adjustable load and PV uncertainty into considerations is described quantitatively. The method is verified by an IEEE 33 node system. Taking the results obtained from Monte Carlo method as the reference,the proposed method is proved to be accurate and fast. Further study is made on comparing the effects of PV power grid-connection on the probability load flow,and the results show that the closer the PV power connection point is to the power system,the more significant impact is on the power system.

Key words: renewable energy, probabilistic flow, three-point estimation, cumulant, Cornish-Fisher series expansion, PV

中图分类号:

TK01：TM73

高明, 陈家豪, 王丽晓, 唐武琛, 王智东, 张紫凡, 马海霞, 冯瑞珏, 周长鹏. 考虑光伏不确定性因素的电力系统概率潮流三点估计法[J]. 综合智慧能源, 2022, 44(9): 1-10.

GAO Ming, CHEN Jiahao, WANG Lixiao, TANG Wuchen, WANG Zhidong, ZHANG Zifan, MA Haixia, FENG Ruijue, ZHOU Changpeng. A three-point probabilistic load flow estimation algorithm for the power system considering photovoltaic uncertainties[J]. Integrated Intelligent Energy, 2022, 44(9): 1-10.

图/表 10

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参考文献 33

[1]	康重庆, 杜尔顺, 张宁, 等. 可再生能源参与电力市场:综述与展望[J]. 南方电网技术, 2016, 10(3): 16-23.
	KANG Chongqing, DU Ershun, ZHANG Ning, et al. Review and prospect[J]. Southern Power System Technology, 2016, 10(3): 16-23.
[2]	陆为华. 考虑光伏出力与负荷时序相关性的概率潮流计算方法研究[D]. 沈阳: 东北电力大学, 2021.
[3]	ZHANG J, XIONG G, MENG K, et al. An improved probabilistic load flow simulation method considering correlated stochastic variables[J]. International Journal of Electrical Power & Energy Systems, 2019, 111: 260-268. doi: 10.1016/j.ijepes.2019.04.007
[4]	BORKOWSKA B. Probabilistic load flow[J]. IEEE Transactions on Power Apparatus and Systems, 1974(3): 752-759.
[5]	蔡德福, 周鲲鹏, 忻俊慧, 等. 概率潮流计算方法研究综述[J]. 湖北电力, 2015, 39(10): 20-25.
	CAI Defu, ZHOU Kunpeng, XIN Junhui, et al. A Review of probabilistic load flow algorithm[J]. Hubei Electric Power, 2015, 39(10):20-25.
[6]	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. Review on algorithms for probabilistic load flow in power system[J]. Automation of Electric Power Systems, 2014, 38(23):127-135.
[7]	周阳洋. 重要抽样法在概率潮流中的应用[D]. 北京: 华北电力大学, 2011.
[8]	王晗, 严正, 徐潇源, 等. 计及可再生能源不确定性的孤岛微电网概率潮流计算[J]. 电力系统自动化, 2018, 42(15):110-117.
	WANG Han, YAN Zheng, XU Xiaoyuan, et al. Probabilistic power flow calculation of islanded microgrid considering uncertainty of renewable energy[J]. Automation of Electric Power Systems, 2018, 42(15): 110-117.
[9]	YU H, CHUNG C Y, WONG K P, et al. Probabilistic load flow evaluation with hybrid latin hypercube sampling and Cholesky decomposition[J]. IEEE Transactions on Power Systems, 2009, 24(2): 661-667. doi: 10.1109/TPWRS.2009.2016589
[10]	杨小款. 大规模风电并网电力系统运行风险评估与分析[D]. 北京: 华北电力大学, 2021.
[11]	严岩, 苏宏升, 车玉龙. 基于改进LHS的含风电电力系统概率潮流计算[J]. 太阳能学报, 2018, 39(4): 1111-1118.
	YAN Yan, SU Hongsheng, CHE Yulong. Probabilistic power flow calculation of power system considering wind power based on improved LHS[J]. Acta Energiae Solaris Sinica, 2018, 39(4): 1111-1118.
[12]	杨晓萍, 王李瑾. 基于概率潮流的含分布式电源配电网优化[J]. 太阳能学报, 2021, 42(8):71-76.
	YANG Xiaoping, WANG Lijin. Optimization of distributed power distribution network based on probabilistic load flow[J]. Acta Energiae Solaris Sinica, 2021, 42(8):71-76.
[13]	张德隆, MUBAARAK Saif, 蒋思宇, 等. 基于概率潮流的光伏电站中储能系统的优化配置方法[J]. 储能科学与技术, 2021, 10(6):2244-2251.
	ZHANG Delong, MUBAARAK Saif, JIANG Siyu, et al. Optimal allocation method of energy storage in PV station based on probabilistic power flow[J]. Energy Storage Science and Technology, 2021, 10(6):2244-2251.
[14]	朱星阳, 刘文霞, 张建华. 考虑大规模风电并网的电力系统随机潮流[J]. 中国电机工程学报, 2013, 33(7):77-85.
	ZHU Xingyang, LIU Wenxia, ZHANG Jianhua. Probabilistic load flow method considering large-scale wind power integration[J]. Proceedings of the CSEE, 2013, 33(7):77-85.
[15]	张萍, 张红, 李云峰, 等. 基于改进LHS的半不变量法概率潮流计算[J]. 太阳能学报, 2021, 42(1): 14-20.
	ZHANG Ping, ZHANG Hong, LI Yunfeng, et al. Improved LHS based cumulant method for probabilistic load flow calculation[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 14-20.
[16]	ZHANG P, LEE S T. Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion[J]. IEEE Transactions on Power Systems, 2004, 19(1): 676-682. doi: 10.1109/TPWRS.2003.818743
[17]	FAN M, VITTAL V, HEYDT G T, et al. Probabilistic power flow studies for transmission systems with photovoltaic generation using cumulants[J]. IEEE Transactions on Power Systems, 2012, 27(4):2251-2261. doi: 10.1109/TPWRS.2012.2190533
[18]	石东源, 蔡德福, 陈金富, 等. 计及输入变量相关性的半不变量法概率潮流计算[J]. 中国电机工程学报, 2012, 32(28):104-113.
	SHI Dongyuan, CAI Defu, CHEN Jinfu, et al. Probabilistic load flow calculation based on cumulant method considering correlation between input variables[J]. Proceedings of the Chinese Society for Electrical Engineering, 2012, 32(28): 104-113.
[19]	MORALES J M, PEREZ-RUIZ J. Point estimate schemes to solve the probabilistic power flow[J]. IEEE Transactions on Power Systems, 2007, 22(4): 1594-1601. doi: 10.1109/TPWRS.2007.907515
[20]	CHEN C, WU W, ZHANG B, et al. Correlated probabilistic load flow using a point estimate method with Nataf transformation[J]. International Journal of Electrical Power & Energy Systems, 2015, 65: 325-333. doi: 10.1016/j.ijepes.2014.10.035
[21]	鄂霖, 马振, 肖宇, 等. 直流配电网的三点估计概率潮流计算方法[J]. 分布式能源, 2021, 6(3):38-46.
	E Lin, MA Zhen, XIAO Yu, et al. Three-point estimation probability power flow calculation method for DC distribution network[J]. Distributed Energy, 2021, 6(3): 38-46.
[22]	孟安波, 王鹏, 丁伟锋, 等. 基于强化学习及纵横交叉粒子群算法的电网最优潮流计算[J]. 华电技术, 2021, 43(8): 74-82.
	MENG Anbo, WANG Peng, DING Weifeng, et al. Optimal power flow calculation of power grid based on reinforcement learning and crisscross PSO algorithm particle swarm optimization[J]. Huadian Technology, 2021, 43(8): 74-82.
[23]	陈宏岩, 唐治国, 陈琦, 等. 基于区间潮流算法的低压配电网分散无功补偿优化方法[J]. 华电技术, 2020, 42(6): 1-5.
	CHEN Hongyan, TANG Zhiguo, CHEN Qi, et al. Optimized method on distributed reactive power compensation in low voltage distribution network based on interval power flow calculation[J]. Huadian Technology, 2020, 42(6): 1-5.
[24]	赵真, 袁旭峰, 徐玉韬, 等. 一种改进三点估计法的概率潮流计算方法[J]. 南方电网技术, 2020, 14(11): 43-48.
	ZHAO Zhen, YUAN Xufeng, XU Yutao, et al. An improved three-point estimate method for probability load flow calculation[J]. Southern Power System Technology, 2020, 14(11): 43-48.
[25]	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. Bayesian theory based calculation of probabilistic power flow considering correlation between multi-dimensional wind speed[J]. Automation of Electric Power Systems, 2021, 45(3): 157-165.
[26]	毛晓明, 叶嘉俊. 主元分析结合Cornish-Fisher展开的概率潮流三点估计法[J]. 电力系统保护与控制, 2019, 47(6): 66-72.
	MAO Xiaoming, YE Jiajun. A three-point estimate method for probabilistic load flow computation based on principal component analysis and Cornish-Fisher series[J]. Power System Protection and Control, 2019, 47(6): 66-72.
[27]	R-AFISHER. The advanced theory of statistics[J]. Nature, 1943, 152(3859):431-432. doi: 10.1038/152431a0
[28]	王晓龙. 基于两点估计法的电力系统概率潮流计算[D]. 太原: 太原理工大学, 2013.
[29]	陈思思. 计及分布式电源发电相关性的概率潮流算法研究[D]. 广州: 华南理工大学, 2019.
[30]	姚琼荣. 计及风电不确定性的电力系统潮流及暂态稳定分析[D]. 南宁: 广西大学, 2014.
[31]	王敏, 丁明. 含大型太阳能发电系统的极限传输容量概率计算[J]. 电力系统自动化, 2010, 34(7): 31-35.
	WANG Min, DING Ming. Probabilistic calculation of total transfer capability including large-scale solar park[J]. Automation of Electric Power Systems, 2010, 34(7): 31-35.
[32]	FUJIKOSHI Y, ULYANOV V V. Non-asymptotic analysis of approximations for multivariate statistics[M].Springer Singapore, 2020.
[33]	BARAN M E, WU F F. Network reconfiguration in distribution systems for loss reduction and load balancing[J]. IEEE Power Engineering Review, 1989, 9(4):101-102.

节点号	计及光伏(标幺值)		不计光伏(标幺值)		节点号	计及光伏(标幺值)		不计光伏(标幺值)
节点号	期望值	标准差	期望值	标准差	节点号	期望值	标准差	期望值	标准差
1	0.000 0	0.00E+00	0.000 0	0.00E+00	18	0.916 9	3.93E-03	0.913 1	2.30E-03
2	0.997 1	9.16E-05	0.997 0	7.30E-05	19	0.996 6	9.89E-05	0.996 5	8.20E-05
3	0.983 5	5.74E-04	0.982 9	4.53E-04	20	0.993 0	2.59E-04	0.992 9	2.53E-04
4	0.976 2	7.99E-04	0.975 5	6.34E-04	21	0.992 3	2.99E-04	0.992 2	2.94E-04
5	0.969 0	1.05E-03	0.968 1	8.33E-04	22	0.991 7	3.40E-04	0.991 6	3.36E-04
6	0.951 0	1.72E-03	0.949 7	1.40E-03	23	0.980 1	7.99E-04	0.979 3	6.31E-04
7	0.947 6	1.83E-03	0.946 2	1.47E-03	24	0.973 4	1.17E-03	0.972 7	1.06E-03
8	0.943 1	2.09E-03	0.941 3	1.57E-03	25	0.970 1	1.40E-03	0.969 4	1.31E-03
9	0.937 3	2.49E-03	0.935 1	1.68E-03	26	0.949 1	1.78E-03	0.947 7	1.47E-03
10	0.932 0	2.91E-03	0.929 2	1.81E-03	27	0.946 5	1.87E-03	0.945 2	1.58E-03
11	0.931 3	2.99E-03	0.928 4	1.83E-03	28	0.935 1	2.38E-03	0.933 7	2.16E-03
12	0.929 9	3.14E-03	0.926 9	1.87E-03	29	0.926 9	2.80E-03	0.925 5	2.61E-03
13	0.924 5	3.76E-03	0.920 8	2.05E-03	30	0.923 3	2.99E-03	0.921 9	2.81E-03
14	0.922 3	3.81E-03	0.918 5	2.13E-03	31	0.919 2	3.12E-03	0.917 8	2.95E-03
15	0.920 9	3.84E-03	0.917 1	2.17E-03	32	0.918 3	3.16E-03	0.916 9	2.99E-03
16	0.919 5	3.86E-03	0.915 7	2.21E-03	33	0.918 0	3.16E-03	0.916 6	2.99E-03
17	0.917 5	3.91E-03	0.913 7	2.28E-03

起点	终点	计及光伏/MW		不计光伏/MW		起点	终点	计及光伏/MW		不计光伏/MW
起点	终点	期望值	标准差	期望值	标准差	起点	终点	期望值	标准差	期望值	标准差
1	2	3.334 0	2.69E-01	3.918 1	9.27E-02	20	21	0.180 1	1.28E-02	0.180 1	1.27E-02
2	3	2.863 1	2.67E-01	3.444 7	8.98E-02	21	22	0.090 0	9.01E-03	0.090 0	9.01E-03
3	4	1.793 2	2.54E-01	2.363 2	6.09E-02	3	23	0.939 6	6.14E-02	0.939 7	6.14E-02
4	5	1.658 9	2.51E-01	2.223 3	5.86E-02	23	24	0.846 5	6.03E-02	0.846 5	6.03E-02
5	6	1.585 8	2.49E-01	2.144 5	5.72E-02	24	25	0.421 3	4.22E-02	0.421 3	4.22E-02
6	7	1.095 0	3.71E-02	1.095 3	3.71E-02	6	26	0.404 0	2.41E-01	0.950 9	4.00E-02
7	8	0.893 1	3.11E-02	0.893 4	3.11E-02	26	27	0.342 4	2.41E-01	0.888 3	3.93E-02
8	9	0.688 3	2.33E-02	0.688 5	2.33E-02	27	28	0.280 3	2.40E-01	0.825 0	3.85E-02
9	10	0.624 2	2.22E-02	0.624 4	2.22E-02	28	29	0.753 2	3.67E-02	0.753 6	3.68E-02
10	11	0.560 7	2.11E-02	0.560 8	2.11E-02	29	30	0.625 6	3.39E-02	0.625 8	3.39E-02
11	12	0.515 1	2.06E-02	0.515 2	2.06E-02	30	31	0.421 8	2.67E-02	0.421 8	2.68E-02
12	13	0.454 3	1.96E-02	0.454 4	1.96E-02	31	32	0.270 2	2.19E-02	0.270 2	2.19E-02
13	14	0.391 7	1.84E-02	0.391 7	1.84E-02	32	33	0.060 0	6.00E-03	0.060 0	6.00E-03
14	15	0.270 9	1.38E-02	0.270 9	1.39E-02	21	8	0.000 0	0.00E+00	0.000 0	0.00E+00
15	16	0.210 6	1.24E-02	0.210 6	1.24E-02	9	15	0.000 0	0.00E+00	0.000 0	0.00E+00
16	17	0.150 3	1.09E-02	0.150 3	1.09E-02	12	22	0.000 0	0.00E+00	0.000 0	0.00E+00
17	18	0.090 1	9.00E-03	0.090 1	9.01E-03	18	33	0.000 0	0.00E+00	0.000 0	0.00E+00
2	19	0.361 1	1.81E-02	0.361 1	1.81E-02	25	29	0.000 0	0.00E+00	0.000 0	0.00E+00
19	20	0.271 0	1.57E-02	0.271 0	1.57E-02

考虑光伏不确定性因素的电力系统概率潮流三点估计法

A three-point probabilistic load flow estimation algorithm for the power system considering photovoltaic uncertainties

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