基于拟蒙特卡罗和半不变量法的概率潮流计算
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摘要
为实现含风电出力电网概率潮流计算精度的提高,提出一种结合拟蒙特卡罗和半不变量的方法。建立风电出力模型,采用拟蒙特卡罗模拟法(QMCS)抽取Sobol确定性低偏差点列,结合半不变量法,算出节点状态变量以及各支路潮流的半不变量,引入Gram-Charlier级数,以概率潮流计算为工具,对相关节点电压进行模拟,并与传统蒙特卡罗模拟(MCS)方法和MCS结合半不变量法对比。算例表明,相同抽样次数下QMCS结合半不变量法的计算结果更接近真解,并且计算速度相对更快。
In order to improve the accuracy of probabilistic power flow calculation with wind power system,a method combining quasi Monte Carlo and semi-invariant method is proposed in this paper. The establishment of wind power model,the quasi Monte Carlo method (QMCS) is adopted to extract Sobol deterministic low discrepancy,combined with the semiinvariant method,semi-invariant computing node state variables and branch power flow,the introduction of Gram-Charlier series expansion,and the probabilistic load flow calculation tool of node voltage is simulated and compared with the traditional Monte Carlo method and Monte Carlo simulation with semi-invariant contrast method. The calculation results show that the results of QMCS combined with the semi-invariants method under the same sampling times are closer to the true solution,and the calculation speed is relatively faster.
In order to improve the accuracy of probabilistic power flow calculation with wind power system,a method combining quasi Monte Carlo and semi-invariant method is proposed in this paper. The establishment of wind power model,the quasi Monte Carlo method (QMCS) is adopted to extract Sobol deterministic low discrepancy,combined with the semiinvariant method,semi-invariant computing node state variables and branch power flow,the introduction of Gram-Charlier series expansion,and the probabilistic load flow calculation tool of node voltage is simulated and compared with the traditional Monte Carlo method and Monte Carlo simulation with semi-invariant contrast method. The calculation results show that the results of QMCS combined with the semi-invariants method under the same sampling times are closer to the true solution,and the calculation speed is relatively faster.
引文
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[2]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 Powet System[J].Automation of Electric Power Systems,2014,38(23):127-135.
[3]柳志航,卫志农,孙国强,等.计及参数模糊性的含风电场电力系统概率潮流计算[J].电网技术,2017,41(7):2308-2318.Liu Zhihang,Wei Zhinong,Sun Guoqiang,et al.Probabilistic Power Flow Calculation of Power System With Wind Farms Considering Fuzzy Parameters[J].Power System Technology,2017,41(7):2308-2318.
[4]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):16,77-85.Zhu Xingyang,Liu Wenxia,Zhang Jianhua.Probabilistic Load Flow Method Considering Large-scale Wind Power Integration[J].Proceedings of the CESS,2013,33(7):16,77-85.
[5]艾小猛,文劲宇,吴桐,等.基于点估计和Gram-Charlier展开的含风电电力系统概率潮流实用算法[J].中国电机工程学报,2013,33(16):16-23.Ai Xiaomeng,Wen Jinyu,Wu Tong,et al.A Practical Algorithm Based on Point Estimate Method and Gram-Charlier Expansion for Probabilistic Load Flow Calculation of Power Systems Incorporating Wind Power[J].Proceedings of the CESS,2013,33(16):16-23.
[6]柳永妍,乐健,高鹏,等.基于两点估计法的有源配电网概率潮流计算方法研究[J].电测与仪表,2017,54(5):1-8.Liu Yongyan,Le Jian,Gao Peng,et al.A Study of Probabilistic Load Flow Calculation in Active Distribution Network Based on Two-point Estimation Method[J].Electrical Measurement&Instrumentation,2017,54(5):1-8.
[7]侯雨伸,王秀丽,张玥,等.考虑维度重要性的电力系统可靠性评估拟蒙特卡洛方法[J].电力系统自动化,2016,40(16):31-37,158.Hou Yushen,Wang Xiuli,Zhang Yue,et al.Dimensional Important Based Quasi-Carlo Method for Power System Reliability Evaluation[J].Automation of Electric Power System,2016,40(16):31-37,158.
[8]董成.拟蒙特卡罗方法在亚式期权敏感性参数估计中的应用[D].北京:清华大学,2011.Dong Cheng.Quasi-Monte Carlo Methods Applied in Sensitivities of A-sian Options[D].Beijing:Tsinghua University,2011.
[9]张楷旋,陈建英,郭创新,等.考虑天气因素的含风电场电力系统的概率潮流分析[J].电网技术,2014,38(12):3418-3423.Zhang Kaixuan,Chen Jianying,Guo Chuangxin,et al.Weather Factor Based Analysis on Probabilistic Load Flow of Power Grid Containing Wind Farms[J].Power System Technology,2014,38(12):3418-3423.
[10]赵建伟,李禹鹏,杨增辉,等.基于拟蒙特卡罗模拟和核密度估计的概率静态电压稳定计算方法[J].电网技术,2016,40(12):3833-3839.Zhao Jianwei,Li Yupeng,Yang Zenghui,et al,Probabilistic Static Voltage Stability Calculation Based on Quasi-Monte Carlo and Kernel Density Estimation[J].Power System Technology,2016,40(12):3833-3839.
[11]Singhee A.Novel algorithms for fast statistical analysis of scaled circuits[D].USA:Carnegie Mellon University,2007.
[12]Cario M C,Nelson B L.Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix[R].USA:Technical Report,Department of Industrial Engineering and Management Sciences,Northwestern University,Evanston,Illinois,1997.
[13]侯雨伸,王秀丽,刘杰,等.基于拟蒙特卡罗方法的电力系统可靠性评估[J].电网技术,2015,39(3):744-750.Hou Yushen,Wang Xiuli,Liu Jie,et al.A Quasi-Monte Carlo Method Based Power System Reliability Evaluation[J].Power System Technology,2015,39(3):744-750.