基于Copula理论和切片采样技术结合拉丁超立方抽样的概率潮流计算

为评估大规模新能源并网对电力系统概率潮流的影响,提出一种Copula理论、切片采样及拉丁超立方采样相结合的MCMC概率潮流计算方法。利用Copula理论建立计及输入变量相关性的概率分布模型,采用Kendall秩相关系数作为相关性测度,通过切片采样算法产生初始样本,引入拉丁超立方抽样技术对初始样本进行处理,提高算法的计算效率。以改造后的IEEE-14节点测试系统为算例,验证了文中方法的准确性和有效性,研究了风、光出力相关性对电力系统概率潮流的影响。结果表明风、光互补提高了系统运行的可靠性和经济性,考虑风、光相关性可以更合理地评估风、光并网对电力系统概率潮流的影响。

Probabilistic Load Flow Calculation Based on Copula Theory and Slice Sampling Technique Combined with Latin Hypercube Sampling

To assess the impact of large-scale new energy sources on the probabilistic power flow(PLF)of power sys-tems,a Markov Chain Monte Carlo(MCMC)PLF method based on Copula theory, slice sampling and Latin hyper-cube sampling is proposed in this paper.The probabilistic model of the correlative input variables is established by the Copula theory with the Kendall rank correlation coefficient which is used to measure the correlations.The sample space of random input variables is obtained by slice sampling and the Latin hypercube sampling is further introduced to deal with the initial samples to improve the calculation efficiency.The modified IEEE 14-bus system is used as an ex-ample to demonstrate the correctness and effectiveness of the presented method and the influence of correlations be -tween wind and photovoltaic power outputs on PLF is studied.The results show that wind and photovoltaic combined increases the reliability and economy of system operation and consideration of the correlations will provide a more ac -curate assessment on the effect of wind and photovoltaic outputs on PLF.