基于Copula理论的计及输入随机变量相关性的概率潮流计算

Probabilistic load flow considering correlation between input random variables based on Copula theory

考虑不确定性因素和相关性因素对准确全面评估电力系统潮流运行特性具有重要意义。引入Copula理论构建具有相关性输入随机变量的概率分布模型;对于边缘分布不服从常见分布函数的输入随机变量,提出了一种根据实测离散数据构建其经验累积分布函数和逆函数的方法;并将Copula理论与蒙特卡罗仿真法相结合,提出了一种可灵活处理输入随机变量相关性的概率潮流计算方法。以实际风电出力为例,对由Copula理论所构建的具有相关性输入随机变量概率分布模型的准确性进行了评估,并在加入风电后的IEEE 57节点系统上分析了所提概率潮流计算方法的有效性。仿真结果验证了所提方法的有效性和准确性。

It is of great significance to consider uncertainty factors and correlation factors in order to assess load flow characteristics of power system accurately and comprehensively. Copula theory is introduced to establish the probability distribution model of correlated input random variables. A method to obtain input random variable＇s empirical cumulative distribution function and inverse fimction based on measured discrete data is proposed to handle the input random variable whose marginal distribution doesn＇t follow common distribution function. Copula theory is applied into Monte Carlo simulation method, and a probabilistic load flow （PLF） method which can deal with the correlation between input random variables flexibly is proposed. Taking wind power for example, the accuracy of probability distribution model of correlated input random variables established by Copula theory is evaluated. The validity of proposed PLF method is tested on the IEEE 57 bus system with wind power. The simulation results show that the proposed method is effective and accurate.