This paper proposes an improved probabilistic load and distributed energy resources (DERs) modeling as pseudo-measurements by considering the correlation to be used for distribution network state estimation. The two-point method (TPM) is applied for the modeling of pseudo-measurements. The proposed method has the ability to estimate the states of a distribution network with high accuracy and short computational time. To implement the proposed scheme, the probability density functions (PDFs) of uncertain loads and DERs at different buses are extracted using historical data. Then, the TPM achieves two concentration points at each bus from obtained PDFs. Finally, the weighted least squares state estimation method is utilized at these two concentration points to obtain the probabilistic distribution of output variables. To examine the effectiveness of the suggested model, simulations are carried out on IEEE 69-bus standard test system. The proposed TPM-based state estimation approach is then compared with other conventional methods such as the Gaussian-based model, Gaussian mixture model (GMM) and Monte Carlo simulation. The superiority of the proposed TPM-based state estimation model over the GMM and Gaussian model is confirmed by a significant decrease in the running time and a noteworthy increase in the accuracy of all estimated variables.

通过考虑将用于配电网状态估计的相关性，本文提出了一种改进的概率负载和分布式能源（DER）模型作为伪测量。两点法（TPM）用于伪测量的建模。所提出的方法具有以高精度和短计算时间估计配电网状态的能力。为了实施所提出的方案，使用历史数据提取了不同公交车上的不确定负载和DER的概率密度函数（PDF）。然后，TPM根据获得的PDF在每个总线上达到两个集中点。最后，在这两个集中点使用加权最小二乘状态估计方法来获得输出变量的概率分布。为了检查建议模型的有效性，在IEEE 69总线标准测试系统上进行了仿真。然后将所提出的基于TPM的状态估计方法与其他常规方法进行比较，例如基于高斯的模型，高斯混合模型（GMM）和蒙特卡洛模拟。所建议的基于TPM的状态估计模型优于GMM和高斯模型的原因是，运行时间显着减少，所有估计变量的准确性均显着提高。