Abstract
Because the penetration level of renewable energy sources has increased rapidly in recent years, uncertainty in power system operation is gradually increasing. As an efficient tool for power system analysis under uncertainty, probabilistic power flow (PPF) is becoming increasingly important. The point-estimate method (PEM) is a well-known PPF algorithm. However, two significant defects limit the practical use of this method. One is that the PEM struggles to estimate high-order moments accurately; this defect makes it difficult for the PEM to describe the distribution of non-Gaussian output random variables (ORVs). The other is that the calculation burden is strongly related to the scale of input random variables (IRVs), which makes the PEM difficult to use in large-scale power systems. A novel approach based on principal component analysis (PCA) and high-dimensional model representation (HDMR) is proposed here to overcome the defects of the traditional PEM. PCA is applied to decrease the dimension scale of IRVs and eliminate correlations. HDMR is applied to estimate the moments of ORVs. Because HDMR considers the cooperative effects of IRVs, it has a significantly smaller estimation error for high-order moments in particular. Case studies show that the proposed method can achieve a better performance in terms of accuracy and efficiency than traditional PEM.
Highlights
In recent decades, the penetration level of renewable energy sources (RESs), such as wind and solar power, has increased rapidly
The motivation of this paper is to overcome the drawbacks of existing point-estimate method (PEM); we propose a novel probabilistic power flow algorithm based on principal component analysis (PCA) and high-dimensional model representation (HDMR)
The estimation error of the third-order moment is relatively high for all evaluated methods. This is because the absolute value of the third-order moment is usually very close to 0; a small variation will result in a large relative error; this kind of large relative error has a minor influence on describing the probability distribution function (PDF) of a random variable
Summary
The penetration level of renewable energy sources (RESs), such as wind and solar power, has increased rapidly. The second is that it is difficult for the existing methods to estimate high-order moments accurately, such as skewness and kurtosis, which means that existing methods are inappropriate to apply in a power system that has non-Gaussian IRVs. if the drawbacks of the PEM can be eliminated effectively, as an algorithm that can balance the calculation efficiency and accuracy, it has practical application value. The motivation of this paper is to overcome the drawbacks of existing PEMs; we propose a novel probabilistic power flow algorithm based on principal component analysis (PCA) and high-dimensional model representation (HDMR). Unlike existing PEM schemes, HDMR considers the cooperative effects of IRVs on ORVs. Case studies show that the estimation error of high-order moments is significantly reduced compared to that of other PEM schemes;.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
