Application of HDMR in power flow probability evaluation and regulation

Abstract：High dimensional model representation ( HDMR ) has unique performance in describing the relationship between system output and multiple inputs, and the power flow state of the grid and the source flow injection of multiple nodes in the network are in line with the relevant attributes of HDMR. Based on this, HDMR is applied to the probabilistic evaluation and regulation of power flow in power grid : the HDMR relationship between the power transmitted on the key branches and the power supply and load is constructed through typical representative samples, and the traditional power flow calculation method is replaced to undertake the large-scale power flow calculation task in the process of power flow probability evaluation, so as to greatly improve the efficiency of generating the cumulative probability distribution of power flow in key branches and obtaining its related characteristics. For the key branch power flow congestion problem, a probabilistic control strategy is designed by using the global sensitivity information provided by HDMR and taking into account the energy saving and emission reduction performance indicators. The example shows that the application of HDMR can significantly improve the computational efficiency of power flow probability assessment and the performance of key branch power flow blocking probability regulation.