Real-time fast calculation of the power flow of the interconnected power grid is an important guarantee for the reliable operation of the interconnected power grid. The topology of the interconnected power grid is complex, and the calculation of the power flow of the whole network is large and timeconsuming. The sensitivity equivalent model can effectively simplify the interconnected power grid and shorten the time of the power flow calculation of the whole network. The operating state of the power grid is constantly changing. In order to ensure the accuracy of the power flow calculation results, it is necessary to update the uniform sensitivity equivalent model in real time. Due to factors such as the vertical management system between the interconnected power grids and the principle of commercial confidentiality, it is difficult to share information between interconnected power grids in real time, and the sensitivity equivalent model cannot be updated in real time, resulting in too much error in the calculation results and no reference value. To solve this problem, this paper proposes an online update method for the sensitivity equivalent model of the interconnected power grid based on power big data to solve the problem of excessive power flow calculation errors caused by the untimely update of the equivalent model parameters, and to ensure the operational reliability of the interconnected power grid.
Stochastic Power Flow Calculation of High-Density Correlation Input Power Grid Based on Hybrid Gaussian Model