Representation of non-Gaussian probability distributions in stochastic load-flow studies by the method of Gaussian sum approximations

The stochastic load flow (SLF) is extended to include non-Gaussian `long-term¿¿ nodal probability-density-function (PDF) data by replacing each non-Gaussian PDF with a `Gaussian sum¿¿ approximation. A series of SLFs (stochastic load flows) are then performed and the results recombined, with the correct weightings, to generate non-Gaussian PDF profiles for busbars and lines of interest. Generally less than half of the most likely convolution components need evaluating. P ¿¿ ¿¿, Q ¿¿ V decomposition and nodal dependence is easily incorporated in the study and moment matching can be used to determine the `best¿¿ lower order Gaussian sum approximation. Long-term network topological impedance changes can also be included in the proposed method.