Probabilistic Load Flow Calculation based on an enhanced convolution technique

The increasing uncertainties grid operators have to face in their every-day work lead to the necessity for fast and accurate information about the probability of certain operational states of the operated network. Algorithms traditionally used in grid operation and planning are only able to evaluate discrete operational states of the particular power grid. A consideration and integration of probabilistic data can only be done by the analysis of selected discrete states followed by an interpolation. The algorithm presented in this paper falls into the category of Probabilistic Load Flow Calculation with a new approach for the modeling of the underlying network, as well as measures to reduce the inaccuracy introduced by linearization. Aspects like accuracy and calculation time in comparison to existing approaches are covered in detail.