Probabilistic Load Flow by Using Nonparametric Density Estimators

In this paper, a new method has been proposed to calculate the probability density function of load flow results in electrical power systems. The proposed method has introduced an adaptive kernel density estimation based on smoothing properties of linear diffusion process. This method has been applied to the electrical power system including wind energy. In addition, the correlated bus loads have been considered in the power system. In order to demonstrate the effectiveness of the proposed method, it has been applied to the modified New England 39-bus power system including a wind farm. Simulation results show the accuracy of the proposed method in density function estimation of output random variables.

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