A Discrete Point Estimate Method for Probabilistic Load Flow Based on the Measured Data of Wind Power

Probabilistic load flow (LF) calculation is the first step to evaluate the potential impact of the integrated wind power on the power system. Although research works show that the wind speed can be modeled by a Weibull probability density function (pdf), due to the nonlinear relationship between the wind speed and the wind power as well as many other influencing factors, it is hard to fit wind power to any common pdfs. At the same time, the relationship between the input and output variables of LF calculation is nonlinear. In view of the two characteristics, the point estimate method and Gram-Charlier expansion method are combined. Based only on the sample data of the wind power, the expectation, variance, and cumulative distribution of the output random variables can be estimated with the method by 2n + 1 times of LF calculation, where n is the number of input stochastic variables, eliminating the need for the distribution of the input variables. The simulation results on the New England Test System and New York Power Pool (NETS-NYPP) system and the actual Northeast China Grid show that the proposed method provides higher precision with less computation burden. The method can also be applied to other problems with uncertainty factors whose distribution is unknown in the power system.