Comparison for Efficient Methods in Handling Probabilistic DC Optimal Power Flow Calculation

This paper compares three efficient probabilistic methods to deal with the probabilistic DC optimal power flow calculation, including Hong's point estimate method, Zhao's point estimate method and Latin hypercube sampling based Monte Carlo simulation method. In combination with Nataf transformation, all of the three methods are able to handle Pearson correlated random variables following Gaussian and non-Gaussian distributions. Furthermore, the performances of three methods are comprehensively tested and compared in the modified IEEE 118-bus system, according to such reference results obtained by the crude Monte Carlo simulation method with 10
⁴
simple random samples.