A Markov-Chain Monte-Carlo technique for probabilistic load flow calculation

In this paper, a new method is proposed for the probabilistic load flow calculation in power systems. The proposed method is based on a hybrid technique that consists of Markov Chain Mont Carlo (MCMC), deterministic annealing expectation maximization (DAEM) algorithm to evaluate the effect of uncertainties of input variables on the output ones and in the nonlinear load flow. MCMC is useful for generating the samples from an arbitrary probabilistic distribution function (PDF) to reflect the non-Gaussian PDF and the nonlinear correlation. DAEM evaluates the maximum likelihood estimate of the non-Gaussian PDF. The proposed method is successfully applied to a sample system.