Consisting of an electro-energetic matrix with hydro predominance and a continental proportion territory, Brazil presents unique characteristics, being able to make use of the abundant water resources in the national territory. Approximately 65 percent of the electricity generation capacity comes from hydropower while 28 percent from thermoelectric plants. It is known that hydrological regimes have a stochastic nature and it is necessary to treat them so the energy system can be planned, thus the hydrothermal dispatch is extremely important and characterized by its stochastic dependence. From the natural streamflows it is possible to calculate the Natural Inflow Energy (NIE) that will be used directly in the synthetic series simulation process, which, in turn, are used on the optimization process, responsible for optimal policy calculation in order to minimize the system operational costs. The studies concerning the simulation of synthetic scenarios of NIE have been developing with new methodological proposals over the years. Such developments often presuppose data Gaussianity, so that a parametric distribution can be fitted to them. It was noticed that in the majority of real cases, in the context of the Brazilian Electrical Sector, the data cannot be treated like that, since they present in their density relevant tail behavior and skewness. It is necessary for the National Interconnected System (SIN) operational planning that the intrinsic skewness behavior is amenable to reproduction. Thus, this paper proposes two non-parametric approaches to scenarios simulation. The first one refers to the process of NIE series residues sampling, using a Markov Chain Monte Carlo (MCMC) technique and the Kernel Density Estimation. The second methodology is also proposed where the MCMC is applied periodically and directly in the NIE series to simulate synthetic scenarios using an innovative approach for transitions between matrices. The methodologies implementation results, observed graphically and based on statistical tests of adherence to the historical data, indicate that the proposals can reproduce with greater accuracy the asymmetric characteristics without losing the ability to reproduce basic statistics. Thus, one can conclude that the proposed models are good alternatives in relation to the current model of the Brazilian Electric Sector.