Beschreibung:
Uncertainties related to the energy produced and consumed in smart grids especially in microgrids are among the major issues for both the design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. This work thus provides a valuable and straightforward method based on Markov chains to generate a large number of probabilistic energy production and demand profiles when historical measurements are available. The method is intended for energy modelers seeking an efficient generation approach to test different design and operation methods for microgrids without spending too much time in this generation phase. Results show that the proposed method can capture the main statistical features and the temporal variability of real data at both long and short time scales, despite the method simplicity. Furthermore, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements