The electricity system is increasingly exposed to uncertainties and variability dependent on weather conditions, due to the penetration of non-programmable renewable sources and the electrification of consumption. In order to evaluate the adequacy indices of the electricity system and plan the developments of the network from different points of view, tools are necessary that take these characteristics into account. One of the solutions used to do probabilistic planning analyses is Monte Carlo simulation, which requires an hourly input time series for each stochastic variable for each iteration. The SPOPSI – Stochastic Power Profile SImulation – tool is designed to generate realizations of such stochastic variables. This tool separately analyzes the zonal time series of photovoltaic and wind production as well as load absorption: it extracts the deterministic part (annual trend and daily seasonality), identifies the stochastic part through VAR, ARMA and regression models, and uses these models to generate a number of annual hourly series decided a priori. These series are recombined with the deterministic part initially extracted from the time series, so the new series generated by the models have profiles and statistical characteristics comparable to those of the original series. Given that the power produced from renewable sources and the load absorbed depend on meteorological conditions, climate forecasts were taken into consideration, where they exist and are uniquely defined on the area on which the power series are measured. The air temperature was considered, the time series of which were used to identify a relationship with the load time series. A comparison between historical and simulated power series revealed that the latter have a plausible trend because they reflect the historical profiles and preserve the statistical properties. They are also different enough from each other in order to provide to the Monte Carlo iterations that receive them as input a sufficient variability to cover the possible realizations of the power series generated or absorbed in reality. The simulated series also contain extreme events, with a frequency comparable to that of the historical series. Finally, SPOPSI was applied to analyze a specific realization of net load series, whose characteristics were evaluated by calculating flexibility indices.