  In this paper, we propose a non-parametric Bayesian network method to
generate synthetic scenarios of hourly generation for variable renewable
energy(VRE) plants. The methodology consists of a non-parametric estimation of
the probability distribution of VRE generation, followed by an inverse
probability integral transform, in order to obtain normally distributed
variables of VRE generation. Then, we build a Bayesian network based on the
evaluation of the spatial correlation between variables (VRE generation and
hydro inflows, but load forecast, temperature, and other types of random
variables could also be used with the proposed framework), to generate future
synthetic scenarios while keeping the historical spatial correlation structure.
Finally, we present a real-life case study, that uses real data from the
Brazilian power system, to show the improvements that the present methodology
allows for real-life studies.

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