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

Modeling Multiscale Variable Renewable Energy and Inflow Scenarios in Very Large Regions with Nonparametric Bayesian Networks