  For the purpose of Monte Carlo scenario generation, we propose a graphical
model for the joint distribution of wind power and electricity demand in a
given region. To conform with the practice in the electric power industry, we
assume that point forecasts are provided exogenously, and concentrate on the
modeling of the deviations from these forecasts instead of modeling the actual
quantities of interest. We find that the marginal distributions of these
deviations can have heavy tails, feature which we need to handle before fitting
a graphical Gaussian model to the data. We estimate covariance and precision
matrices using an extension of the graphical LASSO procedure which allows us to
identify temporal and geographical (conditional) dependencies in the form of
separate dependence graphs. We implement our algorithm on data publicly
available for the Texas grid as managed by ERCOT, and we confirm that the
geographical dependencies identified by the algorithm are consistent with the
geographical relative locations of the zones over which the data were
collected.
