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For the purpose of Monte Carlo scenario generation, we propose a graphicalmodel for the joint distribution of wind power and electricity demand in agiven region. To conform with the practice in the electric power industry, weassume that point forecasts are provided exogenously, and concentrate on themodeling of the deviations from these forecasts instead of modeling the actualquantities of interest. We find that the marginal distributions of thesedeviations can have heavy tails, feature which we need to handle before fittinga graphical Gaussian model to the data. We estimate covariance and precisionmatrices using an extension of the graphical LASSO procedure which allows us toidentify temporal and geographical (conditional) dependencies in the form ofseparate dependence graphs. We implement our algorithm on data publiclyavailable for the Texas grid as managed by ERCOT, and we confirm that thegeographical dependencies identified by the algorithm are consistent with thegeographical relative locations of the zones over which the data werecollected.

GLASSO Model for Electric Load and Wind Power and Monte Carlo Scenario Generation