  We propose and analyze the application of statistical functional depth
metrics for the selection of extreme scenarios in day-ahead grid planning. Our
primary motivation is screening of probabilistic scenarios for realized load
and renewable generation, in order to identify scenarios most relevant for
operational risk mitigation. To handle the high-dimensionality of the scenarios
across asset classes and intra-day periods, we employ functional measures of
depth to sub-select outlying scenarios that are most likely to be the riskiest
for the grid operation. We investigate a range of functional depth measures, as
well as a range of operational risks, including load shedding, operational
costs, reserves shortfall and variable renewable energy curtailment. The
effectiveness of the proposed screening approach is demonstrated through a case
study on the realistic Texas-7k grid.
