Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning

TL;DR

This paper introduces a statistical approach using functional depth metrics to identify extreme scenarios in day-ahead power grid planning, aiming to improve risk mitigation by selecting the most relevant outlying scenarios.

Contribution

It applies and evaluates functional depth measures for scenario screening in high-dimensional probabilistic load and renewable generation data, enhancing operational risk assessment.

Findings

Effective identification of risky scenarios for grid operation.

Improved risk mitigation through targeted scenario selection.

Demonstrated approach on the Texas-7k grid case study.

Abstract

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.