# Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning

arXiv: https://arxiv.org/abs/2309.11067 · HTML: https://mlsanity.com/paper/2309.11067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Extremal Depth for Functional Data and Applications](https://mlsanity.com/paper/1511.00128/) (2015)
- [Functional Outlier Detection and Taxonomy by Sequential Transformations](https://mlsanity.com/paper/1808.05414/) (2018)
- [Spatial Depth-Based Classification for Functional Data](https://mlsanity.com/paper/1305.2957/) (2013)
- [Outlier Detection for Functional Data with R Package fdaoutlier](https://mlsanity.com/paper/2105.05213/) (2021)
- [Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior](https://mlsanity.com/paper/1509.00051/) (2015)

## Built on by (most cited first, 1 of 1)

- [Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes](https://mlsanity.com/paper/2409.16308/) (2024)

## Abstract (arXiv)

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.