Extreme day-ahead renewables scenario selection in power grid operations

Guillermo Terrén-Serrano and Michael Ludkovski

Applied Energy, 2025, vol. 391, issue C, No S0306261925004775

Abstract: We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios for realized electric load, as well as solar and wind generation in day-ahead grid planning. Our primary motivation is screening probabilistic scenarios to identify those 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, reserve shortfalls, and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

Keywords: Functional depth; Operational planning; Power grids; Renewable energy; Statistical extremality (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125747

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