Operational Risk Financialization of Electricity Under Stochasticity
We introduced a methodology that leverages statistical functional depth metrics to identify the most operationally risky scenarios-those likely to result in high generation costs, reserve shortfalls, load shedding, or renewable curtailment. You can find more information in our publication (Terrén-Serrano & Ludkovski, 2025).
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.
@article{TERRENSERRANO2025125747,title={Extreme day-ahead renewables scenario selection in power grid operations},journal={Applied Energy},volume={391},pages={125747},year={2025},issn={0306-2619},doi={https://doi.org/10.1016/j.apenergy.2025.125747},author={Terrén-Serrano, Guillermo and Ludkovski, Michael},keywords={Functional depth, Operational planning, Power grids, Renewable energy, Statistical extremality},}
