A gradient-based framework that jointly calibrates wind forecast models and the size of a distributionally robust ambiguity set to minimize two-stage power market costs.
Vulnerability and impact of machine learning-based inertia forecasting under cost-oriented data integrity attack,
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Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach
A gradient-based framework that jointly calibrates wind forecast models and the size of a distributionally robust ambiguity set to minimize two-stage power market costs.