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.
Task-based end-to-end model learning in stochastic optimization,
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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.