Year: 2025
Type: article
Abstract: Value-oriented forecasts for two-stage power system operational problems have been demonstrated to reduce cost, but prove to be computationally challenging for large-scale systems because the underlying optimization problem must be internalized into the forecast model training. Therefore, existing approaches typically scale poorly in the usable training data or require relaxations of the underlying optimization. This paper presents a method for value-oriented forecast combinations using progressive hedging, which unlocks high-fidelity, at-scale models and large-scale datasets in training. We also derive one-shot training model for reference and study how different modifications of the training model impact the solution quality.
Source: IEEE Control Systems Letters
Authors: Mehrnoush Ghazanfariharandi, Robert Mieth
Institution: Rutgers, The State University of New Jersey
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411270851