DOE OSTI · 3024842
Performant Optimization Strategies for Multifidelity Stochastic Power Grid Models
Abstract
This talk goes into the algorithmic work done under the Forest project in order to solve expensive power grid models. We explore multiple fidelities of models that balance accuracy and computational expense. We use bundling strategies and progressive hedging in order to parallelize large stochastic programs.
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Alfant, Rachael May [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000296328494), Viens, Matthew Paul [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)], Hart, William Eugene [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000268492780), Skolfield, Joshua Kyle [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000240664376), Kilwein, Zachary Alexander [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000188876629). 2025-03-01. Performant Optimization Strategies for Multifidelity Stochastic Power Grid Models. https://doi.org/10.2172/3024842
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