Accelerating Stochastic Unit Commitment with Multifidelity Scenario Bundling
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| Publicado en: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 1-6 |
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| Otros Autores: | , |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Acceso en línea: | Citation/Abstract |
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| Resumen: | Conference Title: 2025 57th North American Power Symposium (NAPS)Conference Start Date: 2025 Oct. 26Conference End Date: 2025 Oct. 28Conference Location: Storrs, CT, USADay-ahead commitment of generators, or unit commitment, is crucial for operating power grids. The problem of unit commitment can be modeled as a stochastic program, for which the uncertainties typically manifest in loads or renewable generation. However, stochastic unit commitment tends to be computationally challenging and therefore expensive to solve. In this paper, we present a modified progressive hedging algorithm that utilizes multifidelity modeling to rapidly generate high-quality solutions for large-scale stochastic unit commitment problems. Our methodology allows for the decomposition of the main problem into subproblems that bundle multiple high and low fidelity scenarios. This form of multi-fidelity bundling enables information sharing across subproblems that accelerates progressive hedging. We demonstrate the efficacy of our multifidelity scenario bundling approach on a RTS-GMLC unit commitment exemplar. |
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| DOI: | 10.1109/NAPS66256.2025.11272380 |
| Fuente: | Science Database |