| Description |
1 online resource (20 pages) : color illustrations, color maps. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/PR ; 2C00-80357 |
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NREL/PR ; 2C00-80357.
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| Note |
Slideshow presentation. |
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"FERC Software Conference, 23 June 2021." |
| Bibliography |
Includes bibliographical references. |
| Funding |
DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed September 10, 2021). |
| Summary |
Recent advances in deterministic unit commitment, both formulaic and algorithmic, along with modern algorithmic approaches for stochastic programming, have enabled the solution of stochastic unit commitment problems with hundreds of scenarios on large-scale transmission networks. In this presentation, we will give an overview of these methods, including lazy transmission constraint generation, lower-bounding techniques, and heuristics, all of which can be executed in concert with customized decomposition approaches for optimization under uncertainty. We demonstrate the effectiveness of these techniques on the TAMU Texas7K synthetic transmission network, leveraging realistic high-resolution forecasts based on NREL renewable resource availability data. The software leveraged for these demonstrations is available via the open-source software packages EGRET (for electrical grid optimization) and mpi-sppy (for optimization under uncertainty). |
| Subject |
Stochastic analysis.
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Electric power distribution.
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Analyse stochastique.
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Electric power distribution. (OCoLC)fst00905420
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Stochastic analysis. (OCoLC)fst01133499
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| Indexed Term |
decomposition |
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stochastic optimization |
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unit commitment |
| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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United States. Advanced Research Projects Agency-Energy, sponsoring body.
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| Standard No. |
1804443 OSTI ID |
| Gpo Item No. |
0430-P-09 (online) |
| Sudoc No. |
E 9.22:NREL/PR-2 C 00-80357 |
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