Toward scalable stochastic unit commitment. Part 1: load scenario generation

dc.contributor.author Feng, Yonghan
dc.contributor.author Rios, Ignacio
dc.contributor.author Ryan, Sarah
dc.contributor.author Ryan, Sarah
dc.contributor.author Spurkel, Kai
dc.contributor.author Watson, Jean-Paul
dc.contributor.author Wets, Roger
dc.contributor.author Woodruff, David
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-02-16T09:46:25.000
dc.date.accessioned 2020-06-30T04:47:37Z
dc.date.available 2020-06-30T04:47:37Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2015
dc.date.embargo 2016-04-09
dc.date.issued 2015-04-01
dc.description.abstract <p>Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Tra- ditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load sce- narios, with associated probabilities, that in aggregate capture the range of likely load time-series. The shift from point-based to scenario-based forecasting necessi- tates a shift in forecasting technologies, to provide accurate inputs to stochastic unit commitment. In this paper, we discuss a novel scenario generation method- ology for load forecasting in stochastic unit commitment, with application to real data associated with the Independent System Operator for New England (ISO- NE). The accuracy of the expected scenario generated using our methodology is consistent with that of point forecasting methods. The resulting sets of realistic scenarios serve as input to rigorously test the scalability of stochastic unit com- mitment solvers, as described in the companion paper. The scenarios generated by our method are available as an online supplement to this paper, as part of a novel, publicly available large-scale stochastic unit commitment benchmark.</p>
dc.description.comments <p>This is a manuscript of an article from Energy Systems (2015). The final publication is available at Springer via <a href="http://dx.doi.org/10.1007/s12667-015-0146-8" target="_blank">http://dx.doi.org/10.1007/s12667-015-0146-8</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_pubs/10/
dc.identifier.articleid 1015
dc.identifier.contextkey 7126373
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_pubs/10
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44386
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_pubs/10/2014_RyanSM_TowardScalableStochasticPt.1.pdf|||Fri Jan 14 18:06:11 UTC 2022
dc.source.uri 10.1007/s12667-015-0146-8
dc.subject.disciplines Industrial Engineering
dc.subject.disciplines Systems Engineering
dc.title Toward scalable stochastic unit commitment. Part 1: load scenario generation
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 22d808f1-c309-4cb1-8d3e-14c57a6b96a9
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
File
Original bundle
Now showing 1 - 1 of 1
Name:
2014_RyanSM_TowardScalableStochasticPt.1.pdf
Size:
1.2 MB
Format:
Adobe Portable Document Format
Description:
Collections