Classified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind power

Simple item page
dc.contributor.authorDalton, Amarisen_ZA
dc.contributor.authorBekker, Bernarden_ZA
dc.contributor.authorKoivisto, Matti Juhanien_ZA
dc.date.accessioned2021-07-27T08:00:51Z
dc.date.available2021-07-27T08:00:51Z
dc.date.issued2021
dc.descriptionCITATION: Dalton, A, Bekker, B. & Koivisto, M. J. 2021. Classified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind power. Energy Reports, 7:3775-3784, doi:10.1016/j.egyr.2021.06.060.
dc.descriptionThe original publication is available at https://www.sciencedirect.com
dc.descriptionPublication of this article was funded by the Stellenbosch University Open Access Fund
dc.description.abstractENGLISH ABSTRACT: Large-scale atmospheric circulation patterns are the primary drivers of wind power variability on power networks at timescales of hours to days. This paper proposes a methodology that allows power system operators and planners working on networks with high levels of wind generation, to conduct probabilistic power flow (PPF) analyses by defining network ‘operating scenarios’ – i.e. the probability density functions of generators, and correlations between generators representative of a future system state – based on concurrent classified atmospheric states. The most significant contribution made by this paper is in illustrating how PPF operating scenarios derived from clustering historic generation data as a function of a set of classified atmospheric states reduces simulation uncertainty within a PPF analysis. It is anticipated that the proposed methodology may provide network planners with more appropriate operating scenarios for PPF analyses when compared to an unclustered base state, and may assist network operators in converting wind power point-forecasts into probabilistic forecasts whereby the spatial correlations between generators are incorporated. This methodology is illustrated through a case study considering 11 geographically disperse wind generators on the South African transmission network.en_ZA
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S235248472100425X
dc.description.versionPublisher's version
dc.format.extent10 pagesen_ZA
dc.identifier.citationDalton, A, Bekker, B. & Koivisto, M. J. 2021. Classified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind power. Energy Reports, 7:3775-3784, doi:10.1016/j.egyr.2021.06.060
dc.identifier.issn2352-4847 (online)
dc.identifier.otherdoi:10.1016/j.egyr.2021.06.060
dc.identifier.urihttp://hdl.handle.net/10019.1/110772
dc.language.isoen_ZAen_ZA
dc.publisherElsevieren_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectWind poweren_ZA
dc.subjectPower transmissionen_ZA
dc.subjectPower flow analysisen_ZA
dc.subjectSystem simulationen_ZA
dc.subjectEngineering meteorologyen_ZA
dc.titleClassified atmospheric states as operating scenarios in probabilistic power flow analysis for networks with high levels of wind poweren_ZA
dc.typeArticleen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
dalton_classified_2021.pdf
Size:
3.99 MB
Format:
Adobe Portable Document Format
Description:
Download article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Articles (Electrical and Electronic Engineering)