A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorEkström, Jussien_US
dc.contributor.authorKoivisto, Mattien_US
dc.contributor.authorMellin, Ilkkaen_US
dc.contributor.authorMillar, Roberten_US
dc.contributor.authorLehtonen, Mattien_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.departmentDepartment of Mathematics and Systems Analysisen
dc.contributor.groupauthorPower Systems and High Voltage Engineeringen
dc.date.accessioned2018-09-21T09:49:53Z
dc.date.available2018-09-21T09:49:53Z
dc.date.issued2018-09-14en_US
dc.description.abstractIn future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.en
dc.description.versionPeer revieweden
dc.format.extent18
dc.format.extent1-18
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationEkström, J, Koivisto, M, Mellin, I, Millar, R & Lehtonen, M 2018, ' A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations ', Energies, vol. 11, no. 9, 2442 . https://doi.org/10.3390/en11092442en
dc.identifier.doi10.3390/en11092442en_US
dc.identifier.issn1996-1073
dc.identifier.otherPURE UUID: e5d4c0ba-eedd-4048-8db3-b21f07c5617cen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/e5d4c0ba-eedd-4048-8db3-b21f07c5617cen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/27969859/energies_11_02442.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/34063
dc.identifier.urnURN:NBN:fi:aalto-201809215158
dc.language.isoenen
dc.relation.ispartofseriesEnergiesen
dc.relation.ispartofseriesVolume 2018, issue 11en
dc.rightsopenAccessen
dc.subject.keywordMonte Carlo simulationen_US
dc.subject.keywordpower rampsen_US
dc.subject.keywordrenewable energyen_US
dc.subject.keywordvector autoregressive modelen_US
dc.subject.keywordwind power generationen_US
dc.titleA Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locationsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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