Benefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicente

dc.citation.epage174en_US
dc.citation.spage167en_US
dc.citation.volumeNumber105en_US
dc.contributor.authorYuan, S.en_US
dc.contributor.authorKocaman, A.S.en_US
dc.contributor.authorModi, V.en_US
dc.date.accessioned2018-04-12T11:10:31Z
dc.date.available2018-04-12T11:10:31Z
dc.date.issued2017en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractFor electric grids that rely primarily on liquid fuel based power generation for energy provision, e.g. one or more diesel gensets, measures to allow a larger fraction of intermittent sources can pay-off since the displaced is high cost diesel powered generation. This paper presents a case study of Sao Vicente, located in Cape Verde where a particularly high fraction of wind capacity of 5.950�MW (75% of the average demand) is installed, with diesel gensets forming the dispatchable source of power. This high penetration of intermittent power is managed through conservative forecasting and curtailments. Two potential approaches to reduce curtailments are examined in this paper: 1) an improved wind speed forecasting using a rolling horizon ARIMA model; and 2) energy storage. This case study shows that combining renewable energy forecasting and energy storage is a promising solution which enhances diesel fuel savings as well as enables the isolated grid to further increase the annual renewable energy penetration from the current 30.4% up to 38% while reducing grid unreliability. In general, since renewable energy forecasting ensures more accurate scheduling and energy storage absorbs scheduling error, this solution is applicable to any small size isolated power grid with large renewable energy penetration.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:10:31Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.embargo.release2019-05-01en_US
dc.identifier.doi10.1016/j.renene.2016.12.061en_US
dc.identifier.issn0960-1481
dc.identifier.urihttp://hdl.handle.net/11693/37335
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.renene.2016.12.061en_US
dc.source.titleRenewable energyen_US
dc.subjectARIMA modelen_US
dc.subjectIsolated griden_US
dc.subjectMonte Carlo simulationen_US
dc.subjectRolling horizonen_US
dc.subjectStorageen_US
dc.subjectWind speed forecastingen_US
dc.subjectDiesel fuelsen_US
dc.subjectEnergy storageen_US
dc.subjectForecastingen_US
dc.subjectFuel economyen_US
dc.subjectFuel storageen_US
dc.subjectIntelligent systemsen_US
dc.subjectMonte Carlo methodsen_US
dc.subjectSchedulingen_US
dc.subjectWinden_US
dc.subjectARIMA modelingen_US
dc.subjectElectric gridsen_US
dc.subjectIsolated gridsen_US
dc.subjectLarge wind penetrationsen_US
dc.subjectRenewable energiesen_US
dc.subjectRenewable energy penetrationsen_US
dc.subjectRolling horizonen_US
dc.subjectWind speed forecastingen_US
dc.subjectElectric power transmission networksen_US
dc.subjectadsorptionen_US
dc.subjectcost analysisen_US
dc.subjectdieselen_US
dc.subjectelectricity generationen_US
dc.subjectelectricity supplyen_US
dc.subjectenergy budgeten_US
dc.subjectenergy resourceen_US
dc.subjectforecasting methoden_US
dc.subjectMonte Carlo analysisen_US
dc.subjectnumerical methoden_US
dc.subjectnumerical modelen_US
dc.subjectrenewable resourceen_US
dc.subjectsmart griden_US
dc.subjectwind velocityen_US
dc.subjectCape Verde [Macaronesia]en_US
dc.subjectSao Vicente [Cape Verde]en_US
dc.titleBenefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicenteen_US
dc.typeArticleen_US

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