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dc.contributor.authorMathew, Manuel Sathyajith
dc.contributor.authorKolhe, Mohan Lal
dc.contributor.authorKandukuri, Surya Teja
dc.contributor.authorOmlin, Christian Walter Peter
dc.date.accessioned2024-05-27T11:29:48Z
dc.date.available2024-05-27T11:29:48Z
dc.date.created2023-08-29T13:17:36Z
dc.date.issued2023
dc.identifier.citationMathew, M. S., Kolhe, M. L., Kandukuri, S. T. & Omlin, C. W. P. (2023). Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles. Journal of Cleaner Production, 421, Article 138467.en_US
dc.identifier.issn1879-1786
dc.identifier.urihttps://hdl.handle.net/11250/3131527
dc.description.abstractWith the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectElektriske kjøretøyeren_US
dc.subjectElectrical vehiclesen_US
dc.titleData driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehiclesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 The Author(s)en_US
dc.subject.nsiVDP::Miljøteknologi: 610en_US
dc.subject.nsiVDP::Environmental engineering: 610en_US
dc.source.volume421en_US
dc.source.journalJournal of Cleaner Productionen_US
dc.identifier.doihttps://doi.org/10.1016/j.jclepro.2023.138467
dc.identifier.cristin2170582
dc.source.articlenumber138467en_US
cristin.qualitycode2


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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal