Modeling of power demands of electric vehicles in correlated probabilistic load flow studies

Simple item page
dc.contributor.authorBhat, N.G.
dc.contributor.authorPrusty, B.R.
dc.contributor.authorJena, D.
dc.date.accessioned2026-02-06T06:38:45Z
dc.date.issued2017
dc.description.abstractIn this paper, extended cumulant method (ECM) is applied to probabilistic load flow analysis. Input uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. Probability distributions of the result variables such as bus voltages and branch power flows pertaining to these inputs are accurately approximated; and at the same time, multiple input correlation cases are incorporated. The performance of ECM is demonstrated on the modified IEEE 69-bus radial distribution system. The results of ECM are compared with Monte-Carlo simulation. © 2016 IEEE.
dc.identifier.citationIEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2016, 2017, Vol.2016-January, , p. 1-6
dc.identifier.urihttps://doi.org/10.1109/PEDES.2016.7914223
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/31858
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectBattery electric vehicle
dc.subjectExtended cumulant method
dc.subjectPlug-in hybrid electric vehicle
dc.subjectProbabilistic load flow
dc.subjectRadial distribution system
dc.titleModeling of power demands of electric vehicles in correlated probabilistic load flow studies

Files

Collections

Conference Papers