Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/8986
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBhat, N.G.
dc.contributor.authorRajanarayan, Prusty, B.
dc.contributor.authorJena, D.
dc.date.accessioned2020-03-30T10:23:12Z-
dc.date.available2020-03-30T10:23:12Z-
dc.date.issued2017
dc.identifier.citationIEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2016, 2017, Vol.2016-January, , pp.1-6en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8986-
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.en_US
dc.titleModeling of power demands of electric vehicles in correlated probabilistic load flow studiesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.