DocumentCode
678238
Title
Security enhancement with nodal criticality based integration of PHEV micro grids
Author
Jayaweera, Dilan ; Islam, Shariful
Author_Institution
Dept. of Electr. & Comput. Eng., Curtin Univ., Perth, WA, Australia
fYear
2013
fDate
Sept. 29 2013-Oct. 3 2013
Firstpage
1
Lastpage
6
Abstract
Modern distribution networks are increasingly vulnerable to disturbances and improving the security of supply to customers are complex and challenging with the traditional approach. This paper presents a new approach to enhance the security of power supply in an active distribution network by integrating PHEV (Plug-in Hybrid Electric Vehicle) based micro grids on the basis of the nodal criticality. The nodal criticality is assessed by integrating operational uncertainties of events into samples of Monte Carlo simulation and classifying load interruptions on the basis of their magnitudes and frequencies. Criticality of the system stress that results nodal loads shedding is classified into arrays of clusters based on the magnitudes of interrupted loads at samples. The critical clusters that represent largest disturbances to the respective nodal loads are served with PHEV micro grids. Case studies are performed, and the results suggest that the security of distribution networks can be significantly improved with the proposed approach.
Keywords
Monte Carlo methods; distributed power generation; hybrid electric vehicles; load shedding; power distribution planning; power system security; Monte Carlo simulation; PHEV micro grids; active distribution network; grid integration; load interruptions; nodal criticality; nodal loads shedding; plug-in hybrid electric vehicle; power supply security enhancement; Generators; Monte Carlo simulation; distribution network operational planning; grid integration; security of power supply;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Engineering Conference (AUPEC), 2013 Australasian Universities
Conference_Location
Hobart, TAS
Type
conf
DOI
10.1109/AUPEC.2013.6725365
Filename
6725365
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