Multiobjective optimal network reconfiguration considering the charging load of PHEV

Energy crisis and environmental pollution make the electric vehicle (EV) become a hot topic. The connection of EVs to the power grid brings great challenges to the electric utilities. This paper proposes a multiobjective network reconfiguration methodology based on quantum-inspired binary particle s...

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Bibliographic Details
Published in2012 IEEE Power and Energy Society General Meeting pp. 1 - 8
Main Authors Gaowang Li, Dongyuan Shi, Xianzhong Duan, Huijie Li, Meiqi Yao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Convergence
Distribution network
Encoding
Energy loss
fuzzy sets
Optimization
Particle swarm optimization
plug-in hybrid electric vehicle
Quantum-inspired binary particle swarm optimization
Reliability
Security
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Summary:Energy crisis and environmental pollution make the electric vehicle (EV) become a hot topic. The connection of EVs to the power grid brings great challenges to the electric utilities. This paper proposes a multiobjective network reconfiguration methodology based on quantum-inspired binary particle swarm algorithm that aims at alleviating the adverse impact of plug-in hybrid electric vehicle (PHEV) on distribution system. The fuzzy sets are used to handle the multiobjective. The methodology involves two steps: load level division and network reconfiguration on each load level. Two different charging patterns of PHEV are considered in this analysis: uncoordinated charging and coordinated charging. The simulation results of a 33-bus distribution system show that the proposed methodology has a good effect on achieving the energy loss reduction and improving the voltage quality considering the charging load of PHEV.
ISBN:1467327271
9781467327275
ISSN:1932-5517
DOI:10.1109/PESGM.2012.6343963