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Multiobjective optimal network reconfiguration considering the charging load of PHEV

机译:考虑PHEV充电负荷的多目标最佳网络重新配置

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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.
机译:能源危机和环境污染使电动汽车(EV)成为一个热门话题。 EVS对电网的连接为电力公用设施带来了极大的挑战。本文提出了一种基于量子启发二元粒子群算法的多目标网络重新配置方法,其旨在减轻插电混合动力电动车(PHEV)对分配系统的不利影响。模糊集用于处理多目标。该方法涉及两个步骤:在每个负载级别上加载级别划分和网络重新配置。在该分析中考虑了两种不同的PHEV充电模式:不协调的充电和协调充电。 33总线分配系统的仿真结果表明,考虑PHEV的充电负荷,提出了拟议的方法对实现能量损失降低和提高电压质量的良好影响。

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