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Multi-Objective Load Scheduling in a Smart Grid Environment

机译:智能电网环境下的多目标负荷调度

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Smart grid is a remarkable development for managing the existing grids more efficiently. This paper deals with an integration of distributed energy resources and plug-in electric vehicles (PEVs) into an existing grid. There are significant impacts due to PEVs in the existing grid. However they also bring negative impacts to the grid if they are not coordinated properly. The continuous varying load and voltage fluctuations caused by their disordered charging behaviour can be detrimental to the grid. In order to overcome them, an intelligent load-scheduling strategy is applied in this paper. A multi-objective optimization strategy based on non-dominated sorting genetic algorithm (NSGA-II) is used in this paper to minimize two contradicting objective functions such as voltage deviation at buses and the total line loss simultaneously. The applied method is tested on IEEE 17-bus test system. Simulation results show the superiority of the applied method.
机译:智能电网是一项显着的发展,可以更有效地管理现有电网。本文致力于将分布式能源和插电式电动汽车(PEV)集成到现有电网中。现有电网中的PEV会产生重大影响。但是,如果协调不当,它们也会对网格带来负面影响。由无序充电行为引起的连续变化的负载和电压波动可能对电网有害。为了克服它们,本文采用了一种智能的负荷调度策略。本文采用基于非支配排序遗传算法(NSGA-II)的多目标优化策略,以同时最小化两个相互矛盾的目标函数,例如母线电压偏差和总线损。该方法在IEEE 17总线测试系统上进行了测试。仿真结果表明了该方法的优越性。

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