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A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang-Big Crunch Method

机译:有监督大爆炸法的压控分布式发电系统选型和布置的多目标优化

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摘要

This article presents an efficient multi-objective optimization approach based on the supervised big bang-big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The Math Works, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.
机译:本文提出了一种基于监督大爆炸算法的高效多目标优化方法,用于可调度分布式发电机的最优规划。提出的方法旨在通过优化连接到平衡/不平衡配电网络的分布式发电机的大小和位置来提高系统性能指标。所提出的算法中的分布式发电单元被建模为一个电压控制节点,可以灵活地在无功功率超出限制的情况下转换为恒功率节点。所提出的算法是在MATLAB(美国马萨诸塞州纳蒂克的数学工厂)环境中实现的,并且仿真研究是在IEEE 69总线和IEEE 123节点分布测试系统上进行的。通过将结果与从其他竞争方法获得的公开结果进行比较,来验证所提出方法的有效性,随后的讨论证明了所提出方法的有效性。

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