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A multi-objective problems for optimal integration of the DG to the grid using the NSGA-II

机译:使用NSGA-II将DG最佳集成到网格的多目标问题

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In recent years, integration of a wide variety of Distributed Generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. In this paper, one type of the DG i.e. Wind Turbine is optimally integrated in a power network for enhancing the performance of the network. A new variant of Genetic Algorithm (GA) dedicated in multi-objective optimization problems known as Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been proposed for accomplishing the same. To aid the decision maker choosing the best compromise solutions from the Pareto front, the fuzzy-based mechanism is employed for this task. The NSGA-II is used to obtain the optimal integration and sizing of the DG in a suitable load bus of the system. Multi-objective functions are considered as the indices of the system performance viz: maximization of system loadability in system security and stability margin i.e. voltage and line limit whereas minimization of the real power loss of the transmission lines. Simulation studies are undertaken on modified IEEE 14-bus and a practical Indonesia Java-Bali 24-bus systems. Results show that the dynamic performance of the power system can be effectively improved by the optimal integration and sizing of the DG.
机译:近年来,在配电网络中集成各种分布式发电(DG)技术已成为专业工程师的主要管理问题之一。在本文中,一种类型的DG(即风力涡轮机)可以最佳地集成到电力网络中,以增强网络的性能。为此,提出了一种专门用于多目标优化问题的遗传算法(GA)的新变体,称为非支配排序遗传算法II(NSGA-II)。为了帮助决策者从Pareto方面选择最佳折衷解决方案,该任务采用了基于模糊的机制。 NSGA-II用于在系统的合适负载总线中获得DG的最佳集成和大小。多目标函数被认为是系统性能的指标,即:在系统安全性和稳定性裕度(即电压和线路限制)方面使系统负载能力最大化,而在传输线路上的实际功率损耗最小化。在改进的IEEE 14总线和实用的Indonesia Java-Bali 24总线系统上进行了仿真研究。结果表明,通过优化DG的集成度和大小可以有效地改善电力系统的动态性能。

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