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Smart grid reconfiguration using simple genetic algorithm and NSGA-II

机译:使用简单的遗传算法和NSGA-II重新配置智能网格重新配置

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Increased penetration of distributed generators (DGs) is one of the characteristics of smart grids. Distribution grid reconfiguration is one of the methods of accommodating more DG into the electric grid, which is illustrated with the help of a 16 node test network in this paper. The reconfiguration of the distribution grid involves changing the grid topology thereby optimizing a few objectives. In addition to the inclusion of DGs, grid reconfiguration also helps in achieving minimal power loss, minimal voltage deviation etc. In this paper the grid reconfiguration problem is formulated as an optimization problem. Simple genetic algorithm (GA) and its variant NSGA-II are used for solving the optimization problem. For a simple test system like the 16 node system discussed in this paper, simple GA is efficient enough to find the global optimum for a single objective optimization. The paper also illustrates the advantage of NSGA-II compared to simple GA when multiple objectives are considered.
机译:增加分布式发电机(DGS)的渗透是智能电网的特征之一。分布网格重新配置是将更多DG的方法是电网的一种方法之一,这在本文中提供了16个节点测试网络的帮助。分布网格的重新配置涉及改变网格拓扑,从而优化一些目标。除了包含DGS之外,网格重新配置还有助于实现最小的功率损耗,最小电压偏差等。在本文中,网格重新配置问题被制定为优化问题。简单的遗传算法(GA)及其变型NSGA-II用于解决优化问题。对于本文讨论的16节点系统,简单的测试系统,简单的GA能够足够高,以找到单个客观优化的全局最优。本文还示出了与考虑多个目标时与简单GA相比的NSGA-II的优点。

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