基于NSGA-II遗传算法的实际径向配电网分布式发电(DG)和静态无功补偿器(SVC)定位的技术经济优化

Oloulade Arouna, Moukengue Imano Adolphe, Adekambi O. Robert, Amoussou Zinsou Kenneth, Vianou Antoine, Badarou Ramanou, Tamadaho Herman, Dangbedji Celestin
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引用次数: 6

摘要

考虑到与能源部门经济组织相关的竞争,网络管理者过度使用他们的能源系统。这会导致过多的损耗,从而导致电源质量的下降。本文通过对分布式发电机(PV)和SVC在138节点HTA出发时的最优定位,对SBEE配电网进行了优化。在NSGA-II算法中考虑并集成了损耗、安装成本和电压偏差等优化准则。该算法得到了一个光伏系统在节点69处功率为1.03MW,两个svc分别在节点58和节点82处功率为2.07和2.05 MVar的最优定位。仿真得到的最优成本为272.9万美元。NSGA-II算法是一种鲁棒性很强的优化工具,效率高,可用于电网优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technico-economic optimization of Distributed Generation (DG) and Static Var Compensator (SVC) positioning in a real radial distribution network using the NSGA-II genetic algorithm
Network manager overuse their energy system considering the competition linked to energy sectors economic organization. That induces excessive losses and consequently the degradation of the quality of the power supply. This work consists of optimizing a distribution network of SBEE through the optimal positioning of distributed generator (PV) and SVC in a 138 node HTA departure. Optimization criteria such as losses, installation costs and voltage deviation have been considered and integrated in the NSGA-II algorithm. The algorithms have led to an optimal positioning of a PV system with 1.03MW of power at node 69 and two SVCs of respective powers of 2.07 MVar at node 58 and 2.05 MVar at node 82 of the network. The optimal cost obtained from the simulation is 2,729,000(USD). The NSGA-II algorithm is then a very robust optimization tool, efficient and can be used to optimize electrical grid.
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