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Determining the optimal placement and capacity of DG in intelligent distribution networks under uncertainty demands by COA

机译:COA在不确定性需求下确定DG的最佳放置和能力

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Given the cost of traditional methods such as the construction of new posts and feeders to enhance the electricity network, tendency toward the use of modern intelligent technologies has recently grown up. Hence the overall structure of the subject studied in this paper is to create distributed generation resources with optimal capacity in appropriate parts of the distribution network. Since the set is intelligent, considering uncertainty in network loads is a new idea proposed by the authors in network simulation. Monte Carlo technique has been used to implement this idea. Cuckoo optimization algorithm (COA) which has recently been embraced by researchers is the proposed method in this paper for finding the most optimal state governing the network. Evaluated units in this study are distributed generation resources of biomass and solar thermal. The effects of implementing the above methods to improve the voltage profile and reduce losses have been investigated in a separate section of this article. Finally, the economic savings achieved in these two units for implementing the proposed methods were evaluated, and the cost reduction has been expressed.
机译:鉴于传统方法的成本,如新岗位和饲养者的建设,以增强电网,倾向于使用现代智能技术最近长大。因此,本文研究的主题的整体结构是在配送网络的适当部分中创建具有最佳容量的分布式发电资源。由于该组是智能的,考虑到网络负载的不确定性是网络仿真中提出的新想法。 Monte Carlo技术已被用来实现这个想法。杜鹃优化算法(COA)最近被研究人员接受的是本文提出的方法,用于寻找网络的最佳状态。本研究中的评估单位是生物质和太阳能热量的分布发电资源。在本文的单独部分中研究了实施上述方法以改善电压曲线和减少损耗的影响。最后,评估了这两个用于实施所提出的方法的两种单元中实现的经济储蓄,并表达了成本降低。

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