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

机译:确定COA不确定性需求下智能配电网中DG的最佳放置和容量

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摘要

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:考虑到传统方法的成本,例如建设新的接线柱和馈线以增强电力网络的成本,近来发展了使用现代智能技术的趋势。因此,本文研究的主题的总体结构是在配电网的适当部分中创建具有最佳容量的分布式发电资源。由于该集合是智能的,因此考虑网络负载的不确定性是作者在网络仿真中提出的新思路。蒙特卡罗技术已被用来实现这个想法。研究人员最近采用的布谷鸟优化算法(COA)是本文提出的用于找到控制网络的最佳状态的方法。在这项研究中,评估的单位是生物质和太阳热能的分布式发电资源。在本文的单独部分中,已经研究了采用上述方法来改善电压分布并减少损耗的效果。最后,评估了在这两个部门中为实施所提出的方法所实现的经济节省,并表示了降低的成本。

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