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Multi population genetic algorithm for allocation and sizing of distributed generation

机译:分布式发电分配与规模确定的多种群遗传算法

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摘要

Distributed generation has been becoming more well-known in the power sector due to its ability in power loss reduction, low investment cost, increase reliability, and most significantly, to exploit renewable-energy resources. The optimal placement and sizing of distributed generation are necessary for maximizing the distributed generation potential benefits in a power system. In this paper, a novel multi population-based genetic algorithm is proposed for optimal location and sizing of distributed generation in a radial distribution system. The objective is to minimize the total real power losses in the system and improve voltage stability within the voltage constrains. Both the optimal size and location are obtained as outputs from the genetic algorithm toolbox. An analysis is carried out on 30 bus systems and compare with the analytical method and standard genetic algorithm to verify the effectiveness of the proposed methodology. Results show that the proposed method is more efficient in power losses reduction compared to analytical method, also faster in convergence than standard genetic algorithm.
机译:由于分布式发电具有降低功率损耗,降低投资成本,提高可靠性以及最重要的是利用可再生能源的能力,因此在电力领域已变得越来越知名。为了使电力系统中的分布式发电潜在利益最大化,必须对分布式发电进行优化布置和确定尺寸。在本文中,提出了一种新颖的基于多种群的遗传算法,用于径向分布系统中分布式发电的最佳位置和规模。目的是使系统中的总实际有功损耗最小化,并改善电压约束内的电压稳定性。最佳大小和位置都可以从遗传算法工具箱中获得。在30个公交系统上进行了分析,并与分析方法和标准遗传算法进行了比较,以验证所提出方法的有效性。结果表明,与分析方法相比,该方法在降低功率损耗方面更加有效,并且在收敛速度方面也比标准遗传算法更快。

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