Swarm intelligence based multi-phase OPF for peak power loss reduction in a smart grid

A. Anwar, A. Mahmood
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引用次数: 3

Abstract

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.
基于群智能的多相OPF算法在智能电网中的峰值损耗降低
最近,人们对利用计算智能提高智能电网的效率越来越感兴趣。未来智能电网面临的一个关键挑战是设计最优潮流工具来解决包括最优DG容量在内的重要规划问题。虽然针对平衡配电网已有许多OPF工具,但针对不平衡多相配电网的OPF工具研究较少。本文提出了一种新的OPF技术,用于智能电网的DG容量规划。在该算法的制定过程中,考虑了具有不平衡负载、电压控制和无功补偿装置的多相配电系统。该算法建立在一个联合仿真框架上,该框架采用收缩因子粒子群优化方法对目标进行优化。在IEEE 8500节点基准分配系统中验证了所提出的多相OPF技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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