负荷模型对配电系统分布式发电优化规划的影响

Aashish Kumar Bohre, G. Agnihotri, Manisha Dubey, S. Kalambe
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引用次数: 0

摘要

本文采用蝴蝶-粒子群算法和bf -粒子群算法研究了不同负荷模型对分布式发电机组的影响,并对分布式发电机组进行了最优规划(规模和选址)。通过与遗传算法(GA)和标准粒子群算法(PSO)的比较,验证了评价结果的有效性。为了展示其在负荷管理方面的兼容性,本工作还介绍了不同负荷模型对DG的大小和位置的影响。所探索的适应度进化函数是基于有功损耗、无功损耗和电压偏差指标三个重要指标的多目标函数(FMO)。利用BF-PSO、遗传算法和粒子群算法对多目标适应度函数进行最小化,得到最优解。针对恒负荷、工业负荷、住宅负荷和商业负荷等不同类型的负荷模型,比较了不同的优化技术。结果清楚地表明,BF-PSO技术在兼容性、计算时间和工作量方面都具有优越的解决方案。该算法在15总线径向和30总线网格系统中进行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System
The optimal planning (sizing and siting) of the distributed generations (DGs) by using butterfly-PSO/BF-PSO technique to investigate the impacts of load models is presented in this work. The validity of the evaluated results is confirmed by comparing with well-known Genetic Algorithm (GA) and standard or conventional particle swarm optimization (PSO). To exhibit its compatibility in terms of load management, an impact of different load models on the size and location of DG has also been presented in this work. The fitness evolution function explored is the multiobjective function (FMO), which is based on the three significant indexes such as active power loss, reactive power loss, and voltage deviation index. The optimal solution is obtained by minimizing the multiobjective fitness function using BF-PSO, GA, and PSO technique. The comparison of the different optimization techniques is given for the different types of load models such as constant, industrial, residential, and commercial load models. The results clearly show that the BF-PSO technique presents the superior solution in terms of compatibility as well as computation time and efforts both. The algorithm has been carried out with 15-bus radial and 30-bus mesh system.
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