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Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

机译:负荷模型对配电系统分布式发电优化规划的影响

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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.
机译:通过蝴蝶-PSO / BF-PSO技术研究负荷模型的影响,提出了分布式发电(DG)的最佳计划(规模和选址)。通过与众所周知的遗传算法(GA)和标准或常规粒子群优化(PSO)进行比较,可以确定评估结果的有效性。为了展示其在负载管理方面的兼容性,这项工作还提出了不同负载模型对DG大小和位置的影响。探索的适应性进化函数是多目标函数(FMO),它基于三个重要指标,例如有功功率损耗,无功功率损耗和电压偏差指数。通过使用BF-PSO,GA和PSO技术最小化多目标适应度函数来获得最佳解决方案。针对不同类型的负载模型(例如恒定,工业,住宅和商业负载模型),给出了不同优化技术的比较。结果清楚地表明,BF-PSO技术在兼容性以及计算时间和工作量两方面都提供了出色的解决方案。该算法已在15总线径向和30总线网格系统中执行。

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