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Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System
Author(s) -
Aashish Kumar Bohre,
Ganga Agnihotri,
Manisha Dubey,
Shilpa Kalambe
Publication year - 2015
Publication title -
advances in artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 1687-7489
pISSN - 1687-7470
DOI - 10.1155/2015/297436
Subject(s) - particle swarm optimization , sizing , mathematical optimization , computer science , fitness function , computation , genetic algorithm , electric power system , ac power , voltage , power (physics) , algorithm , mathematics , visual arts , art , physics , quantum mechanics
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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