2016
DOI: 10.1016/j.rser.2015.12.204
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Genetic algorithm for impact assessment of optimally placed distributed generations with different load models from minimum total MVA intake viewpoint of main substation

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Cited by 31 publications
(8 citation statements)
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“…By considering the impacts of an unconstrained increase in RES-DG penetration into the grid, as discussed in the previous section, it is important to optimally allocate the appropriate RES-DG type. The impact of capacity, types, and operating power factors of different RES-DGs on the grid was investigated in [72]. Analytical approaches based on loss sensitivity factor, voltage stability factor, and selection index for optimal RES-DG allocation are common in recent literature.…”
Section: Active Distribution Network Planningmentioning
confidence: 99%
“…By considering the impacts of an unconstrained increase in RES-DG penetration into the grid, as discussed in the previous section, it is important to optimally allocate the appropriate RES-DG type. The impact of capacity, types, and operating power factors of different RES-DGs on the grid was investigated in [72]. Analytical approaches based on loss sensitivity factor, voltage stability factor, and selection index for optimal RES-DG allocation are common in recent literature.…”
Section: Active Distribution Network Planningmentioning
confidence: 99%
“…Among these technical indicators, power loss [272], short circuit current capacity [273], voltage stability [274] and power system transient stability [275] are more relevant to DG distribution (i.e. topography of DG-based generators and their injection points to power grid), microgrid power management and detailed DG expansion planning [276].…”
Section: Optimisation Criteriamentioning
confidence: 99%
“…real and reactive power loss expressions [273], squared difference-based voltage deviation [333], and convex thermal system cost function [334]. These approaches involve rapid convergence speed in spite of large number of variables involved in DG placement for different test bus systems and multiple scenario simulations for comprehensive analyses.…”
Section: Mathematical Modelling Optimisation Techniquesmentioning
confidence: 99%
“…Genetic algorithm is a stochastic global adaptive search optimization technique which works as a population containing a number of chromosomes and an objective function is used for each chromosome to find a solution to the problem [38,104,105]. The architecture of the GA is shown in Figure 11.…”
Section: Description Of Optimization Algorithms 411 Genetic Algorithmmentioning
confidence: 99%