2012
DOI: 10.1016/j.ijepes.2012.04.011
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Distributed generation planning using differential evolution accounting voltage stability consideration

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Cited by 131 publications
(53 citation statements)
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“…Moreover, a mutation is considered as a random selection of an allele from an individual, which is modified. The literature shows some applications of this metaheuristic to power system planning, such as the integration of renewable resources in smart grids [27], the location and size of DG [28][29][30][31][32][33], and renewable DG [34,35]. In this research, we used the algorithm to find the location and size of photovoltaic panels, wind turbines, and capacitors.…”
Section: Genetic Algorithmmentioning
confidence: 99%
“…Moreover, a mutation is considered as a random selection of an allele from an individual, which is modified. The literature shows some applications of this metaheuristic to power system planning, such as the integration of renewable resources in smart grids [27], the location and size of DG [28][29][30][31][32][33], and renewable DG [34,35]. In this research, we used the algorithm to find the location and size of photovoltaic panels, wind turbines, and capacitors.…”
Section: Genetic Algorithmmentioning
confidence: 99%
“…The Evolutionary algorithms play an important role in placement problems, as it is easy to consider more constraints and fast solution. [3,4]. The charateristics of the uncertain nature of the renewable energy based power plant is used to create the constraints of the optimization using Stochastic programming (SP) [7]- [9] and robust optimization (RO) [10]- [15] is included in the expansion planning problem.…”
Section: Introductionmentioning
confidence: 99%
“…Several analytical approaches minimizing line losses are proposed for the DG allocation [5][6][7][8][9][10] and optimal power flow [11,12]. For the same purpose of DG allocation, an evolutionary algorithm (EA) uses genetic algorithm and an ε-constrained method [13] and other heuristic algorithm methods through harmony search algorithm [14], particle swarm optimization [15,16], artificial bee colony algorithm [17], and differential evolution (DE) [18] and so on have been applied to sit single and/or multi-DGs for various objectives. In addition, the reader can be referred to [19,20], in which very comprehensive reviews covering the available DG placement models and various approaches with satisfactory classifications of researches are covered.…”
Section: Introductionmentioning
confidence: 99%