Reconfiguration of deregulated distribution network for minimizing energy supply cost by using BGA

Kargar, Hossein Kazemi; Jalilzadeh, S.; Rezazadeh, Alireza; Nabi, V.; Govar, G.Z.

doi:10.1109/upec.2008.4651545

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…While solving multiobjective optimization problems, it is necessary to pay attention to convergence to optimal set of solutions (Pareto set) and maintain diversity of solutions within the set of current solutions [14]. Evolution algorithms, among which are genetic algorithms, have provided very good results in reconfiguration of distribution networks [1][2][3][4].…”

Section: Methods To Solve Multiobjective Network Reconfiguration Problemmentioning

confidence: 99%

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Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Nuhanovic¹,

Hivziefendić²,

Hadžimehmedović³

2013

Journal of Electrical Engineering

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This paper discusses the problem of finding the optimal network topological configuration by changing the feeder status. The reconfiguration problem is considered as a multiobjective problem aiming to minimize power losses and total interruptions costs subject to the system constraints: the network radiality voltage limits and feeder capability limits. Due to its complexity, the metaheuristic methods can be applied to solve the problem and often the choice is genetic algorithm. NSGA II is used to solve the multiobjective optimization problem in order to get Pareto optimal set with possible solutions. The proposed method has been tested on real 35 kV distribution network. The numerical results are presented to illustrate the feasibility of the proposed genetic algorithm.

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Section: Methods To Solve Multiobjective Network Reconfiguration Problemmentioning

confidence: 99%

“…Over the recent years, reconfiguration has been addressed through stochastic, that is, metaheuristic methods, such are the methods based on simulated annealing algorithms [7], fuzzy logics [5], tabu search [12], ant colony [11], and genetic algorithms [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Nuhanovic¹,

Hivziefendić²,

Hadžimehmedović³

2013

Journal of Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…The primary contrast between these networks is the presence of numerous vitality merchants with different conditions for offering energy in a focused cost [18].…”

Section: Introductionmentioning

confidence: 99%

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

Kaur

Ghosh

2016

Applied Soft Computing

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Reconfiguration of deregulated distribution network for minimizing energy supply cost by using BGA

Cited by 2 publications

References 9 publications

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Distribution Network Reconfiguration Considering Power Losses and Outages Costs Using Genetic Algorithm

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

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