A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

de Faria Jr., Haroldo; Resende, Mauricio; Ernst, Damien

doi:10.1007/s10732-017-9355-8

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A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

de Faria Jr., Haroldo; Resende, Mauricio; Ernst, Damien

2017 • In Journal of Heuristics

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/213622

DOI
10.1007/s10732-017-9355-8

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Keywords :

Distribution network reconfiguration; iased random-key genetic algorithms; Optimization; Power losses

Abstract :

[en] This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

Disciplines :

Energy

Author, co-author :

de Faria Jr., Haroldo

Resende, Mauricio

Ernst, Damien ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids

Language :

English

Title :

A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

Publication date :

2017

Journal title :

Journal of Heuristics

ISSN :

1381-1231

eISSN :

1572-9397

Publisher :

Springer Science & Business Media B.V., Dordrecht, Netherlands

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://link.springer.com/journal/10732

Available on ORBi :

since 17 August 2017

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