Grey wolf optimisation algorithm for solving distribution network reconfiguration considering distributed generators simultaneously

This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is util...

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Veröffentlicht in:International journal of sustainable energy Jg. 41; H. 11; S. 2121 - 2149
Hauptverfasser: Pujari, Harish Kumar, Rudramoorthy, Mageshvaran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 01.12.2022
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Algorithms
Combinatorial analysis
Distributed generation
Distributed generations
Electric potential
Generators
grey wolf optimisation algorithm
Heuristic methods
loss reduction
network reconfiguration
Optimization
radial distribution systems
Reconfiguration
Voltage
voltage profile
ISSN:1478-6451, 1478-646X
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Zusammenfassung:This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is utilised to minimise the active power losses (APL) and enhance the voltage profile. Various scenarios were considered in this context to compare the performance of the proposed algorithm under voltage and current capacity constraints. Furthermore, a detailed validation via comparison of the results is being carried out with other methods from the exhaustive literature. The proposed algorithm reduces the APL by 63.13%, 56.19%, and 34.27% with DNR in IEEE 33, 69 and 118-bus systems. Similarly, APL reduction by 69.61%, 82.09%, and 36.08% with DNR considering DGs simultaneously. The results show that the proposed algorithm is an effective and promising method to solve problems similar to this work.
Bibliographie:ObjectType-Article-1
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ISSN:1478-6451
1478-646X
DOI:10.1080/14786451.2022.2134383