Multi-objective distribution network reconfiguration optimization problem

This paper dealt with the optimal reconfiguration of a power distribution network where a genetic algorithm (GA) based on graph theory was considered. This paper addressed this issue as a multi-criteria one with low investment cost and reduced active power losses objectives. The topological and volt...

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Bibliographic Details
Published inElectrical engineering Vol. 101; no. 1; pp. 45 - 55
Main Authors Souifi, Hayfa, Kahouli, Omar, Hadj Abdallah, Hsan
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019
Springer Nature B.V
Subjects
Computer simulation
Economics and Management
Electric power distribution
Electrical Engineering
Electrical Machines and Networks
Energy Policy
Engineering
Genetic algorithms
Graph theory
Load-Flow
Mathematical analysis
Multiple criterion
Multiple objective analysis
Networks
Original Paper
Pareto optimization
Power efficiency
Power Electronics
Power loss
Reconfiguration
Voltage stability
Voltage stability
Graph theory
Load-flow
Distribution network reconfiguration
Genetic algorithm
Optimization
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Summary:This paper dealt with the optimal reconfiguration of a power distribution network where a genetic algorithm (GA) based on graph theory was considered. This paper addressed this issue as a multi-criteria one with low investment cost and reduced active power losses objectives. The topological and voltage stability constraints were taken into account in this study, and each objective was firstly optimized individually. The Pareto-optimality principle for the criteria trade-off was then presented. In order to calculate the active power losses, a new load-flow approach was implemented. The genetic algorithm was tested on IEEE 10-bus system. Results show that the proposed load-flow algorithm was an efficient tool to obtain an accurate calculus for the system power losses. Efficiency and capability of the GA to achieve good solutions in a conspicuously short time are also discussed, and all the simulation results explained that the two objectives mentioned above are conflicting with each other.
ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-019-00755-3