Journal
SOFT COMPUTING
Volume 24, Issue 24, Pages 18573-18590Publisher
SPRINGER
DOI: 10.1007/s00500-020-05093-2
Keywords
Metaheuristics; Particle swarm optimisation; Electric distribution systems; Reconfiguration
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Funding
- Lashtenesha-Zibakenar Branch, Islamic Azad University [17-16-14-39782]
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In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming.
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