Multi-objective collaborative optimization of active distribution network operation based on improved particle swarm optimization algorithm
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View abstract on PubMed
Summary
This summary is machine-generated.Active distribution networks (ADNs) face operational challenges affecting power quality and safety. This study introduces an improved particle swarm optimization for multi-objective collaborative optimization, enhancing ADN stability and performance during peak demand.
Area Of Science
- Electrical Engineering
- Power Systems
Background
- Active distribution networks (ADNs) are susceptible to operational disturbances, leading to degraded power quality and reduced safety.
- Existing optimization methods may not adequately address the complex, multi-objective nature of ADN operation.
Purpose Of The Study
- To develop and simulate a multi-objective collaborative optimization strategy for ADN operation.
- To improve the stability, power quality, and operational safety of ADNs.
Main Methods
- Constructed an objective function for multi-objective collaborative optimization of ADN operation.
- Applied an improved particle swarm optimization algorithm with population mutation for optimal energy storage configuration.
- Developed a simulation platform for ADN cooperative operation, incorporating hierarchical load management.
- Achieved multi-objective collaborative optimization of ADN operating voltage in both frequency and time domains.
Main Results
- The improved particle swarm optimization algorithm yielded optimal energy storage capacity configurations.
- Hierarchical load management enabled multi-objective collaborative optimization of ADN voltage.
- Optimized systems demonstrated stable power supply during peak demand, with load capacity significantly improved compared to unoptimized states.
- Multi-objective optimization in both frequency and time domains proved most effective.
Conclusions
- The proposed improved particle swarm optimization effectively optimizes ADN operation for enhanced stability and power quality.
- The multi-objective collaborative optimization approach ensures reliable power supply, even under peak load conditions.
- The method shows significant improvements in managing active and reactive power within the ADN.
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