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Multi-objective collaborative optimization of active distribution network operation based on improved particle swarm

Shumin Sun1, Peng Yu1, Jiawei Xing2

  • 1State Grid Shandong Electric Power Research Institute, Jinan, 250002, China.

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|March 16, 2025
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.

Keywords:
Active distribution networkCollaborative optimizationDistribution network operationImproved particle swarm optimizationMulti-objective

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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.