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Research on site selection and capacity determination problem based on improved particle swarm algorithm.

Xiaotong Mi1, Qinyang Liu1, Bo Geng1

  • 1Department of Physics and Electrics, Fuyang Normal University, Fuyang, 236037, China.

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|July 2, 2025
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Summary
This summary is machine-generated.

This study introduces an improved hybrid optimization algorithm (IPSOACO) for efficient distributed power source integration. The method enhances energy transmission efficiency and reduces costs in power distribution systems.

Keywords:
Ant colony optimization algorithmParticle swarm algorithmPenalty functionReactive power correctionSiting and capacity

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Power Systems

Background:

  • Effective integration of distributed power sources (DPS) is crucial for modern power grids.
  • Maximizing energy transmission efficiency and minimizing operational costs are key challenges.
  • Existing optimization algorithms face limitations in speed, convergence, and avoiding local optima.

Purpose of the Study:

  • To propose an integrated optimization scheme for effective DPS utilization.
  • To enhance energy transmission efficiency and reduce costs in distribution networks.
  • To address limitations of traditional optimization methods in DPS placement and capacity planning.

Main Methods:

  • Integration of improved particle swarm optimization (IPSO) and improved ant colony optimization (IACO) algorithms (IPSOACO).
  • Application of a reactive power correction method for diverse node processing.
  • Deployment and testing of the IPSOACO algorithm on the IEEE 33-node system for DPS site selection.

Main Results:

  • The IPSOACO algorithm demonstrated superior performance in reducing power loss compared to traditional PSOACO.
  • Significant improvements in voltage quality were observed using the proposed IPSOACO method.
  • Enhanced computational efficiency and system stability were achieved in the IEEE 33-node system.

Conclusions:

  • The IPSOACO scheme offers a robust solution for optimizing distributed power source location and capacity.
  • The method effectively overcomes challenges related to voltage quality and network loss in distribution systems.
  • This research provides a valuable approach for improving the operational performance of power distribution networks with integrated DPS.