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<p>To promote the effective utilization of distributed power sources after grid connection and achieve the goal of maximizing energy transmission efficiency and minimizing cost, this paper proposes a scheme based on the integration of the improved particle swarm optimization algorithm and the improved ant colony optimization algorithm (IPSOACO). This scheme first adopts the reactive power correction method to process various types of nodes. Secondly, the traditional particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms are improved to solve problems such as slow optimization speed in the early and late stages of optimization, premature convergence, and being prone to fall into local optimum. The optimal solution of the improved PSO algorithm is combined with the initial value of the ant colony algorithm and deployed in the IEEE33 node system for site selection. Compared with the traditional particle swarm optimization algorithm fused with Ant colony optimization algorithm (PSOACO), the improved algorithm is more prominent in reducing power loss and improving voltage quality. It solves problems such as poor voltage quality, high network loss and limitations of the optimization algorithm in the IEEE 33-node system, and improves the computational efficiency and stability of the system to a certain extent. It provides a better solution for the research on the location and capacity of distributed power sources in the distribution network.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Xiaotong Mi,
  • Qinyang Liu,
  • Bo Geng,
  • Yong Zhu

摘要

To promote the effective utilization of distributed power sources after grid connection and achieve the goal of maximizing energy transmission efficiency and minimizing cost, this paper proposes a scheme based on the integration of the improved particle swarm optimization algorithm and the improved ant colony optimization algorithm (IPSOACO). This scheme first adopts the reactive power correction method to process various types of nodes. Secondly, the traditional particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms are improved to solve problems such as slow optimization speed in the early and late stages of optimization, premature convergence, and being prone to fall into local optimum. The optimal solution of the improved PSO algorithm is combined with the initial value of the ant colony algorithm and deployed in the IEEE33 node system for site selection. Compared with the traditional particle swarm optimization algorithm fused with Ant colony optimization algorithm (PSOACO), the improved algorithm is more prominent in reducing power loss and improving voltage quality. It solves problems such as poor voltage quality, high network loss and limitations of the optimization algorithm in the IEEE 33-node system, and improves the computational efficiency and stability of the system to a certain extent. It provides a better solution for the research on the location and capacity of distributed power sources in the distribution network.