Mi Xiaotong, Liu Qinyang, Geng Bo, Zhu Yong
Department of Physics and Electrics, Fuyang Normal University, Fuyang, 236037, China.
Sci Rep. 2025 Jul 1;15(1):21201. doi: 10.1038/s41598-025-04242-7.
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.
为促进分布式电源并网后有效利用，实现能量传输效率最大化和成本最小化的目标，本文提出一种基于改进粒子群优化算法与改进蚁群优化算法（IPSOACO）相结合的方案。该方案首先采用无功功率校正方法对各类节点进行处理。其次，对传统粒子群优化（PSO）算法和蚁群优化（ACO）算法进行改进，以解决优化前期和后期优化速度慢、早熟收敛以及容易陷入局部最优等问题。将改进后的PSO算法的最优解与蚁群算法的初始值相结合，并部署在IEEE33节点系统中进行选址。与传统粒子群优化算法融合蚁群优化算法（PSOACO）相比，改进算法在降低功率损耗和提高电压质量方面更为突出。它解决了IEEE 33节点系统中电压质量差、网络损耗高以及优化算法局限性等问题，并在一定程度上提高了系统的计算效率和稳定性。为配电网中分布式电源的选址和容量研究提供了更好的解决方案。
