Multi-objective Optimal Power Flow Solution Using CSVPSO

Optimal power flow (OPF) is essential for achieving the objectives of maximizing power security as well as minimizing fuel costs and emission amount of thermal plants. Few studies focus on the collaborative optimization of multi-objective OPF problems. In this paper, we improve the traditional particle swarm optimization (PSO) and adopt PSO of constriction ranges of search space and velocity (CSVPSO) to solve the problem. The problem is simulated on IEEE-30 node system. The result shows the advantages of this algorithm.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/icpre62586.2024.10768576
Citations
1
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.17
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article

Multi-objective Optimal Power Flow Solution Using CSVPSO

Jinghua Li, Bowei Sun, Xiaoli Zhang
1 citations
Power Systems and Renewable Energy
0.17
article

Multi-objective Optimal Power Flow Solution Using CSVPSO

Jinghua Li, Bowei Sun, Xiaoli Zhang
article en
1 citations

Abstract

Optimal power flow (OPF) is essential for achieving the objectives of maximizing power security as well as minimizing fuel costs and emission amount of thermal plants. Few studies focus on the collaborative optimization of multi-objective OPF problems. In this paper, we improve the traditional particle swarm optimization (PSO) and adopt PSO of constriction ranges of search space and velocity (CSVPSO) to solve the problem. The problem is simulated on IEEE-30 node system. The result shows the advantages of this algorithm.

Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 41%
Power Systems and Renewable Energy
0.17
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Multi-objective Optimal Power Flow Solution Using CSVPSO — Jinghua Li, Bowei Sun, et al. · (2024) | TGRS Research Map | TGRS