南方电网技术2026，Vol.20Issue(1)：108-118,11.DOI:10.13648/j.cnki.issn1674-0629.2026.01.011
基于多目标粒子群算法的新能源汇集网架规划
Renewable Energy Collection Grid Planning Based on Multi-Objective Particle Swarm Algorithm
摘要
Abstract
Large-scale renewable energy bases in deserts,Gobi areas,and wastelands face challenges in their AC collection grid frameworks,such as lacking conventional power source support and AC main grid connections.Grid-forming converter stations configured for centralized power delivery can provide voltage support to the AC collection grid.Optimizing the placement of these grid-forming converter stations can maximize the voltage strength of the AC grid.To address this,this paper proposes a voltage strength evaluation method for renewable energy collection grids supported by grid-forming voltage-sourced converters(VSCs).Furthermore,it introduces a multi-objective particle swarm optimization(MOPSO)algorithm to optimize the placement of grid-forming converter stations in such scenarios,thereby enhancing the overall voltage strength of the renewable energy collection grid.Firstly,based on the electrical distance between devices and the voltage fluctuations caused by their grid integration,a voltage strength evaluation method is derived for radial AC collection grids supported by grid-forming converter stations.Secondly,the proposed grid strength evaluation method is integrated into the MOPSO algorithm to construct an optimization model for the place-ment of grid-forming converter stations,facilitating the planning of renewable energy AC collection grids.Finally,case studies verify the accuracy of the proposed grid voltage strength evaluation method and demonstrate the rationality of optimizing converter station placement for improving overall grid voltage strength.关键词
沙戈荒/交流电网强度/新能源汇集/电网规划/多目标粒子群Key words
deserts,Gobi areas,and wastelands/AC grid strength/renewable energy collection/power grid planning/MOPSO分类
信息技术与安全科学引用本文复制引用
刘超男,黄勇,赵成勇..基于多目标粒子群算法的新能源汇集网架规划[J].南方电网技术,2026,20(1):108-118,11.基金项目
国家重点研发计划资助项目(2023YFB2405900).Supported by the National Key Research and Development Program of China(2023YFB2405900). （2023YFB2405900）
