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基于改进粒子群算法的多目标分布式电源选址定容规划
引用本文:周洋,许维胜,王宁,邵炜晖.基于改进粒子群算法的多目标分布式电源选址定容规划[J].计算机科学,2015,42(Z11):16-18, 31.
作者姓名:周洋  许维胜  王宁  邵炜晖
作者单位:同济大学电子与信息工程学院控制科学与工程系 上海201804,同济大学电子与信息工程学院控制科学与工程系 上海201804,同济大学电子与信息工程学院控制科学与工程系 上海201804,同济大学电子与信息工程学院控制科学与工程系 上海201804
基金项目:本文受国家自然科学基金资助
摘    要:通过分析分布式电源对配电网的影响,以有功功率损耗、电压质量及分布式电源总容量为优化目标,基于模糊理论建立了分布式电源在配电网中选址定容的多目标优化模型,并提出了一种改进粒子群算法进行求解。在算例仿真中,基于IEEE 14标准节点系统,采用MATLAB仿真工具对所提算法进行了测试,证实了所提算法全局搜索能力较强、收敛速度较快,并通过比较分析验证了该模型和算法的可行性及有效性。

关 键 词:分布式电源  选址定容  粒子群算法  多目标优化

Multi-objective Siting and Sizing of Distributed Generation Planning Based on Improved Particle Swarm Optimization Algorithm
ZHOU Yang,XU Wei-sheng,WANG Ning and SHAO Wei-hui.Multi-objective Siting and Sizing of Distributed Generation Planning Based on Improved Particle Swarm Optimization Algorithm[J].Computer Science,2015,42(Z11):16-18, 31.
Authors:ZHOU Yang  XU Wei-sheng  WANG Ning and SHAO Wei-hui
Affiliation:Department of Control Science and Engineering,School of Electronics and Engineering,Tongji University,Shanghai 201804,China,Department of Control Science and Engineering,School of Electronics and Engineering,Tongji University,Shanghai 201804,China,Department of Control Science and Engineering,School of Electronics and Engineering,Tongji University,Shanghai 201804,China and Department of Control Science and Engineering,School of Electronics and Engineering,Tongji University,Shanghai 201804,China
Abstract:By analyzing the impact of the distributed generations(DGs) on the distribution network,a method was built for siting and sizing of DGs in the distribution network.Comprehensively considering three indices of active power losses,voltage quality and DG capacity,the paper presented an improved particle swarm optimization algorithm for multi-objective optimization based on fuzzy theory.The proposed method was testified on IEEE 14-node system achieved by MATLAB.Simulation results show that the solving algorithm has high capability of global search and better convergence rate.The feasibility and effectiveness of the method and the algorithm were finally evaluated through comparative analysis.
Keywords:Distributed generations  Siting and sizing  Particle swarm optimization  Multi-objective optimization
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