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基于多目标沙猫群算法的含风光储配电网无功优化

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针对现有智能优化算法在求解配电网无功优化时存在的收敛速度慢、易陷入局部最优解等问题,提出一种基于多目标沙猫群算法(MOSCSO)的含风光储配电网无功优化方法.MOSCSO融合了多目标算法中外部储存集的更新和选择机制,具有较好的全局寻优能力,而沙猫群算法(SCSO)特有的搜索和攻击的种群更新方式保证了其具有较快收敛速度和较好寻优能力.建立储能设施(ESS)作为控制变量的 IEEE 33 节点系统数学模型,应用MOSCSO进行仿真验证.结果表明,本文所提方法在平衡风光发电系统的同时能够降低网损和提高电网稳定性,通过与传统算法比较,验证了MOSCSO在无功优化模型上的有效性和稳定性.
Reactive power optimization of wind/solar power storage and distribution networks based on multi-objective sand cat swarm algorithm
Existing intelligent optimization algorithms for reactive power optimization in distribution networks are perplexed by problems of slow convergence speed and easy falling into local optima.Here,a new approach based on the Multi-Objective Sand Cat Swarm Optimization(MOSCSO)is proposed to solve the reactive power optimization of wind and solar power storage and distribution networks.MOSCSO integrates the update and selection mechanism of external save sets in multi-objective algorithms,and has good global optimization ability.Meanwhile,the unique search and attack population update method of the sand cat swarm algorithm ensures its fast convergence speed and good optimization ability.An IEEE 33 bus system mathematical model with Energy Storage System(ESS)was estab-lished as the control variable,and then the MOSCSO was applied for simulation verification.The results demonstrate that the proposed approach can reduce grid loss and improve grid stability while balancing the wind and solar power generation systems,which verify the effectiveness and stability of MOSCSO in reactive power optimization.

reactive power optimization of distribution networkmulti-objective sand cat swarm algorithmenergy storage systemdistributed generation

商立群、张少强、刘江山

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西安科技大学 电气与控制工程学院,西安, 710054

配电网无功优化 多目标沙猫群算法 储能系统 分布式电源

陕西省自然科学基础研究计划

2021JM-393

2024

南京信息工程大学学报
南京信息工程大学

南京信息工程大学学报

CSTPCD北大核心
影响因子:0.737
ISSN:1674-7070
年,卷(期):2024.16(2)
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