考虑模块化动态分区的有源配电网电压优化控制方法OA
Voltage optimization control method for active distribution networks considering modular dynamic zoning
针对高比例分布式电源接入配电网引发的电压越限问题,提出了一种融合模块化动态分区与雪消融优化算法(snow ablation optimization,SAO)的协同电压控制策略.首先,阐述了有源配电网的动态分区准则,引入了基于灵敏度的电气距离分区法,建立了考虑模块度、灵敏度和集群电压自治能力的动态分区评价体系.其次,构建了有源配电网电压优化控制目标函数,采取SAO算法实现优化模型高效求解.最后,采用了改进的IEEE33节点配电系统展开仿真分析,通过与传统聚类分区方法、经典粒子群算法进行定量对比,验证了所提方法的先进性和适应性.结果表明:所提方法可保障集群功率分配更趋于平衡,有效地提升节点电压稳定性且降低了配电网网损.
To address voltage violation issues caused by the high proportion of distributed generation in distribution networks,this paper proposes a coordinated voltage control strategy that integrates modular dynamic zoning and the snow ablation optimization(SAO)algorithm.First,the dynamic zoning criteria for active distribution networks are clarified,and a sensitivity-based electrical distance zoning method is introduced.On this basis,a dynamic zoning evaluation system is established by comprehensively considering modularity,sensitivity,and cluster-level voltage self-regulation capability.Second,an objective function for voltage optimization control in active distribution networks is constructed,and the SAO algorithm is employed to efficiently solve the model.Finally,a modified IEEE 33-bus distribution system is used for simulation analysis.Through quantitative comparisons with traditional clustering-based zoning methods and the classical particle swarm optimization algorithm,the effectiveness,superiority,and adaptability of the proposed method are verified.The results demonstrate that the proposed method achieves more balanced power allocation among clusters,effectively improves node voltage stability,and reduces distribution network power losses.
曹玉媛;黄达文;李丰;李必伟;陈磊;李暮思;陈红坤
广东电网有限责任公司肇庆供电局,广东 肇庆 526040广东电网有限责任公司肇庆供电局,广东 肇庆 526040广东电网有限责任公司肇庆供电局,广东 肇庆 526040广东电网有限责任公司肇庆供电局,广东 肇庆 526040武汉大学电气与自动化学院,湖北 武汉 430072武汉大学电气与自动化学院,湖北 武汉 430072武汉大学电气与自动化学院,湖北 武汉 430072
动态分区分布式电源雪消融优化算法有源配电网
dynamic zoningdistributed generationSAOactive distribution network
《电力系统保护与控制》 2026 (3)
70-79,10
This work is supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd.(No.GDKJXM20231387). 中国南方电网有限责任公司项目资助(GDKJXM 20231387)国家科技重大专项资助(2024ZD0800600)
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