上海电力大学电气工程学院,上海市 200090
刘博(1985—),女,博士,讲师,硕士生导师,主要研究方向:电力系统优化运行。E-mail:liubo973@163.com
岳挺(2000—),男,通信作者,硕士研究生,主要研究方向:配电网安全性分析。E-mail:18790131811@163.com
收稿:2025-04-17,
修回:2025-10-27,
网络首发:2026-01-04,
纸质出版:2026-02-25
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刘博,岳挺.计及风光出力不确定性的配电网安全域自适应搜索算法[J].电力系统自动化,2026,50(4):111-119.
LIU Bo,YUE Ting.Adaptive Search Algorithm for Security Regions in Distribution Network Considering Uncertainties of Wind and Photovoltaic Output[J].Automation of Electric Power Systems,2026,50(4):111-119.
刘博,岳挺.计及风光出力不确定性的配电网安全域自适应搜索算法[J].电力系统自动化,2026,50(4):111-119. DOI: 10.7500/AEPS20250417003.
LIU Bo,YUE Ting.Adaptive Search Algorithm for Security Regions in Distribution Network Considering Uncertainties of Wind and Photovoltaic Output[J].Automation of Electric Power Systems,2026,50(4):111-119. DOI: 10.7500/AEPS20250417003.
现有配电网安全域算法在处理风光出力不确定性时,常因场景生成依赖单一分布假设而忽
略了时空相关性,导致算法搜索效率低、冗余搜索路径多。为此,文中提出一种计及风光出力不确定性的配电网安全域自适应搜索算法。首先,基于核密度估计和混合Copula函数构建风光出力联合分布模型,通过蒙特卡洛采样与
K
-means聚合构建风光出力时空耦合场景。其次,提出自适应混合搜索策略,结合一次迭代与二次修正的自适应区间分割与灵敏度矩阵引导机制,通过关键约束进行搜索方向动态调整,显著减少冗余搜索路径,降低算法计算时长,实现鲁棒边界逼近。最后,通过并行计算加速机制优化算法性能,并在IEEE 33节点配电系统中验证所提算法的有效性。
Existing algorithms for security regions in distribution network often suffer from low searching efficiency and redundant search paths when handling uncertainties of wind and photovoltaic output. This is due to their reliance on single-distribution assumptions for scenario generation
which ignores spatio-temporal correlations. To address these issues
this paper proposes an adaptive search algorithm for security regions in distribution network considering the uncertainties of wind and photovoltaic output. Firstly
a joint distribution model of wind and photovoltaic output is constructed using kernel density estimation and the mixed Copula function. Spatio-temporal coupling scenarios are then generated through Monte Carlo sampling and reduced via
K
-means clustering. Secondly
an adaptive hybrid search strategy is proposed
combining adaptive interval segmentation with sensitivity matrix guidance in a two-step process of initial iteration and secondary correction. This strategy dynamically adjusts the search direction based on key constraints
significantly reducing redundant search paths and computation time while achieving robust boundary approximation. Finally
algorithm performance is optimized through a parallel computing acceleration mechanism. The effectiveness of the proposed method is validated on an IEEE 33-bus distribution system.
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