浙江电力

2026, v.45;No.366(10) 102-112

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考虑时空相关性的城市配电网无功优化配置
Optimal reactive power configuration for urban distribution network considering spatiotemporal correlation

宋耀,杨秀,孙改平,张美霞,杨帆
SONG Yao,YANG Xiu,SUN Gaiping,ZHANG Meixia,YANG Fan

摘要(Abstract):

针对高比例新型源-荷接入给配电网安全稳定运行带来的严峻挑战,提出了一种考虑用户无功时空分布特性的城市配电网多目标无功优化配置方法。首先,采用基于拉普拉斯特征映射与高斯混合模型的联合方法,挖掘负荷无功特性的时空耦合关系,提取不同用户的典型无功负荷场景。其次,基于典型场景构建双向综合裕度指标,实现考虑电压双向越限情况的无功补偿点筛选,获得多场景耦合条件下的最优无功补偿配置点。进一步地,建立以电压偏移、有功损耗及投资成本最小为目标的多场景耦合无功补偿配置模型,并采用改进的黑翅鸢优化算法进行求解。最后,基于上海市用户实测数据构建改进的IEEE 33节点系统进行算例分析,验证了所提方法的有效性与可行性。
The high penetration of emerging source-load integrations poses significant challenges to the secure and stable operation of distribution networks. To address this issue, a multi-objective reactive power configuration method for urban distribution networks is proposed, taking into account the spatiotemporal distribution characteristics of user-side reactive power. First, a hybrid approach combining Laplacian eigenmaps and a Gaussian mixture model is employed to uncover the spatiotemporal coupling of reactive load characteristics and to extract typical reactive load scenarios for different users. Next, a bidirectional composite margin index is constructed based on these typical scenarios to screen candidate reactive power compensation locations under bidirectional voltage limit violations, thereby identifying optimal compensation locations under multi-scenario coupling conditions. Furthermore, a multi-scenario coupled reactive power compensation configuration model is established with the objectives of minimizing voltage deviation, active power losses, and investment cost, and is solved using an improved black-winged kite algorithm(BKA). Finally, an improved IEEE 33-bus system is developed using measured data from users in Shanghai for case studies, verifying the effectiveness and feasibility of the proposed method.

关键词(KeyWords): 城市配电网;分布式光伏;无功补偿;时空相关性
urban distribution network;distributed photovoltaic(PV);reactive power compensation;spatiotemporal correlation

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(52177098);; 上海电力人工智能工程技术研究中心项目(19DZ2252800)

作者(Author): 宋耀,杨秀,孙改平,张美霞,杨帆
SONG Yao,YANG Xiu,SUN Gaiping,ZHANG Meixia,YANG Fan

DOI: 10.19585/j.zjdl.202610010

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