闫丽梅,丁泽华
YAN Limei,DING Zehua

• 1:东北石油大学电气信息工程学院

摘要(Abstract)：

高比例分布式光伏接入配电网后，传统优化方案无法有效平抑电压波动，分布式光伏逆变器的无功调控能力难以充分利用。为此，提出一种基于谱聚类的主动配电网多时间尺度无功优化策略，该方法分为日前优化和日内实时优化两个阶段。首先，对离散设备的时间耦合性进行解耦，以配电网网损、平均电压偏差、电压波动严重程度为目标函数，建立基于社交网络搜索算法的日前无功优化模型，确定离散设备静态最优档位序列；其次，通过谱聚类的方法进行耦合，确定离散设备动态最优档位序列，结合改进的分布式光伏逆变器就地控制策略，建立日内实时优化模型，从而抑制日前预测数据偏差导致的电压波动；最后，基于改进后的IEEE 33节点系统进行仿真实验。仿真结果表明，所提策略可以有效降低运算难度、提高求解效率，验证了该策略的有效性和优越性。
With a significant increase in the integration of distributed photovoltaic(PV) systems into distribution networks, traditional optimization approaches struggle to effectively mitigate voltage fluctuations, and the reactive power control capability of distributed PV inverters remains underutilized. In response, this paper proposes a reactive power optimization strategy for active distribution networks based on spectral clustering across multiple timescales. The approach consists of two stages: day-ahead optimization and real-time optimization. Firstly, the temporal coupling of discrete equipment actions is decoupled. Using distribution network power loss, average voltage deviation, and voltage fluctuation severity as objective functions, a day-ahead reactive power optimization model is formulated based on a social network search algorithm. This model determines the static optimal operating sequences for discrete equipment. Secondly, employing spectral clustering for coupling, the dynamic optimal operating sequences for discrete equipment are determined. The strategy incorporates an improved local control strategy for distributed PV inverters and establishes a real-time optimization model, thereby mitigating voltage fluctuations caused by discrepancies in day-ahead forecast data. Finally, the proposed strategy is validated through simulations on an improved IEEE 33-node system. Simulation results demonstrate that the proposed strategy effectively reduces computational complexity, enhances solution efficiency, and verifies the effectiveness and superiority of the approach.

关键词(KeyWords)： 主动配电网;多时间尺度;动态无功优化;谱聚类解耦方法;社交网络搜索算法
active distribution network;multi-timescale;dynamic reactive power optimization;decoupling method using spectral clustering;social network search algorithm

Abstract:

Keywords:

基金项目(Foundation): 黑龙江省自然科学基金项目（LH2019E016）

作者(Author): 闫丽梅,丁泽华
YAN Limei,DING Zehua

DOI: 10.19585/j.zjdl.202402007

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