浙江电力2024，Vol.43Issue(2)：58-68,11.DOI:10.19585/j.zjdl.202402007
基于谱聚类的主动配电网多时间尺度无功优化策略
A multi-timescale reactive power optimization strategy for active distribution net-works based on spectral clustering
摘要
Abstract
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 reac-tive power optimization strategy for active distribution networks based on spectral clustering across multiple times-cales.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 devia-tion,and voltage fluctuation severity as objective functions,a day-ahead reactive power optimization model is formu-lated 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 dis-crepancies in day-ahead forecast data.Finally,the proposed strategy is validated through simulations on an im-proved IEEE 33-node system.Simulation results demonstrate that the proposed strategy effectively reduces computa-tional complexity,enhances solution efficiency,and verifies the effectiveness and superiority of the approach.关键词
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Key words
active distribution network/multi-timescale/dynamic reactive power optimization/decoupling method using spectral clustering/social network search algorithm引用本文复制引用
闫丽梅,丁泽华..基于谱聚类的主动配电网多时间尺度无功优化策略[J].浙江电力,2024,43(2):58-68,11.基金项目
黑龙江省自然科学基金项目(LH2019E016) （LH2019E016）
