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.
active distribution networkmulti-timescaledynamic reactive power optimizationdecoupling method using spectral clusteringsocial network search algorithm
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