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Keywords

distributed power generation, bi-level programming, power characteristics, optimized configuration

Abstract

The large-scale integration of distributed photovoltaics (DPV) poses significant threats to the safe and stable operation of distribution networks, creating an urgent need to optimize the configuration of power resources. A bi-level programming model that considers the dynamic clustering of DPV was proposed; it uses an improved adaptive genetic algorithm for dynamic DPV cluster partitioning. Based on the proposed bi-level programming model, the dynamic optimized configuration of power resources was achieved, node voltages were maintained, and power losses were reduced. The results show that, compared with the voltage optimization scheme, the comprehensive optimization approach reduces the total cost by 4.68%. Compared with the pre-adjustment state and the initial cluster partitioning, the system power consumption after dynamic cluster adjustment and voltage adjustment decreases by 26.51% and 13.30%, respectively. The photovoltaic-storage system incorporated into the planning model effectively reduces active power losses in the distribution network, and the effect of reducing active power loss is more obvious when solar radiation is relatively weak.

DOI

10.19781/j.issn.1673-9140.2026.03.015

First Page

150

Last Page

159

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