Planning method of station area interconnection device considering voltage-reactive power regulation
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    Abstract:

    With the widespread integration of distributed energy sources into low-voltage distribution networks,the demands for operational flexibility and absorptive capacity of distribution grids have been continuously increasing. The use of low-voltage flexible interconnection devices to interconnect independently operated low-voltage distribution substations helps to avoid frequent operations of traditional voltage regulation and reactive power compensation devices. Considering the high cost of flexible interconnection devices,a method is proposed to plan the siting and capacity of low-voltage flexible interconnection devices in coordination with traditional voltage-reactive power regulation devices. Firstly,the topology and operational mode of the low-voltage flexible interconnection devices are analyzed,and their power flow model is established. Subsequently,a dual-layer planning model is formulated for optimizing the configuration of low-voltage flexible interconnection devices. The upper-layer planning aims to minimize the annual comprehensive cost,while the lower-layer planning takes into account a time-series model for voltage-reactive power coordination control. The objectives of the lower-layer planning are to minimize operation costs and voltage deviations. The optimal solution for the distribution network system's flexible interconnection scheme and operational mode is obtained through alternating iterations of particle swarm optimization and mixed-integer second-order cone programming algorithms. Finally,a case study is conducted on the IEEE 33-node test system to validate the effectiveness of the proposed dual-layer planning algorithm. The results indicate that the proposed method can effectively reduce the excessive deployment of flexible interconnection devices while simultaneously decreasing the operational costs caused by frequent fluctuations in distributed energy sources. The method of convexifying and linearizing the model significantly enhances the solution efficiency.

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History
  • Received:November 28,2023
  • Revised:January 07,2024
  • Adopted:November 07,2023
  • Online: May 23,2024
  • Published: May 28,2024