Distribution Network Reconfiguration Considering the Impacts of Local Renewable Generation and External Power Grid

Journal Article (2023)
Author(s)

H. Li (Chongqing University of Technology)

Aleksandra Lekić (TU Delft - Intelligent Electrical Power Grids)

Shan Li (Chongqing University of Technology)

Dongrong Jiang (Chongqing University of Technology)

Qiang Guo (Chongqing University of Technology)

Lin Zhou (Chongqing University)

Research Group
Intelligent Electrical Power Grids
Copyright
© 2023 H. Li, A. Lekić, Shan Li, Dongrong Jiang, Qiang Guo, Lin Zhou
DOI related publication
https://doi.org/10.1109/TIA.2023.3307070
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 H. Li, A. Lekić, Shan Li, Dongrong Jiang, Qiang Guo, Lin Zhou
Research Group
Intelligent Electrical Power Grids
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
6
Volume number
59
Pages (from-to)
7771-7788
Reuse Rights

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Abstract

The distribution network (DN) reconfiguration is a well-known optimal power flow (OPF) problem. However, with the transition of DN from 'passive' to 'active', new technical challenges arise in DN reconfiguration. This article addresses two key issues in this regard. Firstly, the integration of local renewable generation (LRG) introduces uncertainty into the system-wide power flow of the DN. Secondly, the coupling between DN and the external power grid (EPG) affects the determination of DN root voltage. Consequently, a novel DN reconfiguration approach is proposed in this article. To begin with, an explicit mixed-integer convex OPF model is constructed that incorporates both the EPG and DN sides. Notably, the OPF model embeds the function of local droop control that is provided by LRG. Subsequently, the original OPF model is decomposed, and the distributed optimization methods based on the augmented Lagrangian relaxation are employed. The article comprehensively discusses parallel processing and asynchronous implementation as parts of the distributed optimization procedure. Furthermore, to address the uncertainty related to LRG integration, the extreme scenario method is used to provide a robust decision regarding DN reconfiguration. The application of the extreme scenario method in the distributed OPF model concerning DN reconfiguration is successively developed. Finally, numerical results are presented to demonstrate the acceptable performance of the distributed optimization methods, in terms of optimality and convergence. Also, these are validated that the proposed DN reconfiguration approach exhibits robustness to LRG integration, the system-wide voltage profile is improved, and the active power loss is effectively reduced using the proposed DN reconfiguration approach.

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