Distributed Voltage Regulation for Distribution Networks With Privacy‐Preserving Under the Framework of VPP

ABSTRACT Global concern about climate change has accelerated the integration of renewable energy. To accommodate the high penetration of renewables at the distribution level and maintain system flexibility under a fully distributed architecture, this paper develops a voltage control strategy based on federated learning coordinated by a virtual power plant. A dynamic network partitioning method is introduced using a comprehensive performance index, along with an adaptive genetic algorithm featuring elite retention. An enhanced alternating direction method of multipliers with adaptive penalty modulation is employed to improve the convergence efficiency. Additionally, a two‐stage encryption mechanism is applied to protect user privacy and ensure cybersecurity during distributed coordination. The effectiveness and feasibility of the proposed method are validated on a modified IEEE 33‐bus system.

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Publication Details

Journal
IET Energy Systems Integration
Published
2025-01-01
DOI
https://doi.org/10.1049/esi2.70011
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
0.00

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article

Distributed Voltage Regulation for Distribution Networks With Privacy‐Preserving Under the Framework of VPP

Jiani Xiang, Rujing Wang, Tao Xu, Yu Ji et al.
IET Energy Systems Integration
Optimal Power Flow Distribution
article

Distributed Voltage Regulation for Distribution Networks With Privacy‐Preserving Under the Framework of VPP

Jiani Xiang, Rujing Wang, Tao Xu, Yu Ji, Zhang Ying, Ping Song, Hongru Wang, Meng He
article en

Abstract

ABSTRACT Global concern about climate change has accelerated the integration of renewable energy. To accommodate the high penetration of renewables at the distribution level and maintain system flexibility under a fully distributed architecture, this paper develops a voltage control strategy based on federated learning coordinated by a virtual power plant. A dynamic network partitioning method is introduced using a comprehensive performance index, along with an adaptive genetic algorithm featuring elite retention. An enhanced alternating direction method of multipliers with adaptive penalty modulation is employed to improve the convergence efficiency. Additionally, a two‐stage encryption mechanism is applied to protect user privacy and ensure cybersecurity during distributed coordination. The effectiveness and feasibility of the proposed method are validated on a modified IEEE 33‐bus system.

IET Energy Systems IntegrationVol. 7(1)
Tianjin University (CN), State Grid Corporation of China (China) (CN), Shanghai Electric (China) (CN)
National Natural Science Foundation of China, Natural Science Foundation of Tianjin City, National Key Research and Development Program of China
Openalex Percentile: Top 87%
Optimal Power Flow Distribution
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