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Distributed Data-Driven Optimization for Voltage Regulation in Distribution Systems
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DOI：10.1109/TPWRS.2023.3242868.png)
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摘要
En 中文
This paper proposes a distributed data-driven optimization framework for voltage regulation in distribution systems. The recursive kernel regression and alternating direction method of multipliers (ADMM) are selected to cover the system learning and distributed optimization tasks. The proposed distributed data-driven framework is capable of having a rapid response to system or load changes while considering the operation optimality. Besides, the distributed algorithm parallels the computation tasks and reduces the computational expense of a single agent. To validate the performance of the proposed method, a hypothetical 7-Bus system and the IEEE 123-Bus system are selected to show the effectiveness of the proposed data-driven framework. According to the numerical study results, the proposed method offers great flexibility for selecting customized kernel models for different regions and can effectively improve the system voltage profile in a distributed manner.
Keyword:
Voltage control
Indexes
Computational modeling
Kernel
Optimization
Topology
Load modeling
Data-driven optimization
multiphase distribution system
recursive kernel regression
voltage control
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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引用论文
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Branch-Wise Parallel Successive Algorithm for Online Voltage Regulation in Distribution Networks
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