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Data-Driven Optimization Framework for Voltage Regulation in Distribution Systems

delete2022-04-01
delete7
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OA
AI
T
Tianqi Hong *
Y
Yichen Zhang
DOI:10.1109/TPWRD.2021.3136773delete
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Abstract

Abstract

En 中文
This letter proposes a data-driven optimization framework for voltage regulation problems to address the challenge of model inaccuracy and parameter varying. To achieve online voltage optimization, the recursive kernel regression and interior point methods are integrated. The IEEE 123-Bus system and EPRI Ckt5 feeder are selected to validate the effectiveness of the proposed data-driven optimization framework. The proposed method is also compared with a linear function based method.
Keywords:
Voltage control
Optimization
Kernel
Mathematical models
Load flow
Voltage measurement
Linear programming
Recursive kernel regression
data-driven optimization
multiphase distribution system
voltage control

Journal

IEEE Transactions on Power Delivery cover
IEEE Transactions on Power Delivery
IF:
3.7
Papers:
9.1K
Citations:
2.2W

Organization

U
united states department of energy (doe)
Scholars:
11.3W
Papers: 9.6W
Citations: 246
Cited Papers

Cited Papers

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Data-Driven Voltage Regulation in Radial Power Distribution Systems
err2020-05-01
err77
errOAAI
errXu, Hanchen; Dominguez-Garcia, Alejandro D.; Veeravalli, Venugopal V.; Sauer, Peter W.
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Model-Free Optimal Voltage Phasor Regulation in Unbalanced Distribution Systems
err2020-01-01
err21
errOAAI
errSankur, Michael D.; Dobbe, Roel; von Meier, Alexandra; Arnold, Daniel B.
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A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning
err2020-11-01
err204
PREAI
errWang, Shengyi; Duan, Jiajun; Shi, Di; Xu, Chunlei; Li, Haifeng; Diao, Ruisheng; Wang, Zhiwei
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