Optimization and Control Strategies for Stochastic Power Quality of Distributed Generation

To solve power quality problems in distribution networks, this paper presents a distributed generation siting and capacity determination method. The methodology aims to minimize network losses, reduce the total harmonic distortion rate of voltage (THDV), and take into account load and power supply uncertainties. A probabilistic optimization solution algorithm that combines a traceless variation method and a traversal optimization algorithm is proposed, which greatly reduces the computational effort. Simulations are performed to validate it using an IEEE-69 node system. It is demonstrated that this proposed method improves the power quality by significantly reducing network losses.

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Publication Details

Published
2024-10-15
DOI
https://doi.org/10.1109/ichqp61174.2024.10768739
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimization and Control Strategies for Stochastic Power Quality of Distributed Generation

Xiangmin Xie, Zhihao Wei, Jinghui Zhang, Shurong Li et al.
Power Systems and Renewable Energy
article

Optimization and Control Strategies for Stochastic Power Quality of Distributed Generation

Xiangmin Xie, Zhihao Wei, Jinghui Zhang, Shurong Li, Juan Wang
article en

Abstract

To solve power quality problems in distribution networks, this paper presents a distributed generation siting and capacity determination method. The methodology aims to minimize network losses, reduce the total harmonic distortion rate of voltage (THDV), and take into account load and power supply uncertainties. A probabilistic optimization solution algorithm that combines a traceless variation method and a traversal optimization algorithm is proposed, which greatly reduces the computational effort. Simulations are performed to validate it using an IEEE-69 node system. It is demonstrated that this proposed method improves the power quality by significantly reducing network losses.

Qingdao University (CN)
China Postdoctoral Science Foundation, Natural Science Foundation of Shandong Province
Affordable and clean energy
Openalex Percentile: Top 78%
Power Systems and Renewable Energy
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