An Overvoltage-Averse Model for Renewable-Rich AC/DC Distribution Networks Considering the Sensitivity of Voltage Violation Probability

The increasing renewable generation increase the probability of voltage violation. The spatial and temporal power transfer can be achieved in AC/DC distribution networks based on voltage source converters (VSCs) and energy storage (ES), which can effectively avoid system voltage violation. However, the existing voltage violation mitigation methods in uncertain scenarios are either limited by the long solution time or complex modeling, which are difficult to meet the overvoltage probability reduction requirements of intra-day dispatch. In addition, the power transfer will simultaneously affect interconnected systems because the power is coupled through the VSCs. Overvoltage probability reduction on one line may lead to an increase on the other. This paper proposes a two-stage overvoltage-averse model considering the sensitivity of voltage violation probability. The proposed method analytically depicts the impact of power adjustment on the system overvoltage probability. The day-ahead optimization model is established as chance-constrained model. The intra-day optimization model is established as a quadratic convex model, which can be efficiently solved. Simulation results verify that the method proposed can effectively achieve the overvoltage probability reduction of renewable-rich AC/DC distribution networks.

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Publication Details

Journal
IEEE Transactions on Sustainable Energy
Published
2024-10-08
DOI
https://doi.org/10.1109/tste.2024.3473011
Citations
8
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
1.67
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article

An Overvoltage-Averse Model for Renewable-Rich AC/DC Distribution Networks Considering the Sensitivity of Voltage Violation Probability

Fang Xie, Wei Tang, Bo Tong, Bo Zhang et al.
8 citations
IEEE Transactions on Sustainable Energy
Optimal Power Flow Distribution
1.67
article

An Overvoltage-Averse Model for Renewable-Rich AC/DC Distribution Networks Considering the Sensitivity of Voltage Violation Probability

Fang Xie, Wei Tang, Bo Tong, Bo Zhang, Gen Li, Lu Zhang
article en
8 citations

Abstract

The increasing renewable generation increase the probability of voltage violation. The spatial and temporal power transfer can be achieved in AC/DC distribution networks based on voltage source converters (VSCs) and energy storage (ES), which can effectively avoid system voltage violation. However, the existing voltage violation mitigation methods in uncertain scenarios are either limited by the long solution time or complex modeling, which are difficult to meet the overvoltage probability reduction requirements of intra-day dispatch. In addition, the power transfer will simultaneously affect interconnected systems because the power is coupled through the VSCs. Overvoltage probability reduction on one line may lead to an increase on the other. This paper proposes a two-stage overvoltage-averse model considering the sensitivity of voltage violation probability. The proposed method analytically depicts the impact of power adjustment on the system overvoltage probability. The day-ahead optimization model is established as chance-constrained model. The intra-day optimization model is established as a quadratic convex model, which can be efficiently solved. Simulation results verify that the method proposed can effectively achieve the overvoltage probability reduction of renewable-rich AC/DC distribution networks.

IEEE Transactions on Sustainable EnergyVol. 16(1)
North China Electric Power University (CN), China Electric Power Research Institute, China Agricultural University (CN), Technical University of Denmark (DK)
Affordable and clean energy
Openalex Percentile: Top 15%
Optimal Power Flow Distribution
1.67
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An Overvoltage-Averse Model for Renewable-Rich AC/DC Distribution Networks Considering the Sensitivity of Voltage Violation Probability — Fang Xie, Wei Tang, et al. · IEEE Transactions on Sustainable Energy (2024) | TGRS Research Map | TGRS