Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems such as reactive power interactions among stations and unreasonable voltage gradients frequently arise. To address these issues, this study proposes an optimized multi-station coordinated response control strategy based on sensitivity analysis and hierarchical AVC. Firstly, based on the topology of the chain-structured hydropower sending-end network, a reactive power–voltage sensitivity matrix is constructed. Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. Finally, case studies based on the actual operational data of a CC-HPP network validate the effectiveness of the proposed strategy, and simulation results demonstrate that the approach reduces the interstation reactive power pulling up to 97.76% and improves the voltage gradient rationality by 16.67%. These results substantially improve grid stability and operational efficiency while establishing a more adaptable voltage control framework for large-scale hydropower integration. Furthermore, they provide a practical foundation for future advancements in multi-scenario hydropower regulation, enhanced coordination strategies, and predictive control capabilities within clean energy systems.

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Publication Details

Journal
Energies
Published
2025-05-22
DOI
https://doi.org/10.3390/en18112681
Citations
1
Primary Topic
Power System Optimization and Stability
Type
article
Field-Weighted Citation Impact
0.73

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article

Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis

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1 citations
Energies
Power System Optimization and Stability
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article

Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis

Haiming Niu, Simon X. Yang, Kun Yang, Lening Wang, Li Da Zhang, Jie Yang, Jun Wang, Xiaobing Liu
article en
1 citations

Abstract

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems such as reactive power interactions among stations and unreasonable voltage gradients frequently arise. To address these issues, this study proposes an optimized multi-station coordinated response control strategy based on sensitivity analysis and hierarchical AVC. Firstly, based on the topology of the chain-structured hydropower sending-end network, a reactive power–voltage sensitivity matrix is constructed. Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. Finally, case studies based on the actual operational data of a CC-HPP network validate the effectiveness of the proposed strategy, and simulation results demonstrate that the approach reduces the interstation reactive power pulling up to 97.76% and improves the voltage gradient rationality by 16.67%. These results substantially improve grid stability and operational efficiency while establishing a more adaptable voltage control framework for large-scale hydropower integration. Furthermore, they provide a practical foundation for future advancements in multi-scenario hydropower regulation, enhanced coordination strategies, and predictive control capabilities within clean energy systems.

EnergiesVol. 18(11)
Xihua University (CN), China Guodian Corporation (China) (CN), University of Guelph (CA)
National Natural Science Foundation of China
Openalex Percentile: Top 28%
Power System Optimization and Stability
0.73
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