Siting and capacity configurations of static transfer switches for three-phase unbalance governance in rural distribution networks: a bi-level optimization programme

Introduction: Mitigating three-phase unbalance in rural distribution networks is a significant challenge, especially with the integration of photovoltaic and energy storage systems (PESS). While Distributed Static Transfer Switches (STS) offer a promising solution by regulating load phase sequences, conventional approaches are costly and inefficient, limiting large-scale implementation. Methods: To address these limitations, we propose a bi-level optimization model for the siting and capacity optimization of STS in rural networks. The upper-layer model focuses on minimizing investment and maintenance costs for STS, while considering branch loss reduction and three-phase unbalance mitigation. The lower-layer model aims to minimize three-phase unbalance in daily operations, with the integration of PESS. We use the hyperparameter alternating iteration (HAI) method to iteratively refine the bi-level model and obtain optimal planning and operational solutions. Results: We applied the proposed model to an IEEE-13 benchmark case study. The results demonstrated that the bi-level optimization approach effectively reduced three-phase unbalance in the rural distribution system while minimizing STS planning costs. Discussion: This innovative approach provides a cost-effective and efficient solution for mitigating three-phase unbalance in rural distribution networks, enhancing the feasibility of large-scale STS deployment. The integration of PESS further contributes to system stability, making this model a robust tool for future network planning.

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Publication Details

Journal
Frontiers in Energy Research
Published
2025-04-22
DOI
https://doi.org/10.3389/fenrg.2024.1325477
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21

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article

Siting and capacity configurations of static transfer switches for three-phase unbalance governance in rural distribution networks: a bi-level optimization programme

Shiyang Zheng, Jiguo Zhang, Chenggang Hao, Houhe Chen et al.
1 citations
Frontiers in Energy Research
Microgrid Control and Optimization
1.21
article

Siting and capacity configurations of static transfer switches for three-phase unbalance governance in rural distribution networks: a bi-level optimization programme

Shiyang Zheng, Jiguo Zhang, Chenggang Hao, Houhe Chen, Jun Feng, Keyin Jia, Junyan Shao
article en
1 citations

Abstract

Introduction: Mitigating three-phase unbalance in rural distribution networks is a significant challenge, especially with the integration of photovoltaic and energy storage systems (PESS). While Distributed Static Transfer Switches (STS) offer a promising solution by regulating load phase sequences, conventional approaches are costly and inefficient, limiting large-scale implementation. Methods: To address these limitations, we propose a bi-level optimization model for the siting and capacity optimization of STS in rural networks. The upper-layer model focuses on minimizing investment and maintenance costs for STS, while considering branch loss reduction and three-phase unbalance mitigation. The lower-layer model aims to minimize three-phase unbalance in daily operations, with the integration of PESS. We use the hyperparameter alternating iteration (HAI) method to iteratively refine the bi-level model and obtain optimal planning and operational solutions. Results: We applied the proposed model to an IEEE-13 benchmark case study. The results demonstrated that the bi-level optimization approach effectively reduced three-phase unbalance in the rural distribution system while minimizing STS planning costs. Discussion: This innovative approach provides a cost-effective and efficient solution for mitigating three-phase unbalance in rural distribution networks, enhancing the feasibility of large-scale STS deployment. The integration of PESS further contributes to system stability, making this model a robust tool for future network planning.

Frontiers in Energy ResearchVol. 12
Northeast Electric Power University (CN), Jilin Electric Power Research Institute (China) (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Microgrid Control and Optimization
1.21
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