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A Graph-Based Genetic Algorithm for Distributed Photovoltaic Cluster Partitioning

Author

Listed:
  • Zhu Liu

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China
    Current address: China Southern Power Grid Research Technology Co., Ltd., Guangzhou 510663, China.)

  • Wenshan Hu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Guowei Guo

    (Foshan Power Supply Bureau, Guangdong Electric Power Co., Ltd., Foshan 528000, China)

  • Jinfeng Wang

    (Electric Power Science Research Institute, Guangdong Electric Power Co., Ltd., Guangzhou 510080, China)

  • Lingfeng Xuan

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China)

  • Feiwu He

    (Qingyuan Yingde Power Supply Bureau, Guangdong Electric Power Co., Ltd., Yingde 513000, China)

  • Dongguo Zhou

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

To easily control distributed photovoltaic power stations and provide fast responses for their regulation, this paper proposes an optimal cluster partitioning method based on a graph-based genetic algorithm (GA). In this approach, a novel structure utilizing a graph model is designed for chromosomes, and enhancements are made to the selection, crossover, and mutation models of the evolutionary to generate a search population for dividing distributed photovoltaic (PV) power grids into clusters. Moreover, the modularity and active power balance degree of the classic Girvan–Newman algorithm are employed as optimal objectives to establish a basis and evaluation system for cluster partitioning. Additionally, a Simulink simulation platform is established for the IEEE 33-bus time-varying scenario to validate its performance. A comparative analysis with some classic PV cluster partitioning algorithms demonstrates that the proposed method can achieve a more accurate and stable division of distributed PV units.

Suggested Citation

  • Zhu Liu & Wenshan Hu & Guowei Guo & Jinfeng Wang & Lingfeng Xuan & Feiwu He & Dongguo Zhou, 2024. "A Graph-Based Genetic Algorithm for Distributed Photovoltaic Cluster Partitioning," Energies, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2893-:d:1413791
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    References listed on IDEAS

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    1. Pereira, Luan D.L. & Yahyaoui, Imene & Fiorotti, Rodrigo & de Menezes, Luíza S. & Fardin, Jussara F. & Rocha, Helder R.O. & Tadeo, Fernando, 2022. "Optimal allocation of distributed generation and capacitor banks using probabilistic generation models with correlations," Applied Energy, Elsevier, vol. 307(C).
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