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Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Author

Listed:
  • Jude Suchithra

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

  • Amin Rajabi

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

  • Duane A. Robinson

    (Australian Power Quality Research Centre, University of Wollongong, Wollongong 2522, Australia)

Abstract

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

Suggested Citation

  • Jude Suchithra & Amin Rajabi & Duane A. Robinson, 2024. "Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control," Energies, MDPI, vol. 17(20), pages 1-27, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5037-:d:1495893
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    References listed on IDEAS

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    1. Jain, Akshay Kumar & Horowitz, Kelsey & Ding, Fei & Sedzro, Kwami Senam & Palmintier, Bryan & Mather, Barry & Jain, Himanshu, 2020. "Dynamic hosting capacity analysis for distributed photovoltaic resources—Framework and case study," Applied Energy, Elsevier, vol. 280(C).
    2. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
    3. Li, Peng & Ji, Haoran & Yu, Hao & Zhao, Jinli & Wang, Chengshan & Song, Guanyu & Wu, Jianzhong, 2019. "Combined decentralized and local voltage control strategy of soft open points in active distribution networks," Applied Energy, Elsevier, vol. 241(C), pages 613-624.
    4. Lee, Xian Yeow & Sarkar, Soumik & Wang, Yubo, 2022. "A graph policy network approach for Volt-Var Control in power distribution systems," Applied Energy, Elsevier, vol. 323(C).
    5. Kou, Peng & Liang, Deliang & Wang, Chen & Wu, Zihao & Gao, Lin, 2020. "Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks," Applied Energy, Elsevier, vol. 264(C).
    6. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
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    Cited by:

    1. Hua Zhan & Changxu Jiang & Zhen Lin, 2024. "A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy," Energies, MDPI, vol. 17(24), pages 1-19, December.
    2. Qianwen Dong & Xingyuan Song & Chunyang Gong & Chenchen Hu & Junfeng Rui & Tingting Wang & Ziyang Xia & Zhixin Wang, 2025. "Voltage Regulation Strategies in Photovoltaic-Energy Storage System Distribution Network: A Review," Energies, MDPI, vol. 18(11), pages 1-25, May.
    3. Md Tariqul Islam & M. Jahangir Hossain & Md. Ahasan Habib & Muhammad Ahsan Zamee, 2025. "Adaptive Hosting Capacity Forecasting in Distribution Networks with Distributed Energy Resources," Energies, MDPI, vol. 18(2), pages 1-25, January.
    4. Yalu Sun & Zhou Wang & Yongcheng Liu & Yi Jiang & Yalong Li, 2025. "Distributed PV Bearing Capacity Assessment Method Based on Source–Load Coupling Scenarios," Energies, MDPI, vol. 18(20), pages 1-22, October.
    5. Laurynas Šriupša & Mindaugas Vaitkūnas & Artūras Baronas & Gytis Svinkūnas & Julius Dosinas & Andrius Knyš & Saulius Gudžius & Audrius Jonaitis & Darius Serva, 2025. "Enhancing the Efficiency of Photovoltaic Power Flows Management in Three-Phase Prosumer Grids," Sustainability, MDPI, vol. 17(5), pages 1-21, March.

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