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Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review

Author

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  • Jude Suchithra

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

  • Duane Robinson

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

  • Amin Rajabi

    (DIgSILENT Pacific, Sydney 2000, Australia)

Abstract

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed.

Suggested Citation

  • Jude Suchithra & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2371-:d:1084923
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    References listed on IDEAS

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    1. 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).
    2. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    3. Shi, Zhongtuo & Yao, Wei & Zeng, Lingkang & Wen, Jianfeng & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu, 2020. "Convolutional neural network-based power system transient stability assessment and instability mode prediction," Applied Energy, Elsevier, vol. 263(C).
    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. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    6. 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).
    7. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    8. Zubo, Rana.H.A. & Mokryani, Geev & Rajamani, Haile-Selassie & Aghaei, Jamshid & Niknam, Taher & Pillai, Prashant, 2017. "Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1177-1198.
    9. 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).
    10. Chathurangi, D. & Jayatunga, U. & Perera, S. & Agalgaonkar, A.P. & Siyambalapitiya, T., 2021. "Comparative evaluation of solar PV hosting capacity enhancement using Volt-VAr and Volt-Watt control strategies," Renewable Energy, Elsevier, vol. 177(C), pages 1063-1075.
    11. 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.
    12. 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.
    13. Nikita Tomin & Nikolai Voropai & Victor Kurbatsky & Christian Rehtanz, 2021. "Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
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    2. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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