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A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks

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Listed:
  • Ji, Haoran
  • Wang, Chengshan
  • Li, Peng
  • Zhao, Jinli
  • Song, Guanyu
  • Ding, Fei
  • Wu, Jianzhong

Abstract

The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). The var capacity provided by DG inverters is a potential solution for voltage regulation. The conventional centralized control strategies limited by computation and communication burdens are difficult to meet the requirement of rapid voltage control. However, the local control strategies based on real-time measurements have a fast response to the frequent fluctuations of DG outputs. The performance of these local controllers depends on the tuning of the control parameters. This paper proposes a centralized-based method to determine the local voltage control strategies for DGs. A centralized parameter tuning model of control curves is built, in which Q-V and Pcurt-V curves of DG inverters are mathematically formulated based on piecewise linearization. The original mixed-integer nonlinear programming (MINLP) model is converted into an effectively solved mixed-integer second-order cone programming (MISOCP) model using convex relaxation. The potential benefits of DG inverters are explored to regulate both reactive and curtailed active power, based on local voltage measurements. Case studies on a modified PG&E 69-node distribution system are carried out to verify the effectiveness of the proposed method. Results show that power losses of ADNs are significantly reduced and voltage profiles are also improved.

Suggested Citation

  • Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:2024-2036
    DOI: 10.1016/j.apenergy.2018.07.065
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    Cited by:

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    4. 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.
    5. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    6. Wang, Rui & Li, Peng & Yu, Hao & Ji, Haoran & Xi, Wei & Wang, Chengshan, 2023. "Identification of critical uncertain factors of distribution networks with high penetration of photovoltaics and electric vehicles," Applied Energy, Elsevier, vol. 329(C).
    7. Stringer, Naomi & Haghdadi, Navid & Bruce, Anna & Riesz, Jenny. & MacGill, Iain, 2020. "Observed behavior of distributed photovoltaic systems during major voltage disturbances and implications for power system security," Applied Energy, Elsevier, vol. 260(C).
    8. 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).
    9. Kang, Wenfa & Chen, Minyou & Guan, Yajuan & Wei, Baoze & Vasquez Q., Juan C. & Guerrero, Josep M., 2022. "Event-triggered distributed voltage regulation by heterogeneous BESS in low-voltage distribution networks," Applied Energy, Elsevier, vol. 312(C).
    10. Wang, Licheng & Yan, Ruifeng & Saha, Tapan Kumar, 2019. "Voltage regulation challenges with unbalanced PV integration in low voltage distribution systems and the corresponding solution," Applied Energy, Elsevier, vol. 256(C).
    11. Bartłomiej Mroczek & Paweł Pijarski, 2021. "DSO Strategies Proposal for the LV Grid of the Future," Energies, MDPI, vol. 14(19), pages 1-19, October.
    12. Huy, Phung Dang & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao & Ekanayake, Janaka B., 2020. "Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage," Energy, Elsevier, vol. 195(C).
    13. Se-Heon Lim & Sung-Guk Yoon, 2022. "Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 15(23), pages 1-18, December.
    14. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
    15. Almasalma, Hamada & Claeys, Sander & Deconinck, Geert, 2019. "Peer-to-peer-based integrated grid voltage support function for smart photovoltaic inverters," Applied Energy, Elsevier, vol. 239(C), pages 1037-1048.
    16. Mohammed Alshehri & Jin Yang, 2024. "Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments," Energies, MDPI, vol. 17(5), pages 1-33, March.
    17. Murray, William & Adonis, Marco & Raji, Atanda, 2021. "Voltage control in future electrical distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).

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