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Voltage regulation methods for active distribution networks considering the reactive power optimization of substations

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  • Ma, Wei
  • Wang, Wei
  • Chen, Zhe
  • Wu, Xuezhi
  • Hu, Ruonan
  • Tang, Fen
  • Zhang, Weige

Abstract

Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a voltage regulation method for ADNs to minimize the operational losses while keeping the nodal voltages within the limit with the reduced PV power curtailment and the reduced switching numbers of on-load tap changers (OLTCs) and capacitor banks (CBs). Meanwhile, the proposed voltage regulation method also aims to minimize the reactive power flowing through OLTCs, and to minimize the switching numbers of substation CBs. In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. The MINP model is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical decision-making algorithm is developed to select the best solution from the Pareto optimal set. Moreover, the decentralized voltage regulation aims at mitigating real-time nodal voltage variations via adjusting the real-time active and reactive power of each PV plant. Several simulations and comparisons are carried out on a modified IEEE 33-node system to verify the effectiveness of the proposed methods, and to compare with some previous voltage regulation methods. Simulation results show that the proposed voltage regulation methods can not only effectively control voltage variations of ADNs but also improve the economics of ADNs, substations, and PV plants.

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  • Ma, Wei & Wang, Wei & Chen, Zhe & Wu, Xuezhi & Hu, Ruonan & Tang, Fen & Zhang, Weige, 2021. "Voltage regulation methods for active distribution networks considering the reactive power optimization of substations," Applied Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:appene:v:284:y:2021:i:c:s0306261920317293
    DOI: 10.1016/j.apenergy.2020.116347
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    References listed on IDEAS

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    Cited by:

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    3. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    4. Tianhao Song & Xiaoqing Han & Baifu Zhang, 2021. "Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints," Energies, MDPI, vol. 14(21), pages 1-20, November.
    5. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    6. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    7. Daiva Stanelytė & Virginijus Radziukynas, 2022. "Analysis of Voltage and Reactive Power Algorithms in Low Voltage Networks," Energies, MDPI, vol. 15(5), pages 1-26, March.
    8. 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).
    9. Adetunji, Kayode E. & Hofsajer, Ivan W. & Abu-Mahfouz, Adnan M. & Cheng, Ling, 2022. "An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks," Applied Energy, Elsevier, vol. 322(C).
    10. Hu, Yusha & Man, Yi, 2022. "Two-stage energy scheduling optimization model for complex industrial process and its industrial verification," Renewable Energy, Elsevier, vol. 193(C), pages 879-894.
    11. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    12. Paweł Kelm & Rozmysław Mieński & Irena Wasiak, 2024. "Modular PV System for Applications in Prosumer Installations with Uncontrolled, Unbalanced and Non-Linear Loads," Energies, MDPI, vol. 17(7), pages 1-13, March.
    13. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2023. "Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks," Energies, MDPI, vol. 17(1), pages 1-24, December.

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