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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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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Petinrin, J.O. & Shaabanb, Mohamed, 2016. "Impact of renewable generation on voltage control in distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 770-783.
    3. Howlader, Abdul Motin & Sadoyama, Staci & Roose, Leon R. & Chen, Yan, 2020. "Active power control to mitigate voltage and frequency deviations for the smart grid using smart PV inverters," Applied Energy, Elsevier, vol. 258(C).
    4. Wei Ma & Wei Wang & Xuezhi Wu & Ruonan Hu & Fen Tang & Weige Zhang, 2019. "Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
    5. Fotouhi Ghazvini, Mohammad Ali & Soares, João & Horta, Nuno & Neves, Rui & Castro, Rui & Vale, Zita, 2015. "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers," Applied Energy, Elsevier, vol. 151(C), pages 102-118.
    6. Chaudhary, Priyanka & Rizwan, M., 2018. "Voltage regulation mitigation techniques in distribution system with high PV penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3279-3287.
    7. Huda, A.S.N. & Živanović, R., 2017. "Large-scale integration of distributed generation into distribution networks: Study objectives, review of models and computational tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 974-988.
    8. 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.
    9. 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).
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    8. 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.
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    10. 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.
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    12. 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).
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    14. 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.
    15. Dong, Lei & Lin, Hao & Qiao, Ji & Zhang, Tao & Zhang, Shiming & Pu, Tianjiao, 2024. "A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning," Applied Energy, Elsevier, vol. 373(C).
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