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Developing an Integration of Smart-Inverter-Based Hosting-Capacity Enhancement in Dynamic Expansion Planning of PV-Penetrated LV Distribution Networks

Author

Listed:
  • Masoud Hamedi

    (Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Hossein Shayeghi

    (Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Seyedjalal Seyedshenava

    (Energy Management Research Center, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Amin Safari

    (Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 5375171379, Iran)

  • Abdollah Younesi

    (Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Nicu Bizon

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitest, Romania)

  • Vasile-Gabriel Iana

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitest, Romania)

Abstract

With the penetration of distributed energy resources (DERs), new network challenges arise that limit the hosting capacity of the network, which consequently makes the current expansion-planning models inadequate. Smart inverters as a promising tool can be utilized to enhance the hosting capacity. Therefore, in response to technical, economic, and environmental challenges, as well as government support for renewable resources, especially domestic solar resources located at the point of consumption, this paper is an endeavor to propose a smart-inverter-based low-voltage (LV) distribution expansion-planning model. The proposed model is capable of dynamic planning, where multiple periods are considered over the planning horizon. In this model, a distribution company (DISCO), as the owner of the network, intends to minimize the planning and operational costs. Optimal loading of transformers is considered, which is utilized to operate the transformers efficiently. Here, to model the problem, a mixed-integer nonlinear programming (MINLP) model is utilized. Using the GAMS software, the decision variables of the problem, such as the site and size of the installation of distribution transformers, and their service areas specified by the LV lines over the planning years, and the reactive power generation/absorption of the smart inverters over the years, seasons, and hours are determined. To tackle the operational challenges such as voltage control in the points of common coupling (PCC) and the limitations in the hosting capacity of the network for the maximized penetration level of PV cells, a smart-inverter model with voltage control capability in PCC points is integrated into the expansion-planning problem. Then, a two-stage procedure is proposed to integrate the reactive power exchange capability of smart inverters in the distribution expansion planning. Based on the simulations of a residential district with PV penetration, results show that by a 14.7% share of PV energy generation, the loss cost of LV feeders is reduced by 28.3%. Also, it is observed that by optimally making use of the reactive power absorption capability of the smart inverters, the hosting capacity of the network is increased by 50%.

Suggested Citation

  • Masoud Hamedi & Hossein Shayeghi & Seyedjalal Seyedshenava & Amin Safari & Abdollah Younesi & Nicu Bizon & Vasile-Gabriel Iana, 2023. "Developing an Integration of Smart-Inverter-Based Hosting-Capacity Enhancement in Dynamic Expansion Planning of PV-Penetrated LV Distribution Networks," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11183-:d:1196545
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    References listed on IDEAS

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    1. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Vijayan, Vineeth & Mohapatra, Abheejeet & Singh, S.N., 2021. "Demand Response with Volt/Var Optimization for unbalanced active distribution systems," Applied Energy, Elsevier, vol. 300(C).
    3. Rajabi, A. & Elphick, S. & David, J. & Pors, A. & Robinson, D., 2022. "Innovative approaches for assessing and enhancing the hosting capacity of PV-rich distribution networks: An Australian perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Yih-Der Lee & Wei-Chen Lin & Jheng-Lun Jiang & Jia-Hao Cai & Wei-Tzer Huang & Kai-Chao Yao, 2021. "Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm," Energies, MDPI, vol. 14(24), pages 1-22, December.
    5. 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).
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