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A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks

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  • Sadeghian, Hamidreza
  • Wang, Zhifang

Abstract

This paper proposes an impact-assessment framework to appropriately assess the impacts of two different types of distributed solar photovoltaic (DPV) installation on a realistic distribution network. To examine the spontaneous customer-based installations, a detailed Monte Carlo-based technique is introduced. For the controlled utility-based installation, a multi-objective optimization problem for the sizing and location of DPV installation is formulated aiming to improve energy loss, voltage deviation, and voltage fluctuation, avoiding reverse power flow and voltage violation. Solar insolation study is performed using light detection and ranging (LiDAR) data to estimate the potential of the network for DPV installation. In addition, a synthetic load profile modeling is developed to create yearly electricity load profiles for all the buildings in the given distribution network. The proposed framework is applied to a local distribution network with synthetic load profiles, Geographic Information System (GIS), and realistic solar insolation. It is found that for customer-based installation, voltage violation occurs beyond 30% of DPV penetration level, demonstrating the necessity of utility-based installation scheme for higher penetration ratios. Beyond a specific threshold, the probability of reverse power flow significantly increases in customer-based installation. Therefore, to achieve higher penetration ratios without reverse power flow and other negative impacts, utility-aided installation is necessary.

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  • Sadeghian, Hamidreza & Wang, Zhifang, 2020. "A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks," Renewable Energy, Elsevier, vol. 147(P1), pages 2179-2194.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:2179-2194
    DOI: 10.1016/j.renene.2019.09.117
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    Cited by:

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    2. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Vincent Umoh & Innocent Davidson & Abayomi Adebiyi & Unwana Ekpe, 2023. "Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review," Energies, MDPI, vol. 16(8), pages 1-25, April.
    4. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    5. Vergara, Pedro P. & Salazar, Mauricio & Mai, Tam T. & Nguyen, Phuong H. & Slootweg, Han, 2020. "A comprehensive assessment of PV inverters operating with droop control for overvoltage mitigation in LV distribution networks," Renewable Energy, Elsevier, vol. 159(C), pages 172-183.
    6. Mohammad Zain ul Abideen & Omar Ellabban & Luluwah Al-Fagih, 2020. "A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation," Energies, MDPI, vol. 13(11), pages 1-25, June.
    7. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2022. "Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control," Renewable Energy, Elsevier, vol. 198(C), pages 907-915.
    8. 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.

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