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Impact of large scale PV deployment in the sizing of urban distribution transformers

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  • Freitas, Sara
  • Santos, Teresa
  • Brito, Miguel C.

Abstract

With the increasing deployment of solar systems in buildings in urban environments, a future scenario of high photovoltaic penetration is expected to produce impacts on the distribution grid. One of the challenges relates to the power balance at the power transformers, which might not have sufficient spare capacity to accommodate the solar electricity feed in. In this work, we estimate the power balance at the transformers in a small urban area of Lisbon, Portugal, considering full deployment of PV, installed on rooftops and building façades. The PV potential is estimated through two different approaches: the simplified Peak power method, which considers the typical peak power of a module and the available area, and the more labour-intensive Irradiance method that accounts for hourly time step solar irradiance and demand data or simulations.

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  • Freitas, Sara & Santos, Teresa & Brito, Miguel C., 2018. "Impact of large scale PV deployment in the sizing of urban distribution transformers," Renewable Energy, Elsevier, vol. 119(C), pages 767-776.
    • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:767-776
    DOI: 10.1016/j.renene.2017.10.096
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    References listed on IDEAS

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    1. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
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    Cited by:

    1. Wilson Pavón & Esteban Inga & Silvio Simani, 2019. "Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration," Sustainability, MDPI, vol. 11(6), pages 1-18, March.
    2. Tian, B. & Loonen, R.C.G.M. & Bognár, Á. & Hensen, J.L.M., 2022. "Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas," Renewable Energy, Elsevier, vol. 198(C), pages 804-824.
    3. Freitas, Jader de Sousa & Cronemberger, Joára & Soares, Raí Mariano & Amorim, Cláudia Naves David, 2020. "Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug," Renewable Energy, Elsevier, vol. 160(C), pages 1468-1479.
    4. Howlader, Abdul Motin & Sadoyama, Staci & Roose, Leon R. & Sepasi, Saeed, 2018. "Distributed voltage regulation using Volt-Var controls of a smart PV inverter in a smart grid: An experimental study," Renewable Energy, Elsevier, vol. 127(C), pages 145-157.
    5. Jing, Yifan & Zhu, Li & Yin, Baoquan & Li, Fangfang, 2023. "Evaluating the PV system expansion potential of existing integrated energy parks: A case study in North China," Applied Energy, Elsevier, vol. 330(PA).
    6. Qais Alsafasfeh & Omar A. Saraereh & Imran Khan & Sunghwan Kim, 2019. "Solar PV Grid Power Flow Analysis," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    7. Shi, Zhongming & Fonseca, Jimeno A. & Schlueter, Arno, 2021. "A parametric method using vernacular urban block typologies for investigating interactions between solar energy use and urban design," Renewable Energy, Elsevier, vol. 165(P1), pages 823-841.
    8. Lovati, Marco & Dallapiccola, Mattia & Adami, Jennifer & Bonato, Paolo & Zhang, Xingxing & Moser, David, 2020. "Design of a residential photovoltaic system: the impact of the demand profile and the normative framework," Renewable Energy, Elsevier, vol. 160(C), pages 1458-1467.

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