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GIS-Based Distribution System Planning for New PV Installations

Author

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  • Pawita Bunme

    (Department of Electrical and Electronics Engineering, Kyushu Institute of Technology, 1-1, Sensui-cho, Tobata-ku, Fukuoka, Kitakyushu City 804-8550, Japan)

  • Shuhei Yamamoto

    (Department of Electrical and Electronics Engineering, Kyushu Institute of Technology, 1-1, Sensui-cho, Tobata-ku, Fukuoka, Kitakyushu City 804-8550, Japan)

  • Atsushi Shiota

    (Department of Information Technology, General Affairs Bureau City of Kitakyushu, 1-1, Jonai, Kokurakita-ku, Fukuoka, Kitakyushu City 803-8501, Japan)

  • Yasunori Mitani

    (Department of Electrical and Electronics Engineering, Kyushu Institute of Technology, 1-1, Sensui-cho, Tobata-ku, Fukuoka, Kitakyushu City 804-8550, Japan)

Abstract

Solar panel installations have increased significantly in Japan in recent decades. Due to this, world trends, such as clean/renewable energy, are being implemented in power systems all across Japan—particularly installations of photovoltaic (PV) panels in general households. In this work, solar power was estimated using solar radiation data from geographic information system (GIS) technology. The solar power estimation was applied to the actual distribution system model of the Jono area in Kitakyushu city, Japan. In this work, real power consumption data was applied to a real world distribution system model. We studied the impact of high installation rates of solar panels in Japanese residential areas. Additionally, we considered the voltage fluctuations in the distribution system model by assessing the impact of cloud shadows using a novel cloud movement simulation algorithm that uses real world GIS data. The simulation results revealed that the shadow from the cloud movement process directly impacted the solar power generation in residential areas, which caused voltage fluctuations of the overall distribution system. Thus, we advocate distribution system planning with a large number of solar panels.

Suggested Citation

  • Pawita Bunme & Shuhei Yamamoto & Atsushi Shiota & Yasunori Mitani, 2021. "GIS-Based Distribution System Planning for New PV Installations," Energies, MDPI, vol. 14(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3790-:d:581129
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    References listed on IDEAS

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    1. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.
    2. Ordóñez, J. & Jadraque, E. & Alegre, J. & Martínez, G., 2010. "Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2122-2130, September.
    3. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.
    4. Tzoumanikas, P. & Nikitidou, E. & Bais, A.F. & Kazantzidis, A., 2016. "The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system," Renewable Energy, Elsevier, vol. 95(C), pages 314-322.
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    Cited by:

    1. Vytautas Bocullo & Linas Martišauskas & Darius Pupeikis & Ramūnas Gatautis & Rytis Venčaitis & Rimantas Bakas, 2023. "UAV Photogrammetry Application for Determining the Influence of Shading on Solar Photovoltaic Array Energy Efficiency," Energies, MDPI, vol. 16(3), pages 1-19, January.
    2. La Guardia, Marcello & D'Ippolito, Filippo & Cellura, Maurizio, 2022. "A GIS-based optimization model finalized to the localization of new power-to-gas plants: The case study of Sicily (Italy)," Renewable Energy, Elsevier, vol. 197(C), pages 828-835.

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