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Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level

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  • Suomalainen, Kiti
  • Wang, Vincent
  • Sharp, Basil

Abstract

Solar power has been rapidly growing in New Zealand with the total installed capacity increasing eightfold over the last three years. Most of the growth has taken place in the residential sector. Auckland Council has a goal of powering the equivalent of 176 565 homes by solar photovoltaics by 2040. To assess this in number and size of solar installations we first need to assess the solar energy potential on Auckland rooftops. In this study we have used LiDAR data to develop a digital surface model of the city, including topography, buildings and trees. With this model a solar radiation tool has been used to calculate the annual solar radiation on each square meter of roof area, taking into account latitude, time of year, time of day, average climatic conditions, surface orientation and slope, and shading from nearby buildings and trees. Results show that some neighbourhoods are better suited for solar power deployment than others, due to the shape and orientation of roofs, and the absence of shading by nearby objects. The approach of this paper can be used to accurately estimate solar energy potential on existing building stock at the regional and municipal level, with direct application in policy design.

Suggested Citation

  • Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
    • Handle: RePEc:eee:renene:v:111:y:2017:i:c:p:463-475
    DOI: 10.1016/j.renene.2017.04.025
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    Cited by:

    1. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    2. Zhong, Qing & Nelson, Jake R. & Tong, Daoqin & Grubesic, Tony H., 2022. "A spatial optimization approach to increase the accuracy of rooftop solar energy assessments," Applied Energy, Elsevier, vol. 316(C).
    3. Guglielmina Mutani & Valeria Todeschi, 2021. "Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings," Energies, MDPI, vol. 14(13), pages 1-25, July.
    4. Ding, Feng & Yang, Jianping & Zhou, Zan, 2023. "Economic profits and carbon reduction potential of photovoltaic power generation for China's high-speed railway infrastructure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    5. Myeongchan Oh & Hyeong-Dong Park, 2019. "Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University," Energies, MDPI, vol. 12(17), pages 1-17, August.
    6. Sredenšek, Klemen & Štumberger, Bojan & Hadžiselimović, Miralem & Mavsar, Primož & Seme, Sebastijan, 2022. "Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method," Energy, Elsevier, vol. 242(C).
    7. Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    8. Meskiana Boulahia & Kahina Amal Djiar & Miguel Amado, 2021. "Combined Engineering—Statistical Method for Assessing Solar Photovoltaic Potential on Residential Rooftops: Case of Laghouat in Central Southern Algeria," Energies, MDPI, vol. 14(6), pages 1-16, March.
    9. Rehman, Naveed ur & Katebi, Milad & Shaikh, Faraz & Al Karim, Miftah, 2020. "Solar resource assessment of modern parking machines in an urban environment," Renewable Energy, Elsevier, vol. 149(C), pages 1406-1413.
    10. Oh, Myeongchan & Park, Hyeong-Dong, 2018. "A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping," Renewable Energy, Elsevier, vol. 126(C), pages 465-474.
    11. Zhang, Hengxu & Cao, Yongji & Zhang, Yi & Terzija, Vladimir, 2018. "Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data," Applied Energy, Elsevier, vol. 216(C), pages 172-182.
    12. Alhammami, Hasan & An, Heungjo, 2021. "Techno-economic analysis and policy implications for promoting residential rooftop solar photovoltaics in Abu Dhabi, UAE," Renewable Energy, Elsevier, vol. 167(C), pages 359-368.
    13. Gomez-Exposito, Antonio & Arcos-Vargas, Angel & Gutierrez-Garcia, Francisco, 2020. "On the potential contribution of rooftop PV to a sustainable electricity mix: The case of Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    14. Yildirim, Deniz & Büyüksalih, Gürcan & Şahin, Ahmet Duran, 2021. "Rooftop photovoltaic potential in Istanbul: Calculations based on LiDAR data, measurements and verifications," Applied Energy, Elsevier, vol. 304(C).
    15. Kahsar, Rudy, 2021. "The soft path revisited: Policies that drive decentralization of electric power generation in the contiguous U.S," Energy Policy, Elsevier, vol. 156(C).
    16. Lee, Minhyun & Hong, Taehoon & Jeong, Jaewook & Jeong, Kwangbok, 2018. "Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level," Energy, Elsevier, vol. 160(C), pages 213-224.
    17. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    18. Lee, Minhyun & Hong, Taehoon & Jeong, Kwangbok & Kim, Jimin, 2018. "A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity," Applied Energy, Elsevier, vol. 232(C), pages 640-656.
    19. Ngoc Thien Le & Watit Benjapolakul, 2019. "Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques," Energies, MDPI, vol. 12(16), pages 1-13, August.
    20. Wang, Yu & He, Jijiang & Chen, Wenying, 2021. "Distributed solar photovoltaic development potential and a roadmap at the city level in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    21. Shahryar Jafarinejad & Rebecca R. Hernandez & Sajjad Bigham & Bryan S. Beckingham, 2023. "The Intertwined Renewable Energy–Water–Environment (REWE) Nexus Challenges and Opportunities: A Case Study of California," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    22. Olivieri, Lorenzo & Caamaño-Martín, Estefanía & Sassenou, Louise-Nour & Olivieri, Francesca, 2020. "Contribution of photovoltaic distributed generation to the transition towards an emission-free supply to university campus: technical, economic feasibility and carbon emission reduction at the Univers," Renewable Energy, Elsevier, vol. 162(C), pages 1703-1714.
    23. 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.
    24. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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