IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i10p1068-d81114.html
   My bibliography  Save this article

Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)

Author

Listed:
  • Sarawut Ninsawat

    (Remote Sensing and Geographic Information Systems (RS&GIS) FoS, Asian Institute of Technology (AIT), P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Mohammad Dalower Hossain

    (Environmental Engineering and Management (EEM) FoS, Asian Institute of Technology (AIT), P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

Abstract

In an urban area, the roof is the only available surface that can be utilized for installing solar photovoltaics (PV), and the active surface area depends on the type of roof. Shadows on a solar panel can be caused by nearby tall buildings, construction materials such as water tanks, or the roof configuration itself. The azimuth angle of the sun varies, based on the season and the time of day. Therefore, the simulation of shadow for one or two days or using the rule of thumb may not be sufficient to evaluate shadow effects on solar panels throughout the year. In this paper, a methodology for estimating the solar potential of solar PV on rooftops is presented, which is particularly applicable to urban areas. The objective of this method is to assess how roof type and shadow play a role in potentiality and financial benefit. The method starts with roof type extraction from high-resolution satellite imagery, using Object Base Image Analysis (OBIA), the generation of a 3D structure from height data and roof type, the simulation of shadow throughout the year, and the identification of potential and financial prospects. Based on the results obtained, the system seems to be adequate for calculating the financial benefits of solar PV to a very fine scale. The payback period varied from 7–13 years depending on the roof type, direction, and shadow impact. Based on the potentiality, a homeowner can make a profit of up to 200%. This method could help homeowners to identify potential roof area and economic interest.

Suggested Citation

  • Sarawut Ninsawat & Mohammad Dalower Hossain, 2016. "Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1068-:d:81114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/10/1068/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/10/1068/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stevanović, Sanja, 2013. "Optimization of passive solar design strategies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 177-196.
    2. Muhammad-Sukki, Firdaus & Ramirez-Iniguez, Roberto & Munir, Abu Bakar & Mohd Yasin, Siti Hajar & Abu-Bakar, Siti Hawa & McMeekin, Scott G. & Stewart, Brian G., 2013. "Revised feed-in tariff for solar photovoltaic in the United Kingdom: A cloudy future ahead?," Energy Policy, Elsevier, vol. 52(C), pages 832-838.
    3. Shafiullah, G.M. & Amanullah, M.T.O. & Shawkat Ali, A.B.M. & Jarvis, Dennis & Wolfs, Peter, 2012. "Prospects of renewable energy – a feasibility study in the Australian context," Renewable Energy, Elsevier, vol. 39(1), pages 183-197.
    4. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    5. 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.
    6. Jo, J.H. & Otanicar, T.P., 2011. "A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems," Renewable Energy, Elsevier, vol. 36(11), pages 2992-3000.
    7. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenyang Han & Meng Han & Menglong Zhang & Ying Zhao & Kai Xie & Yin Zhang, 2024. "Historic Building Renovation with Solar System towards Zero-Energy Consumption: Feasibility Analysis and Case Optimization Practice in China," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
    2. Dalibor Dobrilovic & Jasmina Pekez & Eleonora Desnica & Ljiljana Radovanovic & Ivan Palinkas & Milica Mazalica & Luka Djordjević & Sinisa Mihajlovic, 2023. "Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    3. Carlos Beltran-Velamazan & Marta Monzón-Chavarrías & Belinda López-Mesa, 2021. "A Method for the Automated Construction of 3D Models of Cities and Neighborhoods from Official Cadaster Data for Solar Analysis," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    4. Nima Monghasemi & Amir Vadiee & Konstantinos Kyprianidis & Elaheh Jalilzadehazhari, 2023. "Rank-Based Assessment of Grid-Connected Rooftop Solar Panel Deployments Considering Scenarios for a Postponed Installation," Energies, MDPI, vol. 16(21), pages 1-16, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    2. 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).
    3. Schallenberg-Rodríguez, Julieta, 2013. "Photovoltaic techno-economical potential on roofs in regions and islands: The case of the Canary Islands. Methodological review and methodology proposal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 219-239.
    4. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    5. Orioli, Aldo & Di Gangi, Alessandra, 2016. "Five-years-long effects of the Italian policies for photovoltaics on the energy demand coverage of grid-connected PV systems installed in urban contexts," Energy, Elsevier, vol. 113(C), pages 444-460.
    6. Calvert, K. & Pearce, J.M. & Mabee, W.E., 2013. "Toward renewable energy geo-information infrastructures: Applications of GIScience and remote sensing that build institutional capacity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 416-429.
    7. Zhang, Chen & Li, Zhixin & Jiang, Haihua & Luo, Yongqiang & Xu, Shen, 2021. "Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 283(C).
    8. Lukač, Niko & Seme, Sebastijan & Dežan, Katarina & Žalik, Borut & Štumberger, Gorazd, 2016. "Economic and environmental assessment of rooftops regarding suitability for photovoltaic systems installation based on remote sensing data," Energy, Elsevier, vol. 107(C), pages 854-865.
    9. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    10. Ruhang, Xu, 2016. "The restriction research for urban area building integrated grid-connected PV power generation potential," Energy, Elsevier, vol. 113(C), pages 124-143.
    11. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    12. Capellán-Pérez, Iñigo & de Castro, Carlos & Arto, Iñaki, 2017. "Assessing vulnerabilities and limits in the transition to renewable energies: Land requirements under 100% solar energy scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 760-782.
    13. Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Choi, Jun-Ki, 2018. "Improving the prediction performance of the finite element model for estimating the technical performance of the distributed generation of solar power system in a building façade," Applied Energy, Elsevier, vol. 215(C), pages 41-53.
    14. De Schepper, Ellen & Van Passel, Steven & Manca, Jean & Thewys, Theo, 2012. "Combining photovoltaics and sound barriers – A feasibility study," Renewable Energy, Elsevier, vol. 46(C), pages 297-303.
    15. Lukač, Niko & Seme, Sebastijan & Žlaus, Danijel & Štumberger, Gorazd & Žalik, Borut, 2014. "Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data," Energy, Elsevier, vol. 66(C), pages 598-609.
    16. Koo, Choongwan & Si, Ke & Li, Wenzhuo & Lee, JeeHee, 2022. "Integrated approach to evaluating the impact of feed-in tariffs on the life cycle economic performance of photovoltaic systems in China: A case study of educational facilities," Energy, Elsevier, vol. 254(PB).
    17. Orioli, Aldo & Di Gangi, Alessandra, 2014. "Review of the energy and economic parameters involved in the effectiveness of grid-connected PV systems installed in multi-storey buildings," Applied Energy, Elsevier, vol. 113(C), pages 955-969.
    18. Diane Palmer & Elena Koumpli & Ian Cole & Ralph Gottschalg & Thomas Betts, 2018. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry," Energies, MDPI, vol. 11(12), pages 1-22, December.
    19. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    20. Molin, Andreas & Schneider, Simon & Rohdin, Patrik & Moshfegh, Bahram, 2016. "Assessing a regional building applied PV potential – Spatial and dynamic analysis of supply and load matching," Renewable Energy, Elsevier, vol. 91(C), pages 261-274.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1068-:d:81114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.