Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach

Year 2023, , 188 - 199, 05.07.2023

Vancho Adjiski , Gordana Kaplan , Stojance Mijalkovski

https://doi.org/10.26833/ijeg.1112274

Cited By: 8

Abstract

The importance of solar energy as a global energy source is expected to grow. Solar power's future looks bright, especially with an aged and deteriorating energy grid and rising fossil fuel prices. More precise methods for assessment of solar capacity are needed as more homes and companies investigate the possibility of small-scale photovoltaic (PV) solar installations. In this study, a spatial solar energy PV potential assessment method based on the combination of LiDAR (Light Detection and Ranging) datasets and GIS (Geographic Information System) is proposed. The proposed methodology is applied to an area in the capital city of Skopje in N. Macedonia, from where the results of the possible annual energy output of PV systems for the selected rooftops were presented. The results of the study were presented in a map showing rooftops that are most suitable for installing PV systems. From this map, three random roofs were selected to perform manual estimates of the number of panels that could fit on them and the potential energy output of the solar PV systems. This study provides crucial results for financial and urban planning, policy formulation for future energy projects and also allows to analyze different mechanisms to promote PV installations on publicly available rooftops.

Keywords

LiDAR, GIS, Solar irradiation, Photovoltaic (PV) potential, Rooftop

References

• 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, 41, 915‐931.
• Suri, M., Huld, T. A., Dunlop, E. D., & Ossenbrink, H. A. (2007). Potential of solar electricity generation in the European Union member states and candidate countries. Solar Energy, 81(10), 1295-1305. http://dx.doi.org/10.1016/j.solener.2006.12.007
• Nwaigwe, K.N., Mutabilwa, P., & Dintwa, E. (2019). An overview of solar power (PV systems) integration into electricity grids. Materials Science for Energy Technologies, 2(3), 629-633. https://doi.org/10.1016/j.mset.2019.07.002.
• Kåberger, T. (2018). Progress of renewable electricity replacing fossil fuels. Global Energy Interconnection, 1(1) 48-52. https://doi.org/10.14171/j.2096-5117.gei.2018.01.006.
• Koo, C., Hong, T., Park, H.S., & Yun, G. (2014). Framework for the analysis of the potential of the rooftop photovoltaic system to achieve the net‐zero energy solar buildings. Progress in photovoltaics: research and applications, 22(4), 462‐478. https://doi.org/10.1002/pip.2448
• United Nations, Department of Economic and Social Affairs, Population Division. (2014). World Urbanization Prospects: The 2014 Revision, Highlights, 32 p.
• Lu, Y., Khan, Z.A., Alvarez-Alvarado, M.S., Zhang, Y., Huang, Z., & Imran, M. A. (2020). Critical Review of Sustainable Energy Policies for the Promotion of Renewable Energy Sources. Sustainability, 12(12), 1-30. https://doi.org/10.3390/su12125078
• Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union L140. pp. 16–62.
• Hong, T., Lee, M., Koo, C., Jeong, K., & Kim, J. (2017). Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Applied Energy, 194, 320‐332. https://doi.org/10.1016/j.apenergy.2016.07.001
• Bergamasco, L., & Asinari, P. (2011). Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: further improvements by ortho‐image analysis and application to Turin (Italy). Solar Energy, 85(11), 2741‐2756. https://doi.org/10.1016/j.solener.2011.08.010
• Kodysh, J.B., Omitaomu, O.A., Bhaduri, B.L., & Neish, B.S. (2013). Methodology for estimating solar potential on multiple building rooftops for photovoltaic systems. Sustainable Cities and Society, 8, 31‐41. https://doi.org/10.1016/j.scs.2013.01.002
• Li, Y., Ding, D., Liu, C., & Wang, C. (2016). A pixel-based approach to estimation of solar energy potential on building roofs. Energy and Buildings, 129, 563-573. https://doi.org/10.1016/j.enbuild.2016.08.025
• Adeleke, A.K., & Smit, J.L. (2016). Intergration of LiDAR data with aerial imagery for estimating rooftop solar photovoltaic potentials in city of Cape Town. ISPRS ‐ International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 617‐624.
• Byrne, J., Taminiau, J., Kurdgelashvili, L., & Kim, K.N. (2015). A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of Seoul. Renewable and Sustainable Energy Reviews, Volume 41, 830-844. https://doi.org/10.1016/j.rser.2014.08.023.
• Lukač, N., Žlaus, D., Seme, S., Žalik, B., & Štumberger, G. (2013). Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data. Applied Energy, 102, 803‐812. https://doi.org/10.1016/j.apenergy.2012.08.042
• Jacques, D.A., Gooding, J., Giesekam, J.J., Tomlin, A.S., & Crook, R. (2014). Methodology for the assessment of PV capacity over a city region using low‐resolution LiDAR data and application to the City of Leeds (UK). Applied Energy, 124, 28‐34. https://doi.org/10.1016/j.apenergy.2014.02.076
• Suomalainen, K., Wang, V., & Sharp, B. (2017). Rooftop solar potential based on LiDAR data: bottom‐up assessment at neighbourhood level. Renewable Energy, 111, 463‐475. https://doi.org/10.1016/j.renene.2017.04.025
• Prieto, I., Izkara, J.L., & Usobiaga, E. (2019). The Application of LiDAR Data for the Solar Potential Analysis Based on Urban 3D Model. Remote Sensing, 11(20), 2348-2358. https://doi.org/10.3390/rs11202348
• https://www.katastar.gov.mk/
• European Union Joint Research Centre, “Typical Meteorological Year” (2017). [Online]. Available: https://re.jrc.ec.europa.eu/tmy.html (Date of access: 15 03 2022).
• Latif, Z. A., Zak,i N. A. M., & Salleh, S. A. (2012). GIS-based estimation of rooftop solar photovoltaic potential using LiDAR. 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, 2012, 388-392. https://doi.org/10.1109/CSPA.2012.6194755.
• Suri, M., & Hofierka, J. (2004). A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS, 8(2), 175-190. https://doi.org/10.1111/j.1467-9671.2004.00174.x
• Margolis, R., Gagnon, P., Melius, J., Phillips, C., & Elmore, R. (2017). Using GIS-based methods and LiDAR data to estimate rooftop solar technical potential in US cities. Environmental Research Letters, 12(7), 1-10. https://doi.org/10.1088/1748-9326/aa7225
• Palmer, D., Koumpli, E., Cole, I., Gottschalg, R., & Bettts, T. (2018). A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry. Energies, 11(12), 1-22. https://doi.org/10.3390/en11123506
• Rapidlasso GmbH LAStools. Available online: https://rapidlasso.com/lastools/ (Date of access: 15 03 2022).
• Lindberg, F., Grimmond, C., Gabey, A., Jarvi, L., Kent, C., Krave, N., Sun, T., Wallenberg, N., & Ward, H. (2019). Urban Multi-scale Environmental Predictor (UMEP) Manual. University of Reading UK, University of Gothenburg Sweden, SIMS China, [Online] Available: https://umep-docs.readthedocs.io. (Date of access: 15 03 2022).
• European Union Joint Reasearch Centre, Photovoltaic Geographical Reference System. (2017). [Online]. Available:https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html (Date of access: 15 03 2022).
• Melius, J., Margolis, R., & Ong, S. (2013). Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques. Golden (CO): National Renewable Energy Laboratory; 2013 December. Report No.: NREL/TP-6A20-60593.
• Chaudhari, M., Frantzis, L., & Hoff, T.E. (2004). PV Grid Connected Market Potential Under a Cost Breakthrough Scenario. EF-Final-September 2004-117373 (Chicago: Navigant Consulting).
• Frantzis, L., Graham, S., & Paidipati, J. (2007). California Rooftop Photovoltaic (PV) Resource Assessment and Growth Potential by County. CEC-500–2007-048 (Chicago: Navigant Consulting).
• Paidipati, J., Frantzis, L., Sawyer, H., & Kurrasch, A. (2008). Rooftop Photovoltaics Market Penetration Scenarios. NREL/SR- 581–42306 (Golden, CO: National Renewable Energy Laboratory). https://doi.org/10.2172/924645
• Ordonez, J., Jadraque, E., Alegre, J., & Martinez, G. (2010). Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain). Renewable and Sustainable Energy Reviews,14, 2122-2130. https://doi.org/10.1016/j.rser.2010.01.001
• Zhang, X., Walker, R., Salisbury, M., Hromiko, R., & Schreiber, J. (2009). Creating a Solar City: Determining the Potential of Solar Rooftop Systems in the City of Newark. Newark, DE: University of Delaware, Center for Energy and Environmental Policy.
• Lindberg, F., Jonsson, P., Honjo, T., & Wästberg, D. (2015). Solar energy on building envelopes – 3D modelling in a 2D environment. Solar Energy, 115, 369-378. https://doi.org/10.1016/j.solener.2015.03.001.
• Boyd, A. (2019). Mapping Solar PV Potential in Ambleside. Centre for Global Eco-Innovation, Joint report between CAfS and Lancaster University, 1-32.
• Senkal, E., Kaplan, G., & Avdan, U. (2021). Accuracy assessment of digital surface models from unmanned aerial vehicles’ imagery on archaeological sites. International Journal of Engineering and Geosciences, 6(2), 81-89.
• Diaz, B. S., Mata-Zayas, E. E., Gama-Campillo, L. M., Rincon-Ramirez, J. A., Vidal-Garcia, F., Rullan-Silva, C. D., & Sanchez-Gutierrez, F. (2022) LiDAR modeling to determine the height of shade canopy tree in cocoa agrosystems as available habitat for wildlife. International Journal of Engineering and Geosciences, 7(3), 283-293.
• Özdemir, S., Akbulut, Z., Karsli, F., & Acar, H. (2021). Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences, 6(1), 20-26.
• Sevgen, S. C. (2019). Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45-51.
• Özendi, M. (2022). Kültür varlıklarının yersel lazer tarama yöntemi ile dijital dokümantasyonu: Zonguldak Uzun Mehmet Anıtı örneği. Geomatik, 7 (2), 139-148. https://doi.org/10.29128/geomatik.917528
• Yakar, İ., Çelik, M. Ö., Hamal, S. N. G. & Bilgi, S. (2021). Kültürel Mirasın Dokümantasyonu Çalışmalarında Farklı Yazılımların Karşılaştırılması: Dikilitaş (Theodosius Obeliski) Örneği. Geomatik, 6 (3), 217-226. https://doi.org/10.29128/geomatik.761475
• Keleş, M. D. & Aydın, C. C. (2020). Mobil Lidar Verisi ile Kent Ölçeğinde Cadde Bazlı Envanter Çalışması ve Coğrafi Sistemleri Entegrasyonu-Ankara Örneği. Geomatik, 5 (3), 193-200. https://doi.org/10.29128/geomatik.643569