##plugins.themes.bootstrap3.article.main##

Meenakshi Mothi Kumar K. E. Nisha Kumari

Abstract

Solar energy is one of the best sustainable forms of renewable energy. India has a cumulative installed capacity of 9.23 GW of grid-connected solar power and set an ambitious target of attaining 100 GW of solar capacity by 2022, including 40 GW of grid-connected rooftop solar installations well. The present study demonstrates the Geospatial technology to estimate the potential of solar photovoltaic on the rooftops of Karnal city. The satellite data of Sentinel -2 and World View-II data was interpreted so as to extract the building footprints.  Digital Elevation Model (DEM) data derived from ALOS (Advanced Land Observing Satellite) PALSAR (2019) data was used to calculate the Global Horizontal Irradiance (GHI). It was calculated that the average annual GHI varied between 0.79-5.9 kWh/m2/day. The study revealed maximum GHI (462 kWh/m2) was recorded during the monsoon season. It was estimated that the seasonal energy generation capacity in urban area was minimum (268.4MWh) in the winter season, while the maximum (2632.4MWh) energy generation capacity was observed during the monsoon season. In the case of the industrial area, the minimum seasonal energy generation capacity was found to be 23.9 MWh in winter while the maximum of 234.8 MWh during the monsoon season. If solar panels installed on the proposed rooftops, an estimated energy potential of 5.9 GWh would be generated in the study area.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

##plugins.themes.bootstrap3.article.details##

##plugins.themes.bootstrap3.article.details##

Keywords

Geospatial Technology, Global Horizontal Irradiance, Photovoltaic, Rooftop

References
Assouline, D., Mohajeri, N., & Scartezzini, J. L. (2017). Quantifying rooftop photovoltaic solar energy potential: A machine learning approach. Solar Energy, 141, 278-296. http://dx.doi.org/10.1016/j.solener.2016.11.045
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). Sol. Energy, 85 (11), 2741–2756. https://doi.org/10.1016/j.solener.2011.08.010
Brito, M. C., Redweik, P., Catita, C., Freitas, S., & Santos, M. (2019). 3D solar potential in the urban environment: A case study in lisbon. Energies, 12 (18), 3457. https://doi.org/10.3390/en12183457
Central Electricity Authority (2019), Central Electricity Authority Government of India Ministry of Power. https://cea.nic.in/old/reports/annual/annualreports /annual_report-2020.pdf
Charfi, W., Chaabane, M., Mhiri, H., Bournot, P., (2018). Performance evaluation of a solar photovoltaic system. Energy Rep., 4, 400–406. https://doi.org/10.1016/j. egyr.2 018.06.004.
Cheng, L., Zhang, F., Li, S., Mao, J., Xu, H., Ju, W. & Li, M. (2020). Solar energy potential of urban buildings in 10 cities of China. Energy, 196, 117038. https://doi.org/1 0.1016/j.energy.2020.117038
Firozjaei, M.K., Nematollahi, O., Mijani, N., Shorabeh, S.N., Firozjaei, H.K., & Toomanian, A. (2019). An integrated GIS-based Ordered Weighted averaging analysis for solar energy evaluation in Iran: current conditions and future planning. Renew. Energy 136, 1130–1146. https://doi.org/10.1016/j.renene.2018.09.090.
Gastli, A., & Charabi, Y. (2010). Solar electricity prospects in Oman using GIS-based solar radiation maps. Renewable and Sustainable Energy Reviews, 14(2), 790-797. doi:10.1016/j.rser.2009.08.018
Gholami, A., Khazaee, I., Eslami, S., Zandi, M., & Akrami, E., (2018). Experimental investigation of dust deposition effects on photo-voltaic output performance. Sol. Energy 159, 346–352. https://doi.org/10.1016/j.solener.20 17.11.0 10.
Kausika, B. B., & van Sark, W. G. (2021). Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands. Energies, 14(7), 1865. doi.org/10.3390/en14071865
Kapoor, K., Pandey, K.K., Jain, A.K., & Nandan, A. (2014). Evolution of solar energy in India: a review. Renew. Sustain. Energy Rev., 40, 475–487. https://doi.org/10.1016/j. rser.2014.07.118.
Lashof, D.A. & Ahuja, D.R. (1990) Relative Contributions of Greenhouse Gas Emissions to Global Warming. Nature, 344, 529-531. http://dx.doi.org/10.1038/344529a0
Luqman, M., Ahmad, S. R., Khan, S., Ahmad, U., Raza, A., & Akmal, F. (2015). Estimation of solar energy potential from rooftop of Punjab government servants cooperative housing society Lahore using GIS. Smart Grid and Renewable Energy, 6(05), 128. http://www.scirp.org/journal/sgre http://dx.doi.org/10.4236/sgre.2015.65012
Mahtta, R., Joshi, P. K., & Jindal, A. K. (2014). Solar power potential mapping in India using remote sensing inputs and environmental parameters. Renewable Energy, 71, 255-262. https://doi.org/10.1016/j.renene.2014.05.037
Mann, C.C., (2015). Solar or Coal? the Energy India Picks May Decide Earth’s Fate. Wired. Retrieved from. https://www.wired.com/2015/11/climate-cha nge-in-india/.
Mattoni, B., Pagliaro, F., Gugliermetti, L., Bisegna, F., & Cellucci, L. (2015). A territorial based strategy for the distribution of sensor networks in smart cities. IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), 653–658. https://doi.org/10.1109/EEEIC.2015.7165242
Meenakshi, Kumar, K. E., & Kumari, N. (2019). Estimation of Photovoltaic Energy Potential and Reduced Carbon Emission through Geo-Spatial Technology–A Case Study of Karnal City, Haryana (India).In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India, 2213-2219.
Mishra, T., Rabha, A., Kumar, U., Arunachalam, K., & Sridhar, V. (2020). Assessment of solar power potential in a hill state of India using Remote Sensing and Geographic Information System. Remote Sensing Applications: Society and Environment, 19, 100370. https://doi.org/10.1016/j.rsase.2020.100370
Ministry of New and Renewable Energy (2017). Ministry of New and Renewable Energy (MNRE), Government of India, New Delhi. www.mnre.gov.in.
Nelson, J. R., & Grubesic, T. H. (2020). The use of LiDAR versus unmanned aerial systems (UAS) to assess rooftop solar energy potential. Sustainable Cities and Society, 61, 102353. https://doi.org/10.1016/j.scs.2020.102353
Noussan, M., & Nastasi, B. (2018). Data analysis of heating systems for buildings - A tool for energy planning, policies and systems simulation. Energies, 11(1), 233. https://doi.org/10.3390/en11010233
Sharma, S., Jain, G., Mishra, S., & Bhattacharya, B., (2018). Assessment of roof-top solar energy potential in proposed smart cities of india. Conference: Asian Conference of Remote Sensing, New Delhi, India.(PDF) Assessment of Roof-Top Solar Energy Potential In Proposed Smart Cities Of India (researchgate.net)
Singh, R., & Banerjee, R. (2015). Estimation of rooftop solar photovoltaic potential of a city. Solar Energy, 115, 589-602. https://doi.org/10.1016/j.solener.2015.03.016
Citation Format
How to Cite
Meenakshi, K. E., M. K. ., & Kumari, N. . (2021). A Geo-spatial approach for quantifying rooftop photovoltaic energy potential in Karnal smart city, Haryana - A case study . Journal of Applied and Natural Science, 13(2), 512 - 519. https://doi.org/10.31018/jans.v13i2.2665
More Citation Formats:
Section
Research Articles