Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies

Yıl 2023, Cilt: 8 Sayı: 1, 63 - 75, 15.02.2023

Osman Salih Yılmaz Fatih Gülgen Ali Murat Ateş

https://doi.org/10.26833/ijeg.1052556

Cited By: 5

Öz

This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.

Anahtar Kelimeler

Remote sensing, GIS, Google Earth Engine, Solar power plant, Solar analysis

Kaynakça

• Du, Z., Bin, L., Ling, F., Li, W., Tian, W., Wang, H., ... & Zhang, X. (2012). Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. Journal of Applied Remote Sensing, 6(1), 063609. https://doi.org/10.1117/1.jrs.6.063609
• Molden, D. J., Vaidya, R. A., Shrestha, A. B., Rasul, G., & Shrestha, M. S. (2014). Water infrastructure for the Hindu Kush Himalayas. International Journal of Water Resources Development, 30(1), 60-77.
• Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., & Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing, 8(4), 354. https://doi.org/10.3390/rs8040354
• Yang, X., & Chen, L. (2017). Evaluation of automated urban surface water extraction from Sentinel-2A imagery using different water indices. Journal of Applied Remote Sensing, 11(2), 026016.. https://doi.org/10.1117/1.JRS.11.026016
• Su, H., Peng, Y., Xu, C., Feng, A., & Liu, T. (2021). Using improved DeepLabv3+ network integrated with normalized difference water index to extract water bodies in Sentinel-2A urban remote sensing images. Journal of Applied Remote Sensing, 15(1), 018504.
• Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422. https://doi.org/10.1038/nature20584
• Arekhi, M., Goksel, C., Balik Sanli, F., & Senel, G. (2019). Comparative evaluation of the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for Igneada longos forest. ISPRS International Journal of Geo-Information, 8(2), 56. https://doi.org/10.3390/ijgi8020056
• Dehwah, A. H., Asif, M., & Rahman, M. T. (2018). Prospects of PV application in unregulated building rooftops in developing countries: A perspective from Saudi Arabia. Energy and Buildings, 171, 76-87. https://doi.org/10.1016/j.enbuild.2018.04.001
• Singh, A. K., Boruah, D., Sehgal, L., & Ramaswamy, A. P. (2019). Feasibility study of a grid-tied 2MW floating solar PV power station and e-transportation facility using ‘SketchUp Pro’for the proposed smart city of Pondicherry in India. Journal of Smart Cities, 2(2), 49-59. https://doi.org/10.18063/jsc.2016.02.004
• García-Pérez, S., Sierra-Pérez, J., & Boschmonart-Rives, J. (2018). Environmental assessment at the urban level combining LCA-GIS methodologies: A case study of energy retrofits in the Barcelona metropolitan area. Building and Environment, 134, 191-204. https://doi.org/10.1016/j.buildenv.2018.01.041
• Merrouni, A. A., Elalaoui, F. E., Mezrhab, A., Mezrhab, A., & Ghennioui, A. (2018). Large scale PV sites selection by combining GIS and Analytical Hierarchy Process. Case study: Eastern Morocco. Renewable energy, 119, 863-873.
• Yilmaz, S., Ozcalik, H. R., & Dincer, F. (2015). Remote detection and assessment of solar energy potential analysis based on available roof surface area: case study in Kahramanmaras, Turkey. Journal of Applied Remote Sensing, 9(1), 097698. https://doi.org/10.1117/1.jrs.9.097698
• Gagnon, P., Margolis, R., Melius, J., Phillips, C., & Elmore, R. (2018). Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling. Environmental Research Letters, 13, 1748–9326. https://doi.org/10.1088/1748-9326/aaa554
• Czirjak, D. W. (2017). Detecting photovoltaic solar panels using hyperspectral imagery and estimating solar power production. Journal of Applied Remote Sensing, 11(2), 026007. https://doi.org/10.1117/1.jrs.11.026007
• Abid, M., Abid, Z., Sagin, J., Murtaza, R., Sarbassov, D., & Shabbir, M. (2019). Prospects of floating photovoltaic technology and its implementation in Central and South Asian Countries. International Journal of Environmental Science and Technology, 16(3), 1755-1762. https://doi.org/10.1007/s13762-018-2080-5
• Pimentel Da Silva, G. D., & Branco, D. A. C. (2018). Is floating photovoltaic better than conventional photovoltaic? Assessing environmental impacts. Impact Assessment and Project Appraisal, 36(5), 390-400. https://doi.org/10.1080/14615517.2018.1477498
• Mansouri Kouhestani, F., Byrne, J., Johnson, D., Spencer, L., Hazendonk, P., & Brown, B. (2019). Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada. International Journal of Energy and Environmental Engineering, 10(1), 13-32. https://doi.org/10.1007/s40095-018-0289-1
• Ranjbaran, P., Yousefi, H., Gharehpetian, G. B., & Astaraei, F. R. (2019). A review on floating photovoltaic (FPV) power generation units. Renewable and Sustainable Energy Reviews, 110, 332-347. https://doi.org/10.1016/j.rser.2019.05.015
• Ates, A. M., Yilmaz, O. S., & Gulgen, F. (2020). Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface. Sustainable Energy Technologies and Assessments, 41, 100799. https://doi.org/10.1016/j.seta.2020.100799
• Song, J., & Choi, Y. (2016). Analysis of the potential for use of floating photovoltaic systems on mine pit lakes: case study at the ssangyong open-pit limestone mine in Korea. Energies, 9(2), 102.
• Charabi, Y., & Gastli, A. (2010). GIS assessment of large CSP plant in Duqum, Oman. Renewable and Sustainable Energy Reviews, 14(2), 835-841.
• Dubayah, R., & Rich, P. M. (1995). Topographic solar radiation models for GIS. International journal of geographical information systems, 9(4), 405-419.
• Zhang, Y., Gao, J., & Wang, J. (2007). Detailed mapping of a salt farm from Landsat TM imagery using neural network and maximum likelihood classifiers: a comparison. International Journal of Remote Sensing, 28(10), 2077-2089. https://doi.org/10.1080/01431160500406870
• Zurqani, H. A., Post, C. J., Mikhailova, E. A., & Allen, J. S. (2019). Mapping urbanization trends in a forested landscape using Google Earth Engine. Remote Sensing in Earth Systems Sciences, 2(4), 173-182.
• Patel, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., ... & Trianni, G. (2015). Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 35, 199-208. https://doi.org/10.1016/j.jag.2014.09.005
• Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., ... & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 225-244. https://doi.org/10.1016/j.isprsjprs.2017.01.019
• Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., ... & Moore III, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote sensing of environment, 185, 142-154. https://doi.org/10.1016/j.rse.2016.02.016
• Pekel, J. F., Cottam, A., Clerici, M., Belward, A., Dubois, G., Bartholome, E., & Gorelick, N. (2014, December). A Global Scale 30m Water Surface Detection Optimized and Validated for Landsat 8. In AGU Fall Meeting Abstracts (Vol. 2014, pp. H33P-01).
• Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., ... & Giri, C. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104-120. https://doi.org/10.1016/j.isprsjprs.2017.07.011
• Wang, C., Jia, M., Chen, N., & Wang, W. (2018). Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: A case study in the middle Yangtze River Basin. Remote Sensing, 10(10), 1635. https://doi.org/10.3390/rs10101635
• Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., ... & Meng, Q. (2019). Changes in water surface area during 1989–2017 in the Huai River Basin using Landsat data and Google earth engine. Remote Sensing, 11(15), 1824.
• Deng, Y., Jiang, W., Tang, Z., Ling, Z., & Wu, Z. (2019). Long-term changes of open-surface water bodies in the Yangtze River basin based on the Google Earth Engine cloud platform. Remote Sensing, 11(19), 2213. https://doi.org/10.3390/rs11192213
• Nguyen, U. N., Pham, L. T., & Dang, T. D. (2019). An automatic water detection approach using Landsat 8 OLI and Google Earth Engine cloud computing to map lakes and reservoirs in New Zealand. Environmental monitoring and assessment, 191(4), 1-12. https://doi.org/10.1007/s10661-019-7355-x
• Jena, R., Pradhan, B., & Jung, H. (2020). Seasonal water change assessment at Mahanadi River, India using multi-temporal data in Google earth engine. Korean Journal of Remote Sensing, 36, 1–13
• Bi, L., Fu, B. L., Lou, P. Q., & Tang, T. Y. (2020). Delineation water of pearl river basin using Landsat images from Google Earth Engine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 5-10. https://doi.org/10.5194/isprs-archives-XLII-3-W10-5-2020
• Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and sustainable energy reviews, 8(4), 365-381.
• Palmas, C., Abis, E., von Haaren, C., & Lovett, A. (2012). Renewables in residential development: an integrated GIS-based multicriteria approach for decentralized micro-renewable energy production in new settlement development: a case study of the eastern metropolitan area of Cagliari, Sardinia, Italy. Energy, Sustainability and Society, 2(1), 1-15. https://doi.org/10.1186/2192-0567-2-10
• Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews, 33, 772-781.
• Sahu, A., Yadav, N., & Sudhakar, K. (2016). Floating photovoltaic power plant: A review. Renewable and sustainable energy reviews, 66, 815-824. https://doi.org/10.1016/j.rser.2016.08.051
• Kumar, D. (2019). Mapping solar energy potential of southern India through geospatial technology. Geocarto International, 34(13), 1477-1495. https://doi.org/10.1080/10106049.2018.1494759
• Kokpinar, M. A., Kumcu, S. Y., Altan-Sakarya, A., & Gogus, M. (2010). Reservoir sedimentation in the Demirköprü Dam, Turkey. River Flow, 1125-1130.
• Landis, J. R., & Koch, G. G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 363-374.
• Duffie, J.A., & Beckman, W. A. (2013). Solar engineering of thermal processes. John Wiley & Sons
• Kumar, D. (2020). Satellite-based solar energy potential analysis for southern states of India. Energy Reports, 6, 1487-1500.
• Wate, P., & Saran, S. (2015). Implementation of CityGML energy application domain extension (ADE) for integration of urban solar potential indicators using object-oriented modelling approach. Geocarto International, 30(10), 1144-1162. https://doi.org/10.1080/10106049.2015.1034192
• Falklev, E. H. (2017). Mapping of solar energy potential on Tromsøya using solar analyst in ArcGIS (Master's thesis, UiT The Arctic University of Norway).
• Fu, P., & Rich, P. M. (1999, July). Design and implementation of the Solar Analyst: an ArcView extension for modeling solar radiation at landscape scales. In Proceedings of the nineteenth annual ESRI user conference (Vol. 1, pp. 1-31). USA: San Diego.
• Belhachat, F., & Larbes, C. (2021). PV array reconfiguration techniques for maximum power optimization under partial shading conditions: A review. Solar Energy, 230, 558-582. https://doi.org/10.1016/j.solener.2021.09.089
• Eke, R., & Demircan, C. (2015). Shading effect on the energy rating of two identical PV systems on a building façade. Solar Energy, 122, 48-57. https://doi.org/10.1016/j.solener.2015.08.022
• Mehedi, I. M., Salam, Z., Ramli, M. Z., Chin, V. J., Bassi, H., Rawa, M. J. H., & Abdullah, M. P. (2021). Critical evaluation and review of partial shading mitigation methods for grid-connected PV system using hardware solutions: The module-level and array-level approaches. Renewable and Sustainable Energy Reviews, 146, 111138. https://doi.org/10.1016/j.rser.2021.111138
• Saiprakash, C., Mohapatra, A., Nayak, B., & Ghatak, S. R. (2021). Analysis of partial shading effect on energy output of different solar PV array configurations. Materials Today: Proceedings, 39, 1905-1909. https://doi.org/10.1016/j.matpr.2020.08.307
• Seapan, M., Hishikawa, Y., Yoshita, M., & Okajima, K. (2020). Detection of shading effect by using the current and voltage at maximum power point of crystalline silicon PV modules. Solar Energy, 211, 1365-1372. https://doi.org/10.1016/j.solener.2020.10.078
• Yang, B., Ye, H., Wang, J., Li, J., Wu, S., Li, Y., ... & Ye, H. (2021). PV arrays reconfiguration for partial shading mitigation: Recent advances, challenges and perspectives. Energy Conversion and Management, 247, 114738. https://doi.org/10.1016/j.enconman.2021.114738
• Charabi, Y., Gastli, A., & Al-Yahyai, S. (2016). Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling Numerical Weather prediction model. Energy Reports, 2, 67-73. https://doi.org/10.1016/j.egyr.2016.05.001
• Gassar, A. A. A., & Cha, S. H. (2021). Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales. Applied Energy, 291, 116817. https://doi.org/10.1016/j.apenergy.2021.116817
• Kumar, D. (2021). Spatial variability analysis of the solar energy resources for future urban energy applications using Meteosat satellite-derived datasets. Remote Sensing Applications: Society and Environment, 22, 100481. https://doi.org/10.1016/j.rsase.2021.100481
• Oh, M., & Park, H. D. (2018). A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping. Renewable Energy, 126, 465-474. https://doi.org/10.1016/j.renene.2018.03.068
• Settou, B., Settou, N., Gahrar, Y., Negrou, B., Bouferrouk, A., Gouareh, A., & Mokhtara, C. (2022). Geographic information-driven two-stage optimization model for location decision of solar power plant: A case study of an Algerian municipality. Sustainable Cities and Society, 77, 103567. https://doi.org/10.1016/j.scs.2021.103567
• Kim, S. M., Oh, M., & Park, H. D. (2019). Analysis and prioritization of the floating photovoltaic system potential for reservoirs in Korea. Applied Sciences, 9(3), 395. https://doi.org/doi:10.3390/app9030395
• Lee, K. R., & Lee, W. H. (2016). Floating photovoltaic plant location analysis using GIS. Journal of Korean Society for Geospatial Information Science, 24(1), 51-59.