Investigating the Effect of Shading on the Capacity Factor of Floating Photovoltaic Systems

Yıl 2022, Cilt: 18 Sayı: 3, 309 - 319, 29.09.2022

Ali Murat Ateş Osman Salih Yılmaz Fatih Gülgen

https://doi.org/10.18466/cbayarfbe.1020070

Cited By: 1

Öz

In this study, remote sensing (RS) was used to determine the 20-year area and shoreline changes of Demirköprü Dam reservoir. Using a geographical information system (GIS) solar analysis tool, annual and monthly total global horizontal irradiance (GHI) values were calculated within the area of the reservoir’s shorelines based on 20-years of observations. The regional theoretical capacity factor (RTCF) proposed in this study was modelled using total annual GHI values. The water surface was divided into four regions using RTCFs 94.97%, 4.92%, 0.08%, and 0.02% of the total water surface area were classified as RTCF21, RTCF20, RTCF19, and RTCF18, respectively. The annual electrical energy potentials per unit for each RTCF were calculated. The novel method developed in this study for determining the optimum location of FPV SPPs to be installed on water surfaces reveals the importance of evaluating land topography and considering annual patterns of shading.

Anahtar Kelimeler

Floating photovoltaic, geographical information system, regional theoretical capacity factor, remote sensing, solar analysis

Kaynakça

• C. Wang et al., “Evaluation of energy and environmental performances of Solar Photovoltaic-based Targeted Poverty Alleviation Plants in China,” Energy Sustain. Dev., vol. 56, pp. 73–87, 2020.
• A. H. Al-Badi, “Measured performance evaluation of a 1.4 kW grid connected desert type PV in Oman,” Energy Sustain. Dev., vol. 47, pp. 107–113, Dec. 2018, doi: 10.1016/J.ESD.2018.09.007.
• M. Ogeya, C. Muhoza, and O. W. Johnson, “Integrating user experiences into mini-grid business model design in rural Tanzania,” Energy Sustain. Dev., vol. 62, pp. 101–112, Jun. 2021, doi: 10.1016/j.esd.2021.03.011.
• F. R. Martins, E. B. Pereira, and S. L. Abreu, “Satellite-derived solar resource maps for Brazil under SWERA project,” Sol. Energy, vol. 81, no. 4, pp. 517–528, 2007, doi: 10.1016/j.solener.2006.07.009.
• S. Pasalic, A. Aksamovic, and S. Avdakovic, “Floating photovoltaic plants on artificial accumulations — Example of Jablanica Lake,” in 2018 IEEE International Energy Conference (ENERGYCON), Jun. 2018, pp. 1–6, doi: 10.1109/ENERGYCON.2018.8398765.
• S. Bensehla, Y. Lazri, and M. C. Brito, “Solar potential of urban forms of a cold semi-arid city in Algeria in the present and future climate,” Energy Sustain. Dev., vol. 62, pp. 151–162, Jun. 2021, doi: 10.1016/j.esd.2021.04.004.
• A. El Hammoumi, A. Chalh, A. Allouhi, S. Motahhir, A. El Ghzizal, and A. Derouich, “Design and construction of a test bench to investigate the potential of floating PV systems,” J. Clean. Prod., vol. 278, p. 123917, 2021, doi: 10.1016/j.jclepro.2020.123917.
• A. P. Sukarso and K. N. Kim, “Cooling effect on the floating solar PV: Performance and economic analysis on the case of west Java province in Indonesia,” Energies, vol. 13, no. 9, 2020, doi: 10.3390/en13092126.
• M. S. M. Azmi, M. Y. H. Othman, M. H. H. Ruslan, K. Sopian, and Z. A. A. Majid, “Study on electrical power output of floating photovoltaic and conventional photovoltaic,” in AIP Conference Proceedings, 2013, pp. 95–101, doi: 10.1063/1.4858636.
• H. Bahaidarah, A. Subhan, P. Gandhidasan, and S. Rehman, “Performance evaluation of a PV (photovoltaic) module by back surface water cooling for hot climatic conditions,” Energy, vol. 59, pp. 445–453, 2013, doi: 10.1016/j.energy.2013.07.050.
• S. K. Yadav and U. Bajpai, “Performance evaluation of a rooftop solar photovoltaic power plant in Northern India,” Energy Sustain. Dev., vol. 43, pp. 130–138, 2018, doi: 10.1016/j.esd.2018.01.006.
• D. B. Riffel, J. A. dos Santos Junior, and A. L. de Moraes Costa, “Optimum operational conditions of hybrid photovoltaic-thermal systems,” Energy Sustain. Dev., vol. 60, pp. 26–32, 2021.
• R. Nagananthini and R. Nagavinothini, “Investigation on floating photovoltaic covering system in rural Indian reservoir to minimize evaporation loss,” Int. J. Sustain. Energy, vol. 0, no. 0, pp. 1–25, 2021, doi: 10.1080/14786451.2020.1870975.
• G. M. Tina, F. Bontempo Scavo, L. Merlo, and F. Bizzarri, “Comparative analysis of monofacial and bifacial photovoltaic modules for floating power plants,” Appl. Energy, vol. 281, no. January 2020, p. 116084, 2021, doi: 10.1016/j.apenergy.2020.116084.
• H. Rauf, M. S. Gull, and N. Arshad, “Integrating Floating Solar PV with Hydroelectric Power Plant: Analysis of Ghazi Barotha Reservoir in Pakistan,” Energy Procedia, vol. 158, pp. 816–821, Feb. 2019, doi: 10.1016/j.egypro.2019.01.214.
• S. Sulaeman, E. Brown, R. Quispe-Abad, and N. Müller, “Floating PV system as an alternative pathway to the amazon dam underproduction,” Renew. Sustain. Energy Rev., vol. 135, no. July 2020, p. 110082, 2021, doi: 10.1016/j.rser.2020.110082.
• I. S. Rodrigues, G. L. B. Ramalho, and P. H. A. Medeiros, “Potential of floating photovoltaic plant in a tropical reservoir in Brazil,” J. Environ. Plan. Manag., vol. 63, no. 13, pp. 2334–2356, Nov. 2020, doi: 10.1080/09640568.2020.1719824.
• M. Arekhi, C. Goksel, F. Balik Sanli, and G. Senel, “Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest,” ISPRS Int. J. Geo-Information, vol. 8, no. 2, p. 56, 2019, doi: 10.3390/ijgi8020056.
• X. Yang, Q. Qin, P. Grussenmeyer, and M. Koehl, “Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery,” Remote Sens. Environ., vol. 219, pp. 259–270, 2018, doi: 10.1016/j.rse.2018.09.016.
• X. Yang, S. Zhao, X. Qin, N. Zhao, and L. Liang, “Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening,” Remote Sens., vol. 9, no. 6, pp. 1–19, 2017, doi: 10.3390/rs9060596.
• N. N. Patel et al., “Multitemporal settlement and population mapping from landsatusing google earth engine,” Int. J. Appl. Earth Obs. Geoinf., vol. 35, no. PB, pp. 199–208, 2015, doi: 10.1016/j.jag.2014.09.005.
• J. Xiong et al., “Automated cropland mapping of continental Africa using Google Earth Engine cloud computing,” ISPRS J. Photogramm. Remote Sens., vol. 126, pp. 225–244, 2017, doi: 10.1016/j.isprsjprs.2017.01.019.
• R. Goldblatt, W. You, G. Hanson, and A. K. Khandelwal, “Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in google earth engine,” Remote Sens., vol. 8, no. 8, 2016, doi: 10.3390/rs8080634.
• Y. Choi, “Solar Power System Planning and Design,” Appl. Sci., vol. 10, no. 1, p. 367, Jan. 2020, doi: 10.3390/app10010367.
• F. M. Kouhestani, J. Byrne, D. Johnson, L. Spencer, P. Hazendonk, and B. Brown, “Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada,” Int. J. Energy Environ. Eng., vol. 10, no. 1, pp. 13–32, 2019, doi: 10.1007/s40095-018-0289-1.
• Y. Charabi and A. Gastli, “GIS assessment of large CSP plant in Duqum, Oman,” Renew. Sustain. Energy Rev., vol. 14, no. 2, pp. 835–841, 2010.
• J. Hofierka and M. Suri, “The solar radiation model for Open source GIS: implementation and applications,” in Proceedings of the Open source GIS-GRASS users conference, 2002, vol. 2002, pp. 51–70.
• L. Bergamasco and P. Asinari, “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, vol. 85, no. 11, pp. 2741–2756, 2011, doi: 10.1016/j.solener.2011.08.010.
• G. Desthieux et al., “Solar Energy Potential Assessment on Rooftops and Facades in Large Built Environments Based on LiDAR Data, Image Processing, and Cloud Computing. Methodological Background, Application, and Validation in Geneva (Solar Cadaster),” Front. Built Environ., vol. 4, p. 14, 2018, doi: 10.3389/fbuil.2018.00014.
• R. Kassner, W. Koppe, T. Schüttenberg, and G. Bareth, “Analysis of the Solar Potential of Roofs By Using Official Lidar Data,” Archives, vol. XXXVII, no. Part B4, pp. 399–404, 2008, [Online]. Available: http://www.isprs.org/proceedings/XXXVII/congress/tc4.aspx.
• J. Lee and S. Zlatanova, “Solar radiation over the urban texture: LIDAR data and image processing techniques for environmental analysis at city scale,” in 3D Geo-information sciences, Springer, 2009, pp. 319–340.
• A. Strzalka, R. Strzalka, V. Coors, and R. Ulbrich, “Analysis of renewable energy using Gis-technology in residential areas,” Ann. Arid Zone, vol. 49, no. 3, pp. 333–348, 2010.
• R. Hippenstiel and J. R. Brownson, “Computing solar energy potential of urban areas using airborne LiDAR and orthoimagery,” in Proceedings of the National Solar Conference and World Renewable Energy Forum, 2012, vol. 3, pp. 2004–2008.
• C. Catita, P. Redweik, J. Pereira, and M. C. Brito, “Extending solar potential analysis in buildings to vertical facades,” Comput. Geosci., vol. 66, pp. 1–12, 2014.
• L. Ko, J. C. Wang, C. Y. Chen, and H. Y. Tsai, “Evaluation of the development potential of rooftop solar photovoltaic in Taiwan,” Renew. Energy, vol. 76, pp. 582–595, 2015, doi: 10.1016/j.renene.2014.11.077.
• N. Salimzadeh and A. Hammad, “High-Level Framework for GIS-Based Optimization of Building Photovoltaic Potential at Urban Scale Using BIM and LiDAR,” 2017, doi: 10.1061/9780784481196.012.
• P. Gagnon, R. Margolis, J. Melius, C. Phillips, and R. Elmore, “Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling,” Environ. Res. Lett., vol. 13, no. 2, p. 24027, 2018.
• J. Liang, J. Gong, J. Zhou, A. N. Ibrahim, and M. Li, “An open-source 3D solar radiation model integrated with a 3D Geographic Information System,” Environ. Model. Softw., vol. 64, pp. 94–101, 2015, doi: 10.1016/j.envsoft.2014.11.019.
• L. Romero Rodríguez, E. Duminil, J. Sánchez Ramos, and U. Eicker, “Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach,” Sol. Energy, vol. 146, pp. 264–275, 2017, doi: 10.1016/j.solener.2017.02.043.
• Y. Charabi, M. B. H. Rhouma, and A. Gastli, “GIS-based estimation of roof-PV capacity & energy production for the Seeb region in Oman,” in 2010 IEEE International Energy Conference, 2010, pp. 41–44.
• G. Liu, W. Wu, Q. Ge, E. Dai, Z. Wan, and Y. Zhou, “GIS-based assessment of roof-mounted solar energy potential in Jiangsu, China,” in 2011 Second International Conference on Digital Manufacturing & Automation, 2011, pp. 565–571.
• J. Hofierka and M. Zlocha, “A New 3-D Solar Radiation Model for 3-D City Models,” Trans. GIS, vol. 16, no. 5, pp. 681–690, 2012, doi: 10.1111/j.1467-9671.2012.01337.x.
• M. R. Majid, J. A. Jamaludin, and W. Y. W. Ibrahim, “Estimation of residential impervious surface using GIS technique,” Plan. Malaysia, vol. 11, no. 2, pp. 23–38, 2013.
• L. Quiquerez, J. Faessler, B. M. Lachal, F. Mermoud, and P. Hollmuller, “GIS methodology and case study regarding assessment of the solar potential at territorial level: PV or thermal?,” Int. J. Sustain. Energy Plan. Manag., vol. 6, pp. 3–16, 2015.
• A. Chow, S. Li, and A. S. Fung, “Modeling urban solar energy with high spatiotemporal resolution: A case study in Toronto, Canada,” Int. J. Green Energy, vol. 13, no. 11, pp. 1090–1101, 2016, doi: 10.1080/15435075.2016.1170686.
• I. Sola et al., “Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes,” Int. J. Appl. Earth Obs. Geoinf., vol. 73, no. June, pp. 63–76, 2018, doi: 10.1016/j.jag.2018.05.020.
• J. Song and Y. Choi, “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, vol. 9, no. 2, p. 102, Feb. 2016, doi: 10.3390/en9020102.
• A. Sahu, N. Yadav, and K. Sudhakar, “Floating photovoltaic power plant: A review,” Renew. Sustain. Energy Rev., vol. 66, pp. 815–824, 2016, doi: 10.1016/j.rser.2016.08.051.
• M. Abid, Z. Abid, J. Sagin, R. Murtaza, D. Sarbassov, and M. Shabbir, “Prospects of floating photovoltaic technology and its implementation in Central and South Asian Countries,” Int. J. Environ. Sci. Technol., vol. 16, no. 3, pp. 1755–1762, 2019.
• N. Mattsson, V. Verendel, F. Hedenus, and L. Reichenberg, “An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions,” Energy Strateg. Rev., vol. 33, p. 100606, 2021, doi: 10.1016/j.esr.2020.100606.
• A. Goswami and P. K. Sadhu, “Degradation analysis and the impacts on feasibility study of floating solar photovoltaic systems,” Sustain. Energy, Grids Networks, vol. 26, p. 100425, 2021, doi: 10.1016/j.segan.2020.100425.
• P. A. Adedeji, S. A. Akinlabi, N. Madushele, and O. O. Olatunji, “Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review,” J. Clean. Prod., vol. 269, p. 122104, 2020, doi: 10.1016/j.jclepro.2020.122104.
• P. A. Trotter, R. Maconachie, and M. C. McManus, “Solar energy’s potential to mitigate political risks: The case of an optimised Africa-wide network,” Energy Policy, vol. 117, no. March, pp. 108–126, 2018, doi: 10.1016/j.enpol.2018.02.013.
• P. Mather and B. Tso, Classification methods for remotely sensed data. CRC press, 2016.
• Y. Zhang, J. Gao, and J. Wang, “Detailed mapping of a salt farm from Landsat TM imagery using neural network and maximum likelihood classifiers: a comparison,” Int. J. Remote Sens., vol. 28, no. 10, pp. 2077–2089, May 2007, doi: 10.1080/01431160500406870.
• J. A. Richards, Remote Sensing Digital Image Analysis, 5th ed., vol. 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
• S. K. McFeeters, “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features,” Int. J. Remote Sens., vol. 17, no. 7, pp. 1425–1432, 1996, doi: 10.1080/01431169608948714.
• O. S. Yılmaz, F. Gülgen, F. B. Şanlı, and A. M. Ateş, “Demirköprü Barajının Su Yüzey Sınırlarının Belirlenmesinde Sentinel –2 (MSI) Görüntüleri Kullanılarak Farklı Algoritmalar ve Su Endeksleri Performanslarının Araştırılması’’,” 2019.
• A. M. Ateş, O. S. Yılmaz, and F. Gulgen, “Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface,” Sustain. Energy Technol. Assessments, vol. 41, no. 100799, pp. 1–12, 2020, doi: 10.1016/j.seta.2020.100799.
• R. Dubayah and P. M. Rich, “Topographic solar radiation models for GIS,” Int. J. Geogr. Inf. Syst., vol. 9, no. 4, pp. 405–419, Jul. 1995, doi: 10.1080/02693799508902046.
• H. Supe et al., “Google earth engine for the detection of soiling on photovoltaic solar panels in arid environments,” Remote Sens., vol. 12, no. 9, 2020, doi: 10.3390/RS12091466.
• P. Fu and P. M. Rich, “Design and Implementation of the Solar Analyst: an ArcView Extension for Modeling Solar Radiation at Landscape Scales,” in 19th Annual ESRI User Conference, 1999, no. February, pp. 1–24.
• M. Mwanza and K. Ulgen, GIS-Based Assessment of Solar Energy Harvesting Sites and Electricity Generation Potential in Zambia. 2021.
• E. Kymakis, S. Kalykakis, and T. M. Papazoglou, “Performance analysis of a grid connected photovoltaic park on the island of Crete,” Energy Convers. Manag., vol. 50, no. 3, pp. 433–438, 2009, doi: 10.1016/j.enconman.2008.12.009.
• H. A. Kazem, T. Khatib, K. Sopian, and W. Elmenreich, “Performance and feasibility assessment of a 1.4 kW roof top grid-connected photovoltaic power system under desertic weather conditions,” Energy Build., vol. 82, pp. 123–129, 2014, doi: 10.1016/j.enbuild.2014.06.048.
• A. M. Ates and H. Singh, “Rooftop solar Photovoltaic (PV) plant – One year measured performance and simulations,” J. King Saud Univ. - Sci., vol. 33, no. 3, p. 101361, 2021, doi: 10.1016/j.jksus.2021.101361.
• A. Gerbo, K. V. Suryabhagavan, and T. Kumar Raghuvanshi, “GIS-based approach for modeling grid-connected solar power potential sites: a case study of East Shewa Zone, Ethiopia,” Geol. Ecol. Landscapes, vol. 00, no. 00, pp. 1–15, 2020, doi: 10.1080/24749508.2020.1809059.
• E. Ghiani, F. Pilo, and S. Cossu, “Evaluation of photovoltaic installations performances in Sardinia,” Energy Convers. Manag., vol. 76, pp. 1134–1142, Dec. 2013, doi: 10.1016/j.enconman.2013.09.012.