GIS-Driven Method for Site Feasibility Assessment of Large-Scale Solar Thermal Seawater Desalination: An Australian Case Study

Yingfei Huang; Amr Omar; David Saldivia; Robert A. Taylor; Greg Leslie

doi:10.52825/solarpaces.v2i.794

Authors

Yingfei Huang UNSW Sydney https://orcid.org/0000-0003-0816-976X
Amr Omar UNSW Sydney https://orcid.org/0000-0001-9265-8464
David Saldivia UNSW Sydney https://orcid.org/0000-0002-6703-8922
Robert A. Taylor UNSW Sydney
Greg Leslie UNSW Sydney

DOI:

https://doi.org/10.52825/solarpaces.v2i.794

Keywords:

Concentrated Solar Power, Multi-Effect Distillation, Geographical Information System, Techno-Economic Analysis, Site Selection

Abstract

Concentrated solar power (CSP) plants can be coupled with seawater desalination via Multi-Effect Distillation (MED) by recovering the cycle’s ‘free’ waste heat. However, project viability, based on the payback period, is contingent upon systematic consideration of climate variability, topography, water resources, markets, and natural hazards. This study describes a data-driven method for screening and then selecting optimal sites in Australia by integrating a Geographic Information System (GIS), System Advisor Model (SAM), MATLAB program, and a Multi-Criteria Decision-Making (MCDM) model. Results for potential sites based on only climate, topography, water resources, markets, and infrastructure identify approximately 2.13×10⁵ km² of land are suitable, granularly mainly located in the north-west and the south coastal regions with high solar resources (average direct normal irradiance (DNI) > 6 ). These regions encompass 56,000 km² and 25,100 km² of suitable areas, respectively, with potential payback periods as low as 12.2 years and 14.0 years. Queensland's northern coastal regions also show promise with a potential payback period of 13.4 years, but the suitable area is only 2,070 km² due to the marine protection areas in the eastern coastal zone. New South Wales faces hurdles due to topography and lower solar resources. Model results were consistent with the development of CSP installations in Australia, particularly, the Aurora facility in South Australia. This study provides a precise delineation of CSP-MED integration regions in Australia through the multi-dimensional analysis, offering insights into payback periods, and quantifying variable impacts on project geographical, technical, and economic feasibility.

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References

[1] T. Sarver, A. Al-Qaraghuli, and L. L. Kazmerski, ‘A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches’, Renewable and Sustainable Energy Reviews, vol. 22, pp. 698–733, Jun. 2013, doi: https://doi.org/10.1016/j.rser.2012.12.065.

[2] S. Bouaddi et al., ‘A Review of Conventional and Innovative- Sustainable Methods for Cleaning Reflectors in Concentrating Solar Power Plants’, Sustainability, vol. 10, no. 11, 2018, doi: https://doi.org/10.3390/su10113937.

[3] L. Sun et al., ‘A GIS-based multi-criteria decision-making method for the potential assessment and suitable sites selection of PV and CSP plants’, Resources, Conservation and Recycling, vol. 168, 2021, doi: https://doi.org/10.1016/j.resconrec.2020.105306.

[4] Y. Huang et al., ‘Can Concentrated Solar Power + Desalination Solve Rising Energy and Water Prices? A n Australian Site Feasibility Study’, in Proceedings of the Asia Pacific Solar Research Conference 2022, Australian PV Institute, Dec. 2022.

[5] Y. Huang, A. Omar, D. Saldivia, R. A. Taylor, and G. Leslie, ‘‪What Factors are Most Important in Determining Payback Period on Concentrated Solar Power+ Desalination Plants: A Sensitivity Analysis‬’, in Proceedings of the Asia Pacific Solar Research Conference 2022, Australian PV Institute, Dec. 2022.‬

[6] A. Omar, A. Nashed, Q. Li, G. Leslie, and R. A. Taylor, ‘Pathways for integrated concentrated solar power - Desalination: A critical review’, Renewable and Sustainable Energy Reviews, vol. 119, p. 109609, Mar. 2020, doi: https://doi.org/10.1016/j.rser.2019.109609.

[7] L. Dawson and P. Schlyter, ‘Less is more: Strategic scale site suitability for concentrated solar thermal power in Western Australia’, Energy Policy, vol. 47, pp. 91–101, Aug. 2012, doi: https://doi.org/10.1016/j.enpol.2012.04.025.

[8] A. Alami Merrouni, F. Elwali Elalaoui, A. Ghennioui, A. Mezrhab, and A. Mezrhab, ‘A GIS-AHP combination for the sites assessment of large-scale CSP plants with dry and wet cooling systems. Case study: Eastern Morocco’, Solar Energy, vol. 166, pp. 2–12, 2018, doi: https://doi.org/10.1016/j.solener.2018.03.038.

[9] S. A. Mohamed, ‘Application of geo-spatial Analytical Hierarchy Process and multi-criteria analysis for site suitability of the desalination solar stations in Egypt’, Journal of African Earth Sciences, vol. 164, 2020, doi: https://doi.org/10.1016/j.jafrearsci.2020.103767.

[10] 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’, Geology, Ecology, and Landscapes, vol. 6, no. 3, pp. 159–173, 2022, doi: https://doi.org/10.1080/24749508.2020.1809059.

[11] A. Yushchenko, A. de Bono, B. Chatenoux, M. K. Patel, and N. Ray, ‘GIS-based assessment of photovoltaic (PV) and concentrated solar power (CSP) generation potential in West Africa’, Renewable and Sustainable Energy Reviews, vol. 81, pp. 2088–2103, 2018, doi: https://doi.org/10.1016/j.rser.2017.06.021.

[12] A. T. A. Levosada, R. P. T. Ogena, J. R. V. Santos, and L. A. M. Danao, ‘Mapping of Suitable Sites for Concentrated Solar Power Plants in the Philippines Using Geographic Information System and Analytic Hierarchy Process’, Sustainability (Switzerland), vol. 14, no. 19, 2022, doi: https://doi.org/10.3390/su141912260.

[13] S. Ziuku, L. Seyitini, B. Mapurisa, D. Chikodzi, and K. van Kuijk, ‘Potential of Concentrated Solar Power (CSP) in Zimbabwe’, Energy for Sustainable Development, vol. 23, pp. 220–227, Dec. 2014, doi: https://doi.org/10.1016/j.esd.2014.07.006.

[14] Y. Charabi and A. Gastli, ‘PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation’, Renewable Energy, vol. 36, no. 9, pp. 2554–2561, Sep. 2011, doi: https://doi.org/10.1016/j.renene.2010.10.037.

[15] M. Uyan, ‘GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey’, Renewable and Sustainable Energy Reviews, vol. 28, pp. 11–17, Dec. 2013, doi: https://doi.org/10.1016/j.rser.2013.07.042.

[16] O. S. Ohunakin and B. O. Saracoglu, ‘A comparative study of selected multi-criteria decision-making methodologies for location selection of very large concentrated solar power plants in Nigeria’, African Journal of Science, Technology, Innovation and Development, vol. 10, no. 5, pp. 551–567, 2018, doi: https://doi.org/10.1080/20421338.2018.1495305.

[17] O. A. Omitaomu, N. Singh, and B. L. Bhaduri, ‘Mapping suitability areas for concentrated solar power plants using remote sensing data’, Journal of Applied Remote Sensing, vol. 9, no. 1, 2015, doi: https://doi.org/10.1117/1.JRS.9.097697.

[18] ‘Global model of cyclone wind 50, 100, 250, 500 and 1000 years return period - Humanitarian Data Exchange’. Accessed: May 23, 2023. [Online]. Available: https://data.humdata.org/dataset/cyclone-wind-100-years-return-period

[19] A. Omar, D. Saldivia, Q. Li, R. Barraza, and R. A. Taylor, ‘Techno-economic optimization of coupling a cascaded MED system to a CSP-sCO2 power plant’, Energy Conversion and Management, vol. 247, p. 114725, Nov. 2021, doi: https://doi.org/10.1016/j.enconman.2021.114725.

[20] A. Gastli, Y. Charabi, and S. Zekri, ‘GIS-based assessment of combined CSP electric power and seawater desalination plant for Duqum—Oman’, Renewable and Sustainable Energy Reviews, vol. 14, no. 2, pp. 821–827, Feb. 2010, doi: https://doi.org/10.1016/j.rser.2009.08.020.

[21] National Renewable Energy Laboratory, ‘System Advisor Model (SAM 2020.11.29)’. Golden, CO, Dec. 27, 2020. [Online]. Available: https://sam.nrel.gov

[22] D. Saldivia, C. Rosales, R. Barraza, and L. Cornejo, ‘Computational analysis for a multi-effect distillation (MED) plant driven by solar energy in Chile’, Renewable Energy, vol. 132, pp. 206–220, Mar. 2019, doi: https://doi.org/10.1016/j.renene.2018.07.139.

[23] A. Omar, M. Saghafifar, and M. Gadalla, ‘Thermo-economic analysis of air saturator integration in conventional combined power cycles’, Applied Thermal Engineering, vol. 107, pp. 1104–1122, Aug. 2016, doi: https://doi.org/10.1016/j.applthermaleng.2016.06.181.

[24] P. Palenzuela, D.-C. Alarcón-Padilla, and G. Zaragoza, ‘Large-scale solar desalination by combination with CSP: Techno-economic analysis of different options for the Mediterranean Sea and the Arabian Gulf’, Desalination, vol. 366, pp. 130–138, Jun. 2015, doi: https://doi.org/10.1016/j.desal.2014.12.037.

[25] SOLARGIS, ‘Solar resource maps and GIS map of World’. 2023. Accessed: Jun. 17, 2023. [Online]. Available: https://solargis.com

[26] Global Wind Atlas 3.0, ‘Worldwide mean wind speed at 10 m above surface level (m/s) - Global Wind Atlas’. Accessed: Jun. 17, 2023. [Online]. Available: https://globalwindatlas.info

[27] Center for International Earth Science Information Network - CIESIN - Columbia University, ‘Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11’. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, New York, 2018. [Online]. Available: https://doi.org/10.7927/H49C6VHW

[28] Esri, ‘Terrain: Slope in Degrees’. Mar. 23, 2023. Accessed: Jun. 17, 2023. [Online]. Available: https://www.arcgis.com/home/item.html?id=af25a795273440deb449b336543602be

[29] NASA Ocean Biology Processing Group, ‘Distance to the Nearest Coast’. 2009. Accessed: Jun. 17, 2023. [Online]. Available: https://oceancolor.gsfc.nasa.gov/docs/distfromcoast/

[30] J. Boutin et al., ‘ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): weekly and monthly sea surface salinity products, v03.21, for 2010 to 2020’. NERC EDS Centre for Environmental Data Analysis, 2021. doi: https://doi.org/10.5285/5920A2C77E3C45339477ACD31CE62C3C.

[31] K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis, and S. P. Brumby, ‘Global land use / land cover with Sentinel 2 and deep learning’, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Jul. 2021, pp. 4704–4707. doi: https://doi.org/10.1109/IGARSS47720.2021.9553499.

[32] C. Arderne, C. Zorn, C. Nicolas, and E. E. Koks, ‘Predictive mapping of the global power system using open data’, Sci Data, vol. 7, no. 1, Art. no. 1, Jan. 2020, doi: https://doi.org/10.1038/s41597-019-0347-4.

[33] Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, and World Resources Institute, ‘Global Power Plant Database’. Resource Watch and Google Earth Engine, 2018. Accessed: Jun. 17, 2023. [Online]. Available: http://resourcewatch.org/ https://earthengine.google.com/

[34] Center For International Earth Science Information Network-CIESIN-Columbia University and Information Technology Outreach Services-ITOS-University Of Georgia, ‘Global Roads Open Access Data Set, Version 1 (gROADSv1)’. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), 2013. doi: https://doi.org/10.7927/H4VD6WCT.

[35] IUCN and UNEP-WCMC, ‘Protected Planet: The World Database on Protected Areas (WDPA)[On-line]’. Cambridge, UK, Jun. 2023. doi: https://doi.org/10.34892/6fwd-af11.

[36] United Nations Office for the Coordination of Humanitarian Affairs (OCHA), ‘Global catalog of earthquakes - Humanitarian Data Exchange’. Accessed: Jun. 17, 2023. [Online]. Available: https://data.humdata.org/dataset/catalog-of-earthquakes1970-2014

[37] UN office for Disaster Risk Reduction (UNDRR), ‘Global model of cyclone wind 50, 100, 250, 500 and 1000 years return period - Humanitarian Data Exchange’. Accessed: Jun. 17, 2023. [Online]. Available: https://data.humdata.org/dataset/cyclone-wind-100-years-return-period

[38] World Resources Institute, ‘Aqueduct Floods Hazard Maps’. Accessed: Jun. 17, 2023. [Online]. Available: https://www.wri.org/data/aqueduct-floods-hazard-maps