GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran

Abstract

Fossil fuel emissions have caused immense harm to the environment, making renewable energy sources like solar power essential. However, finding the optimal location for a solar power plant requires multi-criteria decision analysis (MCDA) due to various factors influencing the selection process. This study used the AHP method to weigh criteria such as GHI, Temperature, Elevation, Slope, Land cover, Distance from city, and Distance from road. The layers created from satellite imagery were combined using algebraic sums to produce a final map with 9 classes The analysis showed that class 9 has the most desirable values for each criterion, indicating the most suitable regions for a solar power plant. The results of the study have identified the southern and some central regions of Ardabil province as being the most suitable location for the construction of a solar power plant. These regions have been found to have favorable values for the criteria studied, indicating a higher potential for solar energy generation. Based on the criteria assigned to class 9, the best lands have been identified, occupying a total area of 3085 hectares. This area represents approximately 0.17% of the total area of Ardabil province. These findings highlight the importance of careful site selection for solar power plants to ensure maximum efficiency and sustainability.

Keywords

• AHP
• Site Selection Analysis
• MCDA
• Renewable Energy

References

1. Albraheem, L., & Alabdulkarim, L. (2021). Geospatial analysis of solar energy in riyadh using a GIS-AHP-based technique. ISPRS International Journal of Geo-Information, 10(5), 291. https://doi.org/10.3390/ijgi10050291
2. U. S. (2021). Energy Information Administration. https://www.eia.gov/todayinenergy/detail.php?id=41433/
3. Gašparović, I., & Gašparović, M. (2019). Determining optimal solar power plant locations based on remote sensing and GIS methods: A case study from Croatia. Remote Sensing, 11(12), 1481. https://doi.org/10.3390/rs11121481
4. Munkhbat, U., & Choi, Y. (2021). GIS-based site suitability analysis for solar power systems in Mongolia. Applied Sciences, 11(9), 3748. https://doi.org/10.3390/app11093748
5. Uyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11-17. https://doi.org/10.1016/j.rser.2013.07.042
6. Sampaio, P. G. V., & González, M. O. A. (2017). Photovoltaic solar energy: Conceptual framework. Renewable and Sustainable Energy Reviews, 74, 590-601. https://doi.org/10.1016/j.rser.2017.02.081
7. World Energy Outlook (2019). https://www.iea.org/reports/world-energy-outlook-2019.
8. Weather Data and Software for Solar Power Investments. Available online: http://solargis.info/doc/_pics/freemaps/1000px/ghi/SolarGIS-Solar-map-Iran-en.png Shaikh, M. R. S., Waghmare, S. B., Labade, S. S., Fuke, P. V., Tekale, A. (2017). A review paper on electricity generation from solar energy. International Journal for Research in Applied Science & Technology 5(9), 1884-1889. http://dx.doi.org/10.22214/ijraset.2017.9272

9. Rabaia, M. K. H., Abdelkareem, M. A., Sayed, E. T., Elsaid, K., Chae, K. J., Wilberforce, T., & Olabi, A. G. (2021). Environmental impacts of solar energy systems: A review. Science of The Total Environment, 754, 141989. https://doi.org/10.1016/j.scitotenv.2020.141989
10. Choi, Y., Suh, J., & Kim, S. M. (2019). GIS-based solar radiation mapping, site evaluation, and potential assessment: A review. Applied Sciences, 9(9), 1960. https://doi.org/10.3390/app9091960
11. Genç, M. S., Karipoğlu, F., Koca, K., & Azgın, Ş. T. (2021). Suitable site selection for offshore wind farms in Turkey’s seas: GIS-MCDM based approach. Earth Science Informatics, 14(3), 1213-1225. https://doi.org/10.1007/s12145-021-00632-3
12. Doorga, J. R., Rughooputh, S. D., & Boojhawon, R. (2019). Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in Mauritius. Renewable energy, 133, 1201-1219. https://doi.org/10.1016/j.renene.2018.08.105
13. Ziuku, S., Seyitini, L., Mapurisa, B., Chikodzi, D., & van Kuijk, K. (2014). Potential of concentrated solar power (CSP) in Zimbabwe. Energy for Sustainable Development, 23, 220-227. https://doi.org/10.1016/j.esd.2014.07.006
14. Neisani Samani, N., & Tahouni, A. (2019). The Evaluation of suitable Sites for Solar Farms by Multi Criteria Decision Making in GIS (Case Study: East Azarbaijan Province). Human Geography Research, 51(3), 747-764. https://doi.org/10.22059/jhgr.2019.279885.1007909
15. Piirisaar, I. (2019). A multi criteria GIS analysis for siting of utility-scale photovoltaic solar plants in county Kilkenny, Ireland. [Master's thesis, Lund University].
16. Ruiz, H. S., Sunarso, A., Ibrahim-Bathis, K., Murti, S. A., & Budiarto, I. (2020). GIS-AHP Multi Criteria Decision Analysis for the optimal location of solar energy plants at Indonesia. Energy Reports, 6, 3249-3263. https://doi.org/10.1016/j.egyr.2020.11.198
17. Taiar, A., M. Rezvan, & H. Hashemi. (2019). Evaluation of suitable locations for large-scale solar power plants using GIS, Hierarchical Analysis Process (AHP) and TOPSIS (Case Study: Karbala Province, Iraq). Energy Engineering and Management, 4, 60-73. https://doi.org/10.22052/11.4.60
18. Watson, J. J., & Hudson, M. D. (2015). Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and Urban Planning, 138, 20-31. https://doi.org/10.1016/j.landurbplan.2015.02.001
19. Asakereh, A., Omid, M., Alimardani, R., & Sarmadian, F. (2014). Developing a GIS-based fuzzy AHP model for selecting solar energy sites in Shodirwan region in Iran. International Journal of Advanced Science and Technology, 68, 37-48. http://dx.doi.org/10.14257/ijast.2014.68.04
20. Noorollahi, E., Fadai, D., Akbarpour Shirazi, M., & Ghodsipour, S. H. (2016). Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)—a case study of Iran. Energies, 9(8), 643. https://doi.org/10.3390/en9080643
21. Suh, J., & Brownson, J. R. (2016). Solar farm suitability using geographic information system fuzzy sets and analytic hierarchy processes: Case study of Ulleung Island, Korea. Energies, 9(8), 648. https://doi.org/10.3390/en9080648
22. Sánchez-Lozano, J. M., Teruel-Solano, J., Soto-Elvira, P. L., & García-Cascales, M. S. (2013). Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renewable and sustainable energy reviews, 24, 544-556. https://doi.org/10.1016/j.rser.2013.03.019
23. https://ardmet.ir
24. Abdelrazek, M. (2017). GIS Approach to Find Suitable Locations for Installing Renewable Energy Production Units in Sinai Peninsula, Egypt. [Master’s thesis, University of Salzburg].
25. Martins, F. R., Pereira, E. B., & Abreu, S. L. (2007). Satellite-derived solar resource maps for Brazil under SWERA project. Solar Energy, 81(4), 517-528. https://doi.org/10.1016/j.solener.2006.07.009
26. Amillo, A. G., Huld, T., & Müller, R. (2014). A new database of global and direct solar radiation using the eastern meteosat satellite, models and validation. Remote sensing, 6(9), 8165-8189. https://doi.org/10.3390/rs6098165
27. Huld, T. (2017). PVMAPS: Software tools and data for the estimation of solar radiation and photovoltaic module performance over large geographical areas. Solar Energy, 142, 171-181. https://doi.org/10.1016/j.solener.2016.12.014
28. https://globalsolaratlas.info
29. Nebey, A. H., Taye, B. Z., & Workineh, T. G. (2020). Site Suitability Analysis of Solar PV Power Generation in South Gondar, Amhara Region. Journal of Energy, 3519257. https://doi.org/10.1155/2020/3519257
30. Al Garni, H. Z., & Awasthi, A. (2017). Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Applied Energy, 206, 1225-1240. https://doi.org/10.1016/j.apenergy.2017.10.024
31. Li, D. (2013). Using GIS and Remote Sensing Techniques for Solar Panel Installation Site Selection. [Master’s thesis, University of Waterloo]. https://doi.org/10.1016/j.solener.2006.07.009
32. Tahri, M., Hakdaoui, M., & Maanan, M. (2015). The evaluation of solar farm locations applying Geographic Information System and Multi-Criteria Decision-Making methods: Case study in southern Morocco. Renewable and sustainable energy reviews, 51, 1354-1362. https://doi.org/10.1016/j.rser.2015.07.054
33. Al-Shammari, S., Ko, W., Al Ammar, E. A., Alotaibi, M. A., & Choi, H. J. (2021). Optimal decision-making in photovoltaic system selection in Saudi Arabia. Energies, 14(2), 357. https://doi.org/10.3390/en14020357
34. Masoom, A., Kosmopolous, P., & Bansal, A. (2021). Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling (No. EMS2021-352). Copernicus Meetings. https://doi.org/10.5194/ems2021-352
35. https://www.earthdata.nasa.gov
36. https://livingatlas.arcgis.com/landcover
37. https://www.openstreetmap.org
38. Robinson, V. B. (2003). A perspective on the fundamentals of fuzzy sets and their use in geographic information systems. Transactions in GIS, 7(1), 3-30. https://doi.org/10.1111/1467-9671.00127
39. Corrente, S., Greco, S., & Słowiński, R. (2017). Handling imprecise evaluations in multiple criteria decision aiding and robust ordinal regression by n-point intervals. Fuzzy Optimization and Decision Making, 16, 127-157. https://doi.org/10.1007/s10700-016-9244-x
40. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281. https://doi.org/10.1016/0022-2496(77)90033-5
41. Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert Systems with Applications, 38(11), 14336-14345. https://doi.org/10.1016/j.eswa.2011.04.143
42. Alhammad, A., Sun, Q., & Tao, Y. (2022). Optimal solar plant site identification using GIS and remote sensing: framework and case study. Energies, 15(1), 312.
43. https://pro.arcgis.com/en/pro-app/latest/arcpy/spatial-analyst/what-is-the-spatial-analyst-module.htm
44. Adjiski, V., Kaplan, G., & Mijalkovski, S. (2022). Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach. International Journal of Engineering and Geosciences, 8(2), 188-199. https://doi.org/10.26833/ijeg.1112274
45. 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. https://doi.org/10.26833/ijeg.696001