Site selection for solar photovoltaic system installation using analytical hierarchy process model in Azerbaijan

  • Nijat Sohrab Imamverdiyev Institute of Geography named after Academician H. A. Aliyev, Azerbaijan National Academy of Sciences, 115 H. Javid Avenue, Baku AZ1143, Azerbaijan https://orcid.org/0000-0002-5573-0209

Abstract

The most suitable sites for solar photovoltaic power installations are determined through a comprehensive assessment of the meteorological, economic and environmental criteria of the energy potential areas. The basic criteria for location selection are evaluated using an analytical hierarchy process method based on multi-criteria decision-making technique for large-scale solar photovoltaic projects. The analytical hierarchy process model is also applied to evaluate areas of high solar potential and factors that are primary criteria for determinate the site suitability index modelling. This method considers various conditions, such as production and technological considerations, which aim to maximise the short-term profit from the project and the efficiency of power generation. In the study, a consistency ratio of suitable localities was determined and proper alternatives for the construction of photovoltaic installations were evaluated. In addition to local meteorology and related satellite measurement data, the country’s radiation values also were compared by converting a digital elevation model data using the tool «Area solar radiation» in GIS. As a result of calculating the site suitability index with the ArcGIS weighted overlay tool, it was concluded that 1.17 % (1016.8 km2) of the country are the most suitable sites for the installation of solar PV systems. These areas mainly include Khizi, Gobustan, Hajigabul, Beylagan, Sharur, Babek and Jeyranchol zones. The total number of locations identified accross the country, classified into 3 categories according to their level of suitability, includes 40 sites. Eight of these high suitability sites, all in Nakhchivan Autonomous Republic, contain 11 % (109.2 km2) of the total potential area. The remaining 32 sites, corresponding to areas with medium and low energy potential, cover 28 % (284.6 km2) and 61 % (623 km2), respectively. When these areas are completely covered with PV panels, it will be possible to fully supply the energy demand of the country with solar energy.

Author Biography

Nijat Sohrab Imamverdiyev, Institute of Geography named after Academician H. A. Aliyev, Azerbaijan National Academy of Sciences, 115 H. Javid Avenue, Baku AZ1143, Azerbaijan

researcher

References

  1. Murdock HE, Gibb D, André T, Sawin JL, Brown A, Appavou F, et al. Renewables 2020 – Global status report [Internet; cited 2020 December 15]. 2020. Available from: https://inis.iaea.org/search/searchsinglerecord.aspx?recordsFor=SingleRecord&RN=51070091.
  2. Ferroukhi R. REthinking energy – 2014: towards a new power system. Abu Dhabi: International Renewable Energy Agency; 2014. 96 p.
  3. Candelise C, Winskel M, Gross RJK. The dynamics of solar PV costs and prices as a challenge for technology forecasting. Renewable and Sustainable Energy Reviews. 2013;26:96–107. DOI: 10.1016/j.rser.2013.05.012.
  4. International Finance Corporation. Utility-scale solar photovoltaic power plants [Internet; cited 2021 January 20]. 2015. Available from: https://www.ifc.org/wps/wcm/connect/a1b3dbd3-983e-4ee3-a67b-cdc29ef900cb/IFC+Solar+Report_Web+_08+05.pdf?MOD=AJPERES&CVID=kZePDPG.
  5. Lopez A, Roberts B, Heimiller D, Blair N, Porro G. US renewable energy technical potentials: a GIS-based analysis. Golden (US): National Renewable Energy Laboratory; 2012 July. 40 p. Technical Report: NREL/TP-6A20-51946.
  6. Azizkhani M, Vakili A, Noorollahi Y, Naseri F. Potential survey of photovoltaic power plants using analytical hierarchy process (AHP) method in Iran. Renewable and Sustainable Energy Reviews. 2017;75:1198–1206. DOI: 10.1016/j.rser.2016.11.103.
  7. Saaty TL. The Analytic Hierarchy Process. New York: McGraw-Hill; 1980. 287 p.
  8. Noorollahi E, Fadai D, Shirazi MA, Ghodsipour SH. Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP) – a case study of Iran. Energies. 2016;9(8):643. DOI: 10.3390/en9080643.
  9. Garni HZAl, Awasthi A. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Applied Energy. 2017;206:1225–1240. DOI: 10.1016/j.apenergy.2017.10.024.
  10. Azerbaijan. Solar energy resource. Global Solar Atlas 3.0 [Internet; cited 2020 August 8]. 2020. Available from: https://globalsolaratlas.info/map?r=AZE:AZE.7_1&c=39.30579,45.4625,9.
  11. The World Bank. Solar resource maps of Azerbaijan [Internet; cited 2020 August 17]. Available from: https://solargis.com/maps-and-gis-data/download/azerbaijan.
  12. Khartchenko NV, Kharchenko VM. Advanced energy systems. 2nd edition. Boca Raton: CRC Press; 2013. 618 p.
  13. Sanchez-Lozano JM, García-Cascales MS, Lamata MT. Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of Cleaner Production. 2016;127:387–398. DOI: 10.1016/j.jclepro.2016.04.005.
  14. Effat HA. Selection of potential sites for solar energy farms in Ismailia Governorate, Egypt using SRTM and multicriteria analysis. International Journal of Advanced Remote Sensing and GIS. 2013;2(1):205–220.
  15. Colak HE, Memisoglu T, Gercek Y. Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: a case study of Malatya Province, Turkey. Renewable energy. 2020;149:565–576. DOI: 10.1016/j.renene.2019.12.078.
  16. Sengupta M, Habte A, Gueymard C, Wilbert S, Renné D, Stoffel T. Best practices handbook for the collection and use of solar resource data for solar energy applications. Golden (US): National Renewable Energy Laboratory; 2017 January. Report No.: NREL/TP-5D00-68886.e.
  17. Greene R, Devillers R, Luther JE, Eddy BG. GIS-based multiple-criteria decision analysis. Geography Compass. 2011;5(6):412–432. DOI: 10.1111/j.1749-8198.2011.00431.x.
  18. Rumbayan M, Nagasaka K. Prioritization decision for renewable energy development using analytic hierarchy process and geographic information system. In: Tokyo University of Agriculture and Technology. The 2012 International Conference on Advanced Mechatronic Systems, 2012 September 18–21; Tokyo, Japan. New York: Institute of Electrical and Electronics Engineers; 2012. p. 36–41.
  19. Uyan M. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews. 2013;28;11–17. DOI: 10.1016/j.rser.2013.07.042.
  20. Watson JJ, Hudson MD. Regional scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and Urban Planning. 2015;138:20–31. DOI: 10.1016/j.landurbplan.2015.02.001.
  21. Charabi Y, Rhouma MBH, Gastli A. Siting of PV power plants on inclined terrains. International Journal of Sustainable Energy. 2016;35(9):834–843. DOI: 10.1080/14786451.2014.952298.
  22. Mentis D, Welsch M, Nerini FF, Broad O, Howells M, Bazilian M, et al. A GIS-based approach for electrification planning – a case study on Nigeria. Energy for Sustainable Development. 2015;29:142–150. DOI: 10.1016/j.esd.2015.09.007.
  23. Kengpol A, Rontlaong P, Tuominen M. Design of a decision support system for site selection using fuzzy AHP: a case study of solar power plant in north-eastern parts of Thailand. In: Proceedings of PICMET’12: Technology Management for Emerging Technologies; 29 July – 2 August 2012; Vancouver, Canada. New York: Institute of Electrical and Electronics Engineers; 2012. p. 734–743.
  24. Massimo A, Dell’Isola M, Frattolillo A, Ficco G. Development of a geographical information system (GIS) for the integration of solar energy in the energy planning of a wide area. Sustainability. 2014;6(9):5730–5744. DOI: 10.3390/su6095730.
  25. Borgogno ME, Fabrizio E, Chiabrando R. Site selection of large ground-mounted photovoltaic plants: a GIS decision support system and an application to Italy. International Journal of Green Energy. 2015;12(5):515–525. DOI: 10.1080/15435075.2013.858047.
  26. Aydin NY, Kentel E, Duzgun HS. GIS-based site selection methodology for hybrid renewable energy systems: a case study from western Turkey. Energy conversion and management. 2013;70:90–106. DOI: 10.1016/j.enconman.2013.02.004.
  27. Janke JR. Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy. 2010;35(10):2228–2234. DOI: 10.1016/j.renene.2010.03.014.
  28. Chien F, Wang C-N, Nguyen VT, Nguyen VT, Chau KY. An evaluation model of quantitative and qualitative fuzzy multi-criteria decision-making approach for hydroelectric plant location selection. Energies. 2020;13(11):2783. DOI: 10.3390/en13112783.
  29. Mammadov RM, scientific editor. Geographical Atlas of the Republic of Azerbaijan. [S. l.]: Baku Cartography Factory; 2018. 207 p.
  30. GMAO MERRA-2 assimilation model (1981–2019) and GEOS-5.12.4. Power Data Access Viewer [Internet; cited 2020 December 1]. Available from: https://power.larc.nasa.gov/data-access-viewer.
  31. Aguayo P. Solar energy potential analysis at building scale using LiDAR and satellite data. Waterloo (ON): University of Waterloo; 2013.
  32. Earth Data Search. ASTER Global Digital Elevation Model NetCDF V003 [Internet; cited 2020 September 12]. Available from: https://search.earthdata.nasa.gov/search/?hdr=1%20to%2030%20meters&fi=ASTER&fst0=Land%20Surface (date of access: 12.09.2020).
  33. Ismayilov M, Jabrayilov E. Protected areas in Azerbaijan: landscape-ecological diversity and sustainability. Ankara Üniversitesi Çevrebilimleri Dergisi. 2019;7(2):31–42.
  34. Asakereh A, Soleymani M, Sheikhdavoodi MJ. A GIS-based Fuzzy-AHP method for the evaluation of solar farms locations: case study in Khuzestan province, Iran. Solar Energy. 2017;155:342–353. DOI: 10.1016/j.solener.2017.05.075.
  35. Asia. Geofabrik downloads [Internet; 2020 November 12]. Available from: https://download.geofabrik.de/asia.html.
  36. Saaty TL. Analytic heirarchy process. In: Wiley StatsRef: Statistics Reference Online [Internet; cited 2020 December 23]. [S. l.]: John Wiley & Sons; 2014 September 29. DOI: 10.1002/9781118445112.stat05310.
  37. Buchhorn M, Smets B, Bertels L, De Roo B, Lesiv M, Tsendbazar N-E. Copernicus global land service. Land cover 100 m: collection 3: epoch 2019: globe [Internet; cited 2020 December 22]. 2020 September 8. Available from: https://lcviewer.vito.be/2015/Azerbaijan. DOI: 10.5281/zenodo.3939050.
  38. The State Statistical Committee of the Republic of Azerbaijan. Energy of Azerbaijan. Baku: The State Statistical Committee of the Republic of Azerbaijan; 2020. 160 p.
Published
2021-06-15
Keywords: renewable energy resources, solar energy, solar photovoltaic system, multi-criteria decision-making, GIS model, AHP model
How to Cite
Imamverdiyev, N. S. (2021). Site selection for solar photovoltaic system installation using analytical hierarchy process model in Azerbaijan. Journal of the Belarusian State University. Geography and Geology, 1, 75-92. https://doi.org/10.33581/2521-6740-2021-1-75-92