Geomatics and Environmental Engineering, vol. 18, no. 1

Issue

Vol. 18 No. 1 (2024): Geomatics and Environmental Engineering

Date Log

Submitted
Jun 26, 2023
Accepted
Dec 15, 2023
Published
Jan 19, 2024
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

GIS- and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia

https://doi.org/10.7494/geom.2024.18.1.51
Vancho Adjiski
Goce Delcev University, Faculty of Natural and Technical Sciences, Stip, North Macedonia
https://orcid.org/0000-0001-6401-6835
Dalibor Serafimovski
Goce Delcev University, Faculty of Electrical Engineering, Stip, North Macedonia
https://orcid.org/0000-0003-1735-1547

Corresponding Author(s) : Vancho Adjiski

vanco.adziski@ugd.edu.mk

Geomatics and Environmental Engineering, Vol. 18 No. 1 (2024): Geomatics and Environmental Engineering

Abstract

This study employs a geographic information system (GIS) and an analytical hierarchy process (AHP) to identify optimal locations for photovoltaic (PV) solar farms in the Republic of North Macedonia. It assesses land suitability using six criteria (solar irradiance, aspect, slope, distance from power lines, roads, and urban areas) and six constraints (urban settlements, agricultural zones, national parks, water bodies, steep slopes, elevations above 1500 m). A suitability map was created using a matrix of pairwise comparisons, and the weights for each criterion were calculated. The map was divided into four categories: highly suitable, suitable, less suitable, and unsuitable. The results showed that 11.6% of the study area was classified as being highly suitable, 40.1% as suitable, 3.6% as less suitable, and 0.8% as unsuitable. Additionally, restricted areas (comprised of national parks, residential and agricultural lands, elevations above 1500 m, and water surfaces with 1000 m buffer zones) accounted for 43.7% of the study area. Utilizing just 0.6% of highly suitable land for PV technology could generate approximately 2870 GWh annually, enough to meet the average electricity needs of the industrial sector across the eight administrative regions of R. N. Macedonia. The study offers a replicable GIS-based approach for solar energy planning, contributing to sustainable development and providing insights for integrating solar PV systems into the national energy strategy.

Keywords

multi-criteria decision analysis (MCDA) renewable energy solar farm suitability map sustainable energy planning
Adjiski, V., & Serafimovski, D. (2024). GIS- and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia. Geomatics and Environmental Engineering, 18(1), 51–82. https://doi.org/10.7494/geom.2024.18.1.51
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Landau S., Ilieva L.: NDC Implementation Roadmap for North Macedonia 2020–2030. Ministry of Environment and Physical Planning 2021.
  2. The strategy for energy development of the Republic of North Macedonia until 2040. Final draft – for public consultations. Reform Assistance to Macedonia, October 2019. https://economy.gov.mk/Upload/Documents/Energy%20Development%20Strategy_FINAL%20DRAFT%20-%20For%20public%20consultations_ENG_29.10.2019(3).pdf [access: 15.03.2023].
  3. Recommendations by the Energy Community Secretariat on the Draft National Energy and Climate Plan of North Macedonia. RE NECP 01/2020 / 20 November 2020, Energy Community 2020. https://www.energy-community.org/dam/jcr:ad73687c-12d3-48ff-a48f-faed18760ce5/ECS_RE01_MK_NECP_112020.pdf [access: 15.03.2023].
  4. Colak H.E., 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, vol. 149, 2020, pp. 565–576. https://doi.org/10.1016/j.renene.2019.12.078.
  5. Gorsevski P.V., Donevska K.R., Mitrovski C.D., Frizado J.P.: Integrating multi-criteria­evaluation­techniques­with­geographic­information­systems­for­landfill­site selection: A case study using ordered weighted average. Waste Management, vol. 32(2), 2012, pp. 287–296. https://doi.org/10.1016/j.wasman.2011.09.023.
  6. Watson J.J.W., Hudson M.D.: Regional scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and Urban Planning, vol. 138, 2015, pp. 20–31. https://doi.org/10.1016/j.landurbplan.2015.02.001.
  7. Fung T., Wong F.K.K.: Ecotourism planning using multiple criteria evaluation with GIS. Geocarto International, vol. 22(2), 2007, pp. 87–105. https://doi.org/10.1080/10106040701207332.
  8. Twumasi Y.A.: The use of GIS and remote sensing techniques as tools for managing nature reserves: the case of Kakum National Park in Ghana. [in:] IGARSS 2001: Scanning the Present and Resolving the Future: Proceedings Volume VII: IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat No01CH37217), 9–13 July 2001, University of New South Wales, Sydney, Australia, IEEE, Piscataway 2001, pp. 3227–3229. https://doi.org/10.1109/IGARSS.2001.978311.
  9. Guaita-Pradas I., Marques-Perez I., Gallego A., Segura B.: Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases. Environmental Monitoring and Assessment, vol. 191(12), 2019, 764. https://doi.org/10.1007/s10661-019-7871-8.
  10. Díaz-Cuevas P., Domínguez-Bravo J., Prieto-Campos A.: Integrating MCDM and GIS for renewable energy spatial models: assessing the individual and combined potential for wind, solar and biomass energy in Southern Spain. Clean Technologies and Environmental Policy, vol. 21(9), 2019, pp. 1855–1869. https://doi.org/10.1007/s10098-019-01754-5.
  11. Stojčić M., Zavadskas E.K., Pamučar D., Stević Ž., Mardani A.: Application of MCDM methods in sustainability engineering: A literature review 2008–2018. Symmetry, vol. 11(3), 2019, 350. https://doi.org/10.3390/sym11030350.
  12. Mardani A., Zavadskas E.K., Khalifah Z., Zakuan N., Jusoh A., Nor K.M., Khoshnoudi M.: A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable and Sustainable Energy Reviews, vol. 71, 2017, pp. 216–256. https://doi.org/10.1016/j.rser.2016.12.053.
  13. Siksnelyte I., Zavadskas E.K., Streimikiene D., Sharma D.: An overview of multi-criteria decision-making methods in dealing with sustainable energy development issues. Energies, vol. 11(10), 2018, 2754. https://doi.org/10.3390/en11102754.
  14. Ruiz H.S., Sunarso A., Ibrahim-Bathis K., Murti S.A., Budiarto I.: GIS-AHP multi criteria decision analysis for the optimal location of solar energy plants at Indonesia. Energy Reports, vol. 6, 2020, pp. 3249–3263. https://doi.org/10.1016/j.egyr.2020.11.198.
  15. Giamalaki M., Tsoutsos T.: Sustainable siting of solar power installations in Mediterranean using a GIS/AHP approach. Renewable Energy, vol. 141, 2019, pp. 64–75. https://doi.org/10.1016/j.renene.2019.03.100.
  16. Settou B., Settou N., Gouareh A., Negrou B., Mokhtara C., Messaoudi D.: A high-resolution geographic information system-analytical hierarchy process-based method for solar PV power plant site selection: A case study Algeria. Clean Technologies and Environmental Policy, vol. 23(1), 2021, pp. 219–234. https://doi.org/10.1007/s10098-020-01971-3.
  17. Uyan M.: GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, vol. 28, 2013, pp. 11–17. https://doi.org/10.1016/j.rser.2013.07.042.
  18. Ziuku S., Seyitini L., Mapurisa B., Chikodzi D., van Kuijk K.: Potential of concentrated solar power (CSP) in Zimbabwe. Energy for Sustainable Development, vol. 23, 2014, pp. 220–227. https://doi.org/10.1016/j.esd.2014.07.006.
  19. Huang J.P., Poh K.L., Ang B.W.: Decision analysis in energy and environmental modeling. Energy, vol. 20(9), 1995, pp. 843–855. https://doi.org/10.1016/0360-5442(95)00036-G.
  20. Heo E., Kim J., Boo K.-J.: Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews, vol. 4(8), 2010, pp. 2214–2220. https://doi.org/10.1016/j.rser.2010.01.020.
  21. Mokarram M., Mokarram M.J., Khosravi M.R., Saber A, Rahideh A.: Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster–Shafer theory. Scientific Reports, vol. 10(1), 2020, 8200. https://doi.org/10.1038/s41598-020-65165-z.
  22. La Guardia M., D’Ippolito F., Cellura M.: Construction of a WebGIS tool based on a GIS semiautomated processing for the localization of P2G plants in Sicily (Italy). ISPRS International Journal of Geo-Information, vol. 10(10), 2021, 671. https://doi.org/10.3390/ijgi10100671.
  23. Choi Y., Suh J., Kim S-M.: GIS-based solar radiation mapping, site evaluation, and potential assessment: A review. Applied Sciences, vol. 9(9), 2019, 1960. https://doi.org/10.3390/app9091960.
  24. Wang J.J., Jing Y.Y., Zhang C.F., Zhao J.H.: Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, vol. 13(9), 2009, pp. 2263–2278. https://doi.org/10.1016/j.rser.2009.06.021.
  25. Jahangiri M., Ghaderi R., Haghani A., Nematollahi O.: Finding the best locations for establishment of solar-wind power stations in Middle-East using GIS: A review. Renewable and Sustainable Energy Reviews, vol. 66, 2016, pp. 38–52. https://doi.org/10.1016/j.rser.2016.07.069.
  26. Abu-Taha R.: Multi-criteria applications in renewable energy analysis: A literature review. [in:] Daim T., Oliver T., Kim J. (eds.), Research and Technology Management in the Electricity Industry: Methods, Tools and Case Studies, Green Energy and Technology, Springer, London 2013, pp. 17–30. https://doi.org/10.1007/978-1-4471-5097-8_2.
  27. Halder B., Banik P., Almohamad H., Al Dughairi A.A., Al-Mutiry M., Al Shahrani H.F., Abdo H.G.: Land suitability investigation for solar power plant using GIS, AHP and multi-criteria decision approach: A case of megacity Kolkata, West Bengal, India. Sustainability, vol. 14(18), 2022, 11276. https://doi.org/10.3390/su141811276.
  28. Sánchez-Lozano J.M., Teruel-Solano J., Soto-Elvira P.L., Socorro GarcíaCascales M.: 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, vol. 24, 2013, pp. 544–556. https://doi.org/10.1016/j.rser.2013.03.019.
  29. Solangi Y.A., Shah S.A.A., Zameer H., Ikram M., Saracoglu B.O.: Assessing the solar PV power project site selection in Pakistan: based on AHP-fuzzy VIKOR approach. Environmental Science and Pollution Research, vol. 26(29), 2019, pp. 30286–30302. https://doi.org/10.1007/s11356-019-06172-0.
  30. Isiaka I., Abayomi A., Suara G.: Site suitability assessment for solar photovoltaic power plant in FCT-Abuja, Nigeria: A Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) approach. World Scientific News: An International Scientific Journal, vol. 172, 2022, pp. 89–104.
  31. Suh J., Brownson J.R.S.: Solar farm suitability using Geographic Information System fuzzy sets and analytic hierarchy processes: Case study of Ulleung Island, Korea. Energies, vol. 9(8), 2016, 648. https://doi.org/10.3390/en9080648.
  32. Aly A., Jensen S.S., Pedersen A.B.: Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis. Renewable Energy, vol. 113, 2017, pp. 159–175. https://doi.org/10.1016/j.renene.2017.05.077.
  33. Yousefi H., Hafeznia H., Yousefi-Sahzabi A.: Spatial site selection for solar power plants using a GIS-based boolean-fuzzy logic model: A case study of Markazi Province, Iran. Energies, vol. 11(7), 2018, 1648. https://doi.org/10.3390/en11071648.
  34. Akkas O.P., Erten M.Y., Cam E., Inanc N.: Optimal site selection for a solar power plant in the Central Anatolian Region of Turkey. International Journal of Photoenergy, vol. 2017, 2017, 7452715. https://doi.org/10.1155/2017/7452715.
  35. Milevski I., Radevski I., Dimitrovska O., Gorin S.: Digital model of the mean annual temperature and precipitation in Macedonia. Geographical Reviews, vol. 48, 2015, pp. 27–34.
  36. National energy and climate plan of the Republic of North Macedonia 2020. Final draft. Open Regional Fund for South East Europe – Energy Efficiency, GIZ office North Macedonia. https://www.economy.gov.mk/content/Official%20NECP_EN.pdf. Accessed on March 25, 2023.
  37. Law on Amendments and Supplements to the Law on Energy. Official Gazette of SRM No. 236/2022. https://www.erc.org.mk/odluki/Izmeni%20na%20Zakon%20za%20energetika_236%2022_MK.pdf [access: 25.03.2023].
  38. Law on Agricultural Land. Official Gazette of SRM No. 135/2007 of 08.11.2007). https://www.moepp.gov.mk/wp-content/uploads/2014/11/Zakon%20za%20zemjodelsko%20zemjiste_SV%20135-2007.pdf [access: 25.03.2023].
  39. Gašparović I., Gašparović M., Medak D.: Determining and analysing solar irradiation based on freely available data: A case study from Croatia. Environmental Development, vol. 26, 2018, pp. 55–67. https://doi.org/10.1016/j.envdev.2018.04.001.
  40. Gerbo A., Suryabhagavan K.V., Kumar Raghuvanshi T.: 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(3), 2022, pp. 159–173. https://doi.org/10.1080/24749508.2020.1809059.
  41. Majumdar D., Pasqualetti M.J.: Analysis of land availability for utility-scale power plants and assessment of solar photovoltaic development in the state of Arizona, USA. Renewable Energy, vol. 134, 2019, pp. 1213–1231. https://doi.org/10.1016/j.renene.2018.08.064.
  42. Al-Shammari S., Ko W., Al Ammar E.A., Alotaibi M.A., Choi H.J.: Optimal decision-making in photovoltaic system selection in Saudi Arabia. Energies, vol.14(2), 2021, 357. https://doi.org/10.3390/en14020357.
  43. Alhammad A., Sun Q., Tao Y.: Optimal solar plant site identification using GIS and remote sensing: Framework and case study. Energies, vol. 15(1), 2022, 312. https://doi.org/10.3390/en15010312.
  44. Kowalczyk A.M., Czyża S.: Optimising photovoltaic farm location using a capabilities matrix and GIS. Energies. vol. 15(18), 2022, 6693. https://doi.org/10.3390/en15186693.
  45. Munkhbat U., Choi Y.: GIS-based site suitability analysis for solar power systems in Mongolia. Applied Sciences, vol. 11(9), 2021, 3748. https://doi.org/10.3390/app11093748.
  46. Saaty T.L.: A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, vol. 15(3), 1977, pp. 234–281. https://doi.org/10.1016/0022-2496(77)90033-5.
  47. Saaty T.L.: Decision making with the Analytic Hierarchy Process. International Journal of Services Sciences, vol. 1(1), 2018, pp. 83–98. https://doi.org/10.1504/IJSSCI.2008.017590.
  48. Saaty R.W.: The analytic hierarchy process – what it is and how it is used. Mathematical Modelling, vol. 9(3), 1987, pp. 161–176. https://doi.org/10.1016/0270-0255(87)90473-8.
  49. Charabi Y., Gastli A.: GIS assessment of large CSP plant in Duqum, Oman. Renewable and Sustainable Energy Reviews, vol. 14(2), 2010, pp. 835–841. https://doi.org/10.1016/j.rser.2009.08.019.
  50. Adjiski V., Kaplan G., Mijalkovski S.: Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach. International Journal of Engineering and Geosciences, vol. 8(2), 2023, pp. 188–199. https://doi.org/10.26833/ijeg.1112274.
  51. Bunodiere A., Lee H.S.: Renewable energy curtailment: Prediction using a logicbased forecasting method and mitigation measures in Kyushu, Japan. Energies, vol. 13(18), 2020, 4703. https://doi.org/10.3390/en13184703.
  52. Mwanza M., Ulgen K.: GIS-based assessment of solar energy harvesting sites and electricity generation potential in Zambia. [in:] Filho W.L., Oguge N., Ayal D., Adeleke L., da Silva I. (eds.), African Handbook of Climate Change Adaptation, Springer, Cham 2021, pp. 899–946. https://doi.org/10.1007/978-3-030-45106-6_60.
  53. Feizizadeh B., Jankowski P., Blaschke T.: A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Computers & Geosciences, vol. 64, 2014, pp. 81–95. https://doi.org/10.1016/j.cageo.2013.11.009.
  54. Benke K.K., Pelizaro C.: A spatial-statistical approach to the visualisation of uncertainty in land suitability analysis. Journal of Spatial Science, vol. 55(2), 2010, pp. 257–272. https://doi.org/10.1080/14498596.2010.521975.
  55. Feizizadeh B., Blaschke T.: An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. International Journal of Geographical Information Science, vol. 28(3), 2014, pp. 610–638. https://doi.org/10.1080/13658816.2013.869821.
  56. Chen Y., Yu J., Khan S.: Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, vol. 25(12), 2010, pp. 1582–1591. https://doi.org/10.1016/j.envsoft.2010.06.001.
  57. Regions of the Republic of North Macedonia, 2022. Republic of North Macedonia, State Statistical Office, Skopje. https://www.stat.gov.mk/PrikaziPublikacija_en.aspx?id=32&rbr=815 [access: 25.03.2023].
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 430 Download : 200

Downloads

Download data is not yet available.