Journal of Green Energy Research and Innovation
Quarterly

Optimal Site Selection of Solar Power Plant Stations Using GIS-ANP and Genetic Optimization Algorithm in Markazi Province, Iran

Document Type : Research article

Authors

Fatemeh Masteri Farahani 1
Azadeh Kazemi 1
Amir Hedayati Aghmashadi 2

1 Department of Environmental Science and Engineering, Faculty of Agriculture and Environment, Arak University, 38156879 Arak, Iran.

2 Department of Sustainable Landscape Development, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120 Halle, Germany.

https://doi.org/10.61186/jgeri.1.4.47
Abstract
The demand for non-renewable energy sources in power generation is crucial for residential and commercial uses, significantly impacting national development. However, with the depletion of fossil fuels, there is a shift towards renewable energy sources such as solar, water, and wind, which have seen a surge in use over recent decades. In Iran, despite abundant fossil fuel resources, solar energy is vital due to the country's favorable geographic conditions for solar exploitation. This study applies the analytic network process (ANP) and Genetic algorithm (GA) to identify optimal locations for Solar Power Plant Stations in Markazi province, Iran. Key morphological factors considered include slope, elevation, and solar radiation. The research identified the northwest and northern parts of Markazi province as the most suitable for solar photovoltaic systems, primarily due to their simpler topography. Using a genetic algorithm, which outperformed the ANP, it was found that about 24,000 km² in these areas are apt for solar power facilities, categorized into highly suitable (2,429.312 km²), moderately suitable (16,818.49 km²), and suitable (5,029.007 km²). Saveh showed the highest potential, while Ashtian, Khondab, and Shazand had the least. These findings provide crucial insights for stakeholders looking to develop solar energy projects in Markazi province.

Graphical Abstract

Optimal Site Selection of Solar Power Plant Stations Using GIS-ANP and Genetic Optimization Algorithm in Markazi Province, Iran

Highlights

 

  • The use of renewable resources for electricity production is unavoidable.
  • Creating solar power plants can help reduce the environmental effects of fossil fuel consumption.
  • Site selection of solar power plants using modern methods is important.
  • Among the methods, the ANP method has not been used so far.
  • It is better to use the genetic algorithm method to verify the location.

 

Keywords

Solar power plant stations
Genetic algorithm
Optimal site selection
Analytic network process (ANP)

 

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

Fatemeh Masteri Farahani: Conceptualization, Data curation, Methodology, Resources, Software, Roles/Writing - original draft. Azadeh Kazemi: Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Validation, Visualization, Writing-review & editing. Amir Hedayati Aghmashadi: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Roles/Writing-original draft,

 

Bibliography

 Fatemeh Masteri Farahani was born in 1998, in Arak, Iran. She received her B.Sc. and M.Sc. degrees in Environmental Science and Engineering and Environment (Assessment and Land Use Planning), respectively, from Arak University, in 2020 and 2023. Her fields of interest include The Environment and Wild Life.

 Azadeh Kazemi is an assistant Professor with a PhD in environmental management and planning.  She is currently a researcher at Arak University, Arak, Iran. She has more than 10 years of experience working with clients from different parties including indigenous peoples, private sections and governments to continuously improve their environmental performance. Her most important tasks, besides teaching and in the form of projects and assignments, are familiarizing himself with new environmental topics, carrying out environmental assessments, monitoring, planning and management, risk assessment and researching based on the remote sensing, environmental modelling and GIS.

 Amir Hedayati Aghmashadi is a sustainability manager with a PhD in environmental planning.  He is currently a researcher at Martin Luther University Halle-Wittenberg, Halle (Saale), Germany. He has more than 5 years of experience working with clients from different parties including indigenous peoples, private sections and governments to continuously improve their environmental performance. His most important tasks, besides teaching and in the form of projects and assignments, are familiarizing himself with new environmental topics, carrying out environmental assessments, monitoring, planning and management, risk assessment and complying with regulations based on the standards and criteria.

 

Citation

F. Masteri Farahani, A. Kazemi, and A. Hedayati Aghmashadi, "Optimal Site Selection of Solar Power Plant Stations Using GIS-ANP and Genetic Optimization Algorithm in Markazi Province, Iran," Journal of Green Energy Research and Innovation, vol. 1, no. 4, pp. 47-63, 2024.

 

  1. O. A. AL-Shahri, F. B. Ismail, et al., "Solar Photovoltaic Energy Optimization Methods, Challenges and Issues: A Comprehensive Review," Journal of Cleaner Production, vol. 284, 125465, 2021
  2. S. Zereshkian, and D. Mansoury, "A Study on The Feasibility of Using Solar Radiation Energy and Ocean Thermal Energy Conversion to Supply Electricity for Offshore Oil and Gas Fields in The Caspian Sea," Renewable Energy, vol. 163, pp. 66-77, 2021.
  3. S. Moonchai, and N. Chutsagulprom, "Short-term Forecasting of Renewable Energy Consumption: Augmentation of a Modified Grey Model with a Kalman Filter," Applied Soft Computing, vol. 87, 105994, 2020.
  4. A. Mollahosseini, S. A. Hosseini, M. Jabbari, A. Figoli, and A. Rahimpour, "Renewable Energy Management and Market in Iran: A Holistic Review on Current State and Future Demands," Renewable and Sustainable Energy Reviews, vol. 80, pp. 774-788, 2017.
  5. S. Zhang, and X. Li, "Future Projections of Offshore Wind Energy Resources in China using CMIP6 Simulations and a Deep Learning-based Downscaling Method," Energy, vol. 217, 119321, 2021.
  6. H. Z. Al Garni, A. Awasthi, "Solar PV Power Plant Site Selection using A GIS-AHP Based Approach with Application in Saudi Arabia," Applied Energy, vol. 206, pp. 1225-1240, 2017.
  7. A. A. Merrouni, F. E. Elalaouni, A. Ghenniouni, 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.
  8. S. M. Habib, A. E. R. E. Suliman, A. H. Al Nahry, and E. N. Abd El Rahman, "Spatial Modeling for the Optimum Site Selection of Solar Photovoltaics Power Plant in the Northwest Coast of Egypt," Remote Sensing Applications: Society and Environment, vol. 18, 100313, 2020.
  9. M. A. Hassaan, A. Hassan, and H. Al-Dashti, "GIS-Based Suitability Analysis for Siting Solar Power Plants in Kuwait," The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, pp. 453-461. 2021.
  10. N. Mirzaei, and R. Nowzari, "Applying A Combination of AHP, ANP, and PROMETHEE Methods to Find the Optimal Location for Solar Power Plant," Avrupa Bilim Ve Teknoloji Dergisi, vol. 32, pp. 1076-1085, 2022.
  11. S. M. W. Sadat, and N. R. Sabory, "Afghanistan Renewable Energy Sector’s Human Resources Estimation until 2032," Repa Proceeding Series, vol. 1, no. 1. pp. 96-101, 2020.
  12. H. Ataee, and R. Fanaee, "Studying the Climate Change of Markazi Province by Mann-Kendall Method," Nivar, vol. 36, no. 77-76, pp. 37-48, 2012.
  13. A. M. Patel, and S. K. Singal, "Optimal Component Selection of Integrated Renewable Energy System for Power Generation in Stand-Alone Applications," Energy, vol. 175, pp. 481-504, 2019.
  14. M. Zoghi, A.H. Ehsani, M. Sadat, M. J. Amiri, and S. Karimi, "Optimization Solar Site Selection by Fuzzy Logic Model and Weighted Linear Combination Method in Arid And Semi-Arid Region: A Case Study Isfahan-IRAN," Renewable and Sustainable Energy Reviews, vol. 68, pp.986-996, 2017.
  15. S. Türk, A. Koç, and G. Şahin, "Multicriteria of PV Solar Site Selection Problem using GIS-Intuitionistic Fuzzy Based Approach in Erzurum Province/Turkey," Scientific Reports, vol. 11, no. 1, p. 5034, 2021.
  16. U. Munkhbat, and Y. Choi, "GIS-based Site Suitability Analysis for Solar Power Systems in Mongolia, " Applied Sciences, vol. 11, no. 9, p. 3748, 2021.
  17. O. Soydan, "Solar Power Plants Site Selection for Sustainable Ecological Development in Nigde, Turkey," SN Applied Sciences, vol. 3, no. 1, p. 41, 2021.
  18. N. Kundakcı, "An Integrated Multicriteria Decision Making Approach for Tablet Computer Selection," European Journal of Multidisciplinary Studies, vol. 2, no. 5, pp. 31-43, 2017.
  19. T. L. Saaty, "Decision Making with Dependence and Feedback: The Analytic Network Process," RWS Publications, vol. 4922, no. 2, 1996.
  20. T. L. Saaty, "Fundamentals of The Analytic Network Process — Dependence and Feedback in Decision-Making with a Single Network," Journal of Systems Science and Systems Engineering, vol. 13, no. 2, pp. 129-157, 2004.
  21. E. L. Subandi, Widiatmaka, and M. Ardiansyah, "Use of WLC (Weighted Linear Combination) to Determine Land Priorities for Development of Paddy Fields in Gorontalo Regency, Indonesia," International Journal of Engineering and Management Research, vol. 9, no. 3, pp. 58-63, 2019.
  22. S. Yin, J. Li, et al., "Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land use and Regional Differences," Sustainability, vol. 12, no. 23, pp. 1-26, 2020.
  23. J. J. Silva-Gallegos, C. A. Aguirre-Salado, et al., "Locating Potential Zones for Cultivating Stevia Rebaudiana in Mexico: Weighted Linear Combination Approach," Sugar Tech, vol. 19, pp. 206-218, 2017.
  24. F. Mehdipour, M. S. Mesgari, B. Golchin, and M. R. Akbari, "Facility Sitting Using GIS and Genetic Algorithms," 25th International Cartographic Conference, vol. 1, pp. 1816-1823, 2011.
  25. S. Rahimi, M. Shadman Roodposhti, and R. Ali Abbaspour, "Using combined AHP-genetic algorithm in artificial groundwater recharge site selection of Gareh Bygone Plain, Iran," Environmental Earth Sciences, vol. 72, pp. 1979-1992, 2014.
  26. M. Kumar, D. M. Husain, N. Upreti, and D. Gupta, "Genetic Algorithm: Review and Application," International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 451-454, 2010.
  27. F. Buontempo, "Genetic Algorithms and Machine Learning for Programmers," The Pragmatic Bookshelf, vol. 52, no. 1, pp. 123-230. 2019.
  28. S. Van Dijk, D. Thierens, and M. De Berg, "Using Genetic Algorithms for Solving Hard Problems in GIS," GeoInformatica, vol. 6, pp. 381-413, 2002.
  29. N. Nagkoulis, E. Loukogeorgaki, and M. Ghislanzoni, "Genetic Algorithms-Based Optimum PV Site Selection Minimizing Visual Disturbance," Sustainability, vol. 14, no.19, p.12602, 2022.
  30. E. Noorollahi, D. Fadai, M. A. Shirazi, and S. H. Ghodsipour, "Land Suitability Analysis for Solar Farms Exploitation Using GIS and Fuzzy Analytic Hierarchy Process (FAHP) - A Case Study of Iran," Energies, vol. 9, no. 8, p. 643, 2016.
  31. J. Van Hoesen, and S. Letendre, "Evaluating Potential Renewable Energy Resources in Poultney, Vermont: A GIS-Based Approach to Supporting Rural Community Energy Planning," Renewable Energy, vol. 35, no. 9, pp. 2114-2122, 2010.
  32. K. R. Lee, W. H. Lee, "Solar Power Plant Location Analysis Using GIS and Analytic Hierarchy Process," Journal of the Korean Association of Geographic Information Studies, vol. 18, no. 4, pp. 1-13, 2015.

 

Journal of Green Energy Research and Innovation
Volume 1, Issue 4
Autumn 2024
Pages 47-63

  • Receive Date 14 April 2024
  • Revise Date 27 April 2024
  • Accept Date 30 April 2024
  • Publish Date 01 December 2024

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us