عنوان مقاله [English]
Energy is the fundamental need of mankind which is essential central to achieving the interrelated goals of modern societies: such as human needs for heating, cooling, lighting, mobility and for running a large diversity of appliances, as well as to supply power and heat to production systems. Global energy resources can be classified in to three main groups, namely fossil energies (oil, gas, coal, etc.), nuclear energy, and renewable energies (wind, solar, geothermal, biomass, etc.). Most of the energy resources currently relied on are finite and will be depleted because of the increasing demand. In addition, there have been serious local air, water, and soil pollution problems as a result of the consumption of various energy resources. It has become clear that continuing to use fossil fuels is not wise not only because of the global impacts on climate system but also the short-term and very long-term impacts on society and the ecosystem. Solar photovoltaic (PV) technology is one of the fastest growing Renewable Energy Resources (RES) technologies worldwide. Harnessing the untapped and unmatched solar potential would not only assist in improving total energy mixture but also reduce the emission of harmful and toxic gases. Multi criteria analysis (MCA) can be defined s formal or structured approaches for individuals or groups to determine overall preferences among alternative options by taking account of multiple criteria and indicators. They have become increasingly popular in decision making on sustainable developments and on energy systems due to their ability to consider and simultaneously evaluate a number of economic, environmental, social and technological aspects, which complex systems require. The purpose of this study is providing a decision support tool for decision makers using Fuzzy AHP as a Multi Criteria Decision Making MCDM technique and GIS tools for investigation and selection of suitable solar farms sites in East Azarbaijan province.
Considering previous studies in the literature and case study of this research, nine criteria including GHI, PVOUT, elevation, slope, aspect, yearly sunshine hours, annual averaged relative humidity, proximity to cities and roads were adopted. GHI and PVOUT layers downloaded from “solargis” website. For generating climatic layers, we applied IDW method using 14 meteorological stations data that downloaded from “East Azarbaijan metrological website”. Elevation, slope and aspect’s layers were created using ASTER satellite Digital Elevation Model (DEM) product. Proximity to cities and roads layers also created using raster calculator tool in ArcGIS 10.3 software. All criteria layers generated in ArcGIS 10.3 software and standardized to [0-1] scale using different equations. In order to perform overlay analysis, all layers’ cell size should be same so cell size for layers determined 29.27 square meters. In some areas, due to legal constraints, engineering or environmental aspects, there is no possibility of deploying solar farms, so three types of areas including protected areas, areas that are so close to cities and areas that are close to active faults defined as constraint areas. To determine the relative weights of the criteria to each other fuzzy AHP technique applied. Finally, sensitivity analysis has been performed on the results of AHP in order to validate the results.
Results and discussion
The results obtained for AHP method show that PVOUT and GHI criteria have highest priority weights and annual averaged relative humidity and yearly sunshine hours have lowest priority weights. in order to generate primary suitability map, criteria and their weights combined using raster calculator tool. highest suitability was 0.8 and lowest was 0.0628. Then constraint layers created using buffer tool integrated to one layer and converted to a binary layer with 0 and 1 values; that 0 indicates that places including constraint and 1 indicates places that there is no constraint there. In order to generate final suitability map, the final binary constraint layer applied to primary suitability map using multiplication operator in raster calculator tool. Results show that 47.99% of study area has weak suitability, 27.10% of study has moderate suitability, 18.31% of study area has good suitability and, 06.60% of study area has great suitability for solar farm deployment. So that west and southwest areas of study area has most suitability and north and northeast areas has least suitability. This is because of west and southwest areas mostly are flat areas with high values of GHI and PVOUT and north and northeast areas despite of having low elevation, values of GHI, PVOUT, yearly sunshine hours, annual averaged relative humidity are not suitable in this areas. For conducting sensitivity analysis, 3 criteria weight scenarios were considered. In first scenario equal weights assigned to all criteria, in second scenario higher weights assigned to Elevation, slope and, aspect criteria and other six criteria weights considered equal. Finally, in third scenario higher weights assigned to GHI and PVOUT criteria. It should be noted that in all scenarios total weight of all nine criteria is 1 (or 100%).
In this study site selection for solar farms using Multi Criteria Decision Making techniques and Geographic Information System was conducted. Nine criteria were adopted and relative priority weights using Fuzzy AHP method were calculated. PVOUT criterion has highest weight and yearly sunshine hour criteria has lowest weight. Layer standardization and overlay analysis was conducted in ArcGIS 10.3. three type of areas considered as constraint areas and applied to primary suitability map. results show that forest and mountain areas are not suitable for solar farms and on the other side smooth and flat areas that mostly located in west and southwest of study area are more suitable for solar farms. policy makers and planners can use results of this study for energy supply of cities using solar energy as one of renewable and cleanest energies. Also methodology used in this study can be performed in other areas with similar conditions.
Solar energy, Geographic Information System (GIS), Fuzzy AHP, Multi Criteria Decision Making (MCDM), sensitivity analysis
10. Dincer, I., 2000, Renewable Energy and Sustainable Development: A Crucial Review. Renewable and Sustainable Energy Reviews, Vol. 4, PP. 75–157.
11. Doorga, J. R. S, Rughooputh, S. D. D. V., and Boojhawon, R., 2018, Multi-Criteria GIS-Based Modelling Technique for Identifying Potential Solar Farm Sites: A Case Study In Mauritius, Renewable Energy, Vol. 133, PP. 1-19.
12. Dozic, S., Lutovac, T., and Kali, M., 2018, Fuzzy AHP Approach to Passenger Aircraft Type Selection, Journal of Air Transport Management, Vol. 68, PP. 165-175.
13. Elliot D., 2007, Sustainable Energy: Opportunities and Limitations, London: Palgrave Macmillan.
14. Ferroukhi, R., Gielen, D., Kieffer, G,. Taylor, M., Nagpal, D., and Khalid, A., 2014, Rethinking Energy: Towards a New Power System, Int Renew Energy Agency (IRENA).
15. Hung, M. M. H., and Yang, W., 2007, A Novel Sustainable Decision Making Model for Municipal Solid Waste Management, Waste Management, Vol. 27, PP. 209–19.
16. IPCC. IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. In: Edenhofer, O., Pichs-Madruga, R., Sokona, Youba, Sayboth, K., Zwickel, T., Eickemeier, P., Hansen, G., Von Stechow, C., Matschoss, P., Kander, S., Schlömer, S., Editors., 2011 United Kingdomand New York, NY, USA: Cambridge University Press. P. 1075 Prep. for Working Group III Intergovernmental Panel on Climate Change.
17. Johnson, K. C., 2010, A Decarbonization Strategy for the Electricity Sector: New-Source Subsidies, Energy Policy, Vol. 38, PP. 2499–507.
18. Liou, T. S., J. Wang, M. J., 1992, Ranking Fuzzy Numbers with Integral Value, Fuzzy Sets and Systems, Vol. 50, PP. 247-255.
19. Kahraman, C., Kaya, İ., and Cebi, S., 2009, A Comparative Analysis for Multiattribute Selection Among Renewable Energy Alternatives Using Fuzzy Axiomatic Design and Fuzzy Analytic Hierarchy Process., Energy, Vol. 34, PP. 1603–16.
20. Kowalski, K., Stagl, S., Madlener, R., and Omann, I., 2009, Sustainable Energy Futures: Methodological Challenges in Combining Scenarios and Participatory Multi- Criteria Analysis, European Journal of Operational Research,Vol. 197, PP. 1063–74.
21. Ku, C.Y., Chang, C.T., and Ho, H. P., 2010, Global Supplier Selection Using Fuzzy Analytic Hierarchy Process and Fuzzy Goal Programming, Journal of Quality and Quantity, Vol. 44, PP. 623-640.
22. Najafi, G., Ghobadian, B., Mamat, R., Yusaf, T., and Azmi, W. H., 2015, Solar Energy in Iran: Current State and Outlook, Renewable and Sustainable Energy Reviews, Vol. 49, PP. 931-942.
23. Pokehar, S. D., and Ramachandran, M., 2004, Application of Multi Criteria Decision Making to Sustainable Energy Planning-A Review, Renewable and Sustainable Energy Reviews, Vol. 8, PP. 365–81.
24. Panwar, N. L., Kaushik, S. C., and Kothari, S., 2011, Role of Renewable Energy Sources in Environmental Protection: A Review, Renewable and Sustainable Energy Reviews, Vol. 15, PP. 1513–24.
25. Saaty, T. L., 2008, Decision Making with the Analytic Hierarchy Process, International Journal of Services Sciences, Vol. 1, PP. 83–98.
26. Sabziparavar, A., and Shetaee, H., 2007, Estimation of Global Solar Radiation in Arid and Semi-Arid Climates of East and West Iran, Energy, Vol. 32, PP. 649–55
27. Sanchez-Lozano, J. M., Antunes, C.H., Garcia-Cascales M.S., and Dias L. C., 2014, GIS-Based Photovoltaic Solar Farms Site Selection Using ELECTRE-TRI: Evaluating the Case for Torre Pacheco, Murcia, Southeast of Spain, Renewable Energy Vol. 66, PP. 478-479.
28. Sindhua, S., Nehraa, V., and Luthra, S., 2017, Investigation of Feasibility Study of Solar Farms Deployment Using Hybrid AHP-TOPSIS Analysis: Case Study of India, Renewable and Sustainable Energy Reviews, Vol. 73. PP. 496–511.
29. Sindhu, S. P., Nehra, V., and Luthra, S., 2016, Recognition and Prioritization of Challenges in Growth of Solar Energy Using Analytical Hierarchy Process: Indian Outlook, Energy, Vol. 100, PP. 332–48.
30. Strantzali, E., and Aravossis, K., 2016, Decision Making in Renewable Energy Investments: A Review, Renewable and Sustainable Energy Reviews, Vol. 55, PP. 885-898.
31. Tahri, M., Hakdaoui, M., and 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, Vol. 51, PP. 1354–1362.
32. Talinli, I., Topuz, E., Aydin, E., and Kabakcı, S. B., 2011, A Holistic Approach for Wind Farm Site Selection by FAHP, Wind Farm: Technical Regulations, Potential Estimation and Siting Assessment Intech, Croatia, PP. 213–34.
33. Uyan, M., 2013, 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.
34. Vafaeipour, M., Zolfani, SH., Varzandeh, M. H. M., Derakhti, A., and Eshkalag, M. K., 2014, Assessment of Regions Priority for Implementation of Solar Projects In Iran: New Application of a Hybrid Multi-Criteria Decision Making Approach, Energy Convers Manag, Vol. 86, PP. 653–63.
35. Wang Y., and Chin K., 2011, Fuzzy Analytic Hierarchy Process: A Logarithmic Fuzzy Preference Programming Methodology, International Journal of Approximate Reasoning, Vol. 52, PP. 541–553.
36. Winebrake, J. J., and Creswick, B. P., 2003, The Future of Hydrogen Fueling Systems for Transportation: An Application of Perspective-Based Scenario Analysis Using the Analytic Hierarchy Process, Technol Forecast Soc Change, Vol. 70. PP. 359–84.
37. Zoghi, M., Ehsani, A., Sadat, M., Amiri, M., and Karimi, S., 2017, 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.