ارزیابی مکان‌های مناسب برای مزارع خورشیدی با استفاده از سیستم اطلاعات جغرافیایی و روش‌های تصمیم‌گیری چندمعیاره (مطالعة موردی: استان آذربایجان شرقی)

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 دانشیار سنجش‌ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران

2 دانشجوی دکتری سنجش‌ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران

چکیده

انرژی از نیازهای اصلی بشر و موتور محرک توسعة اقتصادی است. منابع انرژی سنتی محدود و آلاینده هستند و این موضوع دولت‌ها را خواهان جایگزینی انرژی‌های تجدیدپذیر به‌جای منابع انرژی سنتی کرده است. یکی از منابع انرژی‌های تجدیدپذیر انرژی خورشیدی است که برای استفاده از آن از فناوری سلول‌های خورشیدی استفاده می‌کنند. در ایران، ارادة قوی سیاسی برای توسعة منابع انرژی تجدیدپذیر وجود دارد، اما یکی مهم‌ترین موضوعات در این باره یافتن مکان بهینه برای استقرار صفحات خورشیدی است. در پژوهش حاضر، با استفاده از سیستم اطلاعات جغرافیایی و تکنیک Fuzzy AHP که یکی از روش‌های تصمیم‌گیری چندمعیاره است، مکان‌های مناسب برای استقرار نیروگاه خورشیدی در استان آذربایجان شرقی شناسایی شده است. براساس نتایج این پژوهش، 60/6 درصد از اراضی استان از تناسب عالی، 31/18 درصد از تناسب خوب و 10/27 درصد از تناسب متوسط برای نصب نیروگاه خورشیدی برخوردارند، اما 99/47 درصد اراضی استان مطلوبیت لازم را برای نصب نیروگاه خورشیدی ندارند. در حالت کلی مناطق غربی و جنوب غربی استان بیشترین تناسب و مناطق شمالی و شمال شرقی استان کمترین تناسب را از دیگر مناطق برای نصب نیروگاه خورشیدی دارند. همچنین تحلیل حساسیت وزن‌های به‌دست‌آمده از روش Fuzzy AHP نشان می‌دهد معیارهای GHI و PVOUT اهمیت زیادی در تعیین مناطق مناسب برای بهره‌برداری از انرژی خورشیدی دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Evaluation of suitable Sites for Solar Farms by Multi Criteria Decision Making in GIS (Case Study: East Azarbaijan Province)

نویسندگان [English]

  • Najmeh Neisani Samani 1
  • Amir Tahouni 2
1 Associate Professor of RS and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 MA in RS and GIS, Faculty, Faculty of Geography, University of Tehran Tehran, Iran
چکیده [English]

Introduction 
Energy as the fundamental need of mankind is essential for modern societies to achieve the interrelated goals: such as human needs for heating, cooling, lighting, mobility and for running a large diversity of appliances, it is necessary to supply power and heat to production systems. Global energy resources can be classified into three main groups, namely fossil energies (oil, gas, coal, etc.), nuclear energy, and renewable energies (wind, solar, geothermal, biomass, etc.). Most of the energy sources currently relied on finite resources 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 consumption of the various energy resources. It has become clear that application of fossil fuels is not a wise option not only because of its 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 technologies in using Renewable Energy Resources (RES) 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 as 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 simultaneously a number of economic, environmental, social and technological aspects. The purpose of this study is to provide 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 farm sites in East Azarbaijan province.
Methodology 
Based on the experiences of the previous studies in the literature and case study subject of this research, we selected nine criteria including GHI, PVOUT, elevation, slope, aspect, annual sunshine hours, average annual relative humidity, proximity to cities and roads. GHI and PVOUT layers are downloaded from “solargis” website. For generating climatic layers, we applied IDW method using 14 meteorological stations data downloaded from “East Azarbaijan metrological website”. Elevation, slope and aspect’s layers were created using ASTER satellite Digital Elevation Model (DEM). The layers of proximity to cities and roads have also been created using raster calculator tool in ArcGIS 10.3 software. All criteria have been generated in ArcGIS 10.3 software and standardized in a [0-1] scale using different equations. In order to perform overlay analysis, all layers have been set to same cell size of 29.27 square meters. In some areas, due to legal constraints, engineering or environmental aspects, there is no possibility to deploy solar farms. Thus, three types of areas including protected areas, areas so close to cities and areas close to active faults have been defined as constraint areas. We applied fuzzy AHP technique to determine the relative weights of the criteria to each other. Finally, sensitivity analysis has been performed on the results of AHP in order to validate the outputs. In order to generate primary suitability map, the criteria and their weights have been combined using ArcGIS Raster Calculator. The highest suitability was 0.8 and the lowest was 0.0628 in the results. Then, constraint layers have been created using buffer tool and converted into a binary layer with 0 and 1 values in which the number 0 represents the places of the constraint and 1 represents those with no constraint. In order to generate final suitability map, the final binary constraint layer is applied to primary suitability map using multiplication operator by raster calculator tool.
Results and discussion 
The results obtained by AHP method show that PVOUT and GHI criteria have the highest priority and annual average relative humidity and annual sunshine hours have the lowest priority. The results show that 47.99% of the study area has weak suitability, 27.10% of that has moderate suitability, 18.31% has good suitability, and 06.60% has great suitability for solar farm deployment. The west and southwest areas of the study area are the most suitable and north and northeast areas are the least suitable. This can be argued that the west and southwest areas are mostly flat areas with high values of GHI and PVOUT and north and northeast areas are not suitable. To conduct sensitivity analysis, 3 criteria of weight scenarios were considered. In first scenario, equal weights are assigned to all criteria, in second scenario the higher weights are assigned to elevation, slope, and aspect criteria and weights of other six criteria considered equal. Finally, in third scenario the higher weights are assigned to GHI and PVOUT criteria. It should be noted that in all scenarios, the total weight of all nine criteria is 1 (i.e., 100%). 
Conclusion 
In this study site selection for solar farms has been conducted by Multi Criteria Decision Making techniques and Geographic Information System. Nine criteria were adopted by relative priority weights using Fuzzy AHP method. The PVOUT criterion has the highest weight and annual sunshine hour criterion has the lowest weight. Layer standardization for overlay analysis was conducted in ArcGIS 10.3. Three areas as constraint zones are applied to primary suitability map. The results show that forest and mountain areas are not suitable for solar farms and on the other side smooth and flat areas are mostly located in west and southwest parts of the study area as they are more suitable for solar farms. Policy makers and planners can use the results of this study for energy supply using solar energy as one of renewable and cleanest energy sources. The methodology used in this study can be performed in other areas with similar conditions.

کلیدواژه‌ها [English]

  • Solar energy
  • Geographic Information System (GIS)
  • Fuzzy AHP
  • Multi Criteria Decision Making (MCDM)
  • Sensitivity analysis
  1. مرکز آمار ایران https://www.amar.org.ir..
  2. مهاجرزاده، محمد، معصومی، رحیم، کمالی محمدرضا، 1389،  اصول و معیارهای مکان‌یابی صنایع راهبردی، انتشارات مبنای خرد.
    1. Alamdari, P., Nematollahi, O., and Alemrajabi, A., 2013, Solar Energy Potentials in Iran: A Review, Renewable and Sustainable Energy Reviews, Vol. 21, PP. 778–788.
    2. Al Garni, H., and Awasthi, A., 2017, Solar PV Power Plant Site Selection Using a GIS-AHP Based Approach with Application in Saudi Arabia, Applied Energy Vol. 206, PP. 1225–1240.
    3. Aragonés-Beltrán, P., Chaparro-González, F., Pastor-Ferrando, J-P., and Pla-Rubio, A., 2014, An AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process)-Based Multi-Criteria Decision Approach for the Selection of Solar-Thermal Power Plant Investment Projects, Energy, Vol. 66, PP. 222–38.
    4. Belton, V., and Stewart T. J., 2002 Multiple Criteria Decision Analysis: An Integrated Approach, Norwell, Massachusetts: Kluwer Academic Publishers.
    5. Bunruamkaew, K., and Murayama, Y., 2011, Site Suitability Evaluation for Ecotourism Using GIS and AHP: A Casestudy of Surat Thani Province, Thailand. Procedia Soc Behav Sci, Vol. 21, PP. 269–78.
    6. Candelise, C., Winskel, M., and Gross, R. J. K., 2013, The Dynamics of Solar PV Costs and Prices As a Challenge for Technology Forecasting, Renewable Aand Sustainable Energy Reviews, Vol. 26, PP. 96–107.
    7. Diakoulak, I. D., Henggeler Antunes, D., and Gomes, Martins A., 2005, MCDA Aand Energy Planning, In: Figueira J, Greco S, Ehrgott M, Editors, Multiple Criteria Decision Analysis: State of the Art Surveys, United States of America: Springer.
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.