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Yenilenebilir Enerji Çalışmalarında Bölge Seçimi Problemlerini Etkileyen Kriterlerin Önem Sıralarının Belirlenmesi

Year 2022, Volume: 14 Issue: 2, 475 - 491, 31.07.2022
https://doi.org/10.29137/umagd.1034298

Abstract

Son yıllarda artan nüfus ile birlikte ortaya çıkan çevresel endişeler ve tükenen kaynak rezervleri yenilenebilir enerji kaynaklarına olan ihtiyacı arttırmıştır. Hali hazırda kullanılan fosil yakıtların gerek çevreyi emisyon kirliliğine itmesi gerekse tükenebilen bir kaynak olması öneminin azalmasına neden olmuş ve fosil yakıtların kullanılmamasının tercih edileceği bir dünya düzenine geçilmiştir. Bu sebeple de yenilenebilir enerji kaynakları, fosil yakıt tüketimine karşılık temiz ve sürdürülebilir enerji üretimi için önemli bir seçenektir. Bu kadar güçlü bir dönüşüme uyum sağlamak adına ülkelere ve hatta bölgelere, yenilenebilir enerji yatırımı yapmak çalışmanın çıkış noktasını oluşturmaktadır. Bölgelerin var olan etkenlere yönelik, doğru yenilenebilir enerji kaynağına karar vermesi evrensel bir problem haline gelmektedir. Bu noktadan hareketle bu çalışmada yenilenebilir enerji kaynaklarının doğru bölgelere yatırımının yapılması için önem verilmesi gereken kriterlerin belirlenmesi üzerine bir çalışma yapılmıştır. Çalışmada detaylı literatür taraması neticesinde elde edilen kriterler bir araya getirilerek ve uzman görüşleri yardımıyla 139 farklı yenilenebilir enerji kaynağı seçimini etkileyen kriterlere ulaşılmıştır. Yenilenebilir enerji alanında çalışan kişiler tarafından yapılan değerlendirme ile çok sayıda var olan bu kriterlerin en önemlileri belirlenerek; maliyet, çevre, teknik, sosyal, risk ana boyutları altında gruplandırılmış ve tanımları aktarılmıştır. Elde edilen bulgular literatürde yenilenebilir enerji çalışmalarında yer seçimi, kaynak seçimi, yatırım bölgesi kararı problemlerine yönelik yol gösterici niteliktedir.

Supporting Institution

Tarsus Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

MF.20.007

References

  • Akash, B. A., Mamlook, R., & Mohsen, M. S. (1999). Multi-criteria selection of electric power plants using analytical hierarchy process. Electric power systems research, 52(1), 29-35.
  • Al Garni, H., Kassem, A., Awasthi, A., Komljenovic, D., & Al-Haddad, K. (2016). A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable energy technologies and assessments, 16, 137-150.
  • Alizadeh, R., Soltanisehat, L., Lund, P. D., & Zamanisabzi, H. (2020). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, 137, 111174.
  • Alkan, Ö., & Albayrak, Ö. K. (2020). Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy, 162, 712-726.
  • Amer, M., & Daim, T. U. (2011). Selection of renewable energy technologies for a developing county: a case of Pakistan. Energy for sustainable development, 15(4), 420-435.
  • Barry, M. L., Steyn, H., and Brent, A. 2011. Selection of renewable energy technologies for Africa: Eight case studies in Rwanda, Tanzania and Malawi. Renewable Energy, 36(11), 2845-2852.
  • Beccali, M., Cellura, M., & Mistretta, M. (2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable energy, 28(13), 2063-2087.
  • Bento, N., Borello, M., & Gianfrate, G. (2020). Market-pull policies to promote renewable energy: A quantitative assessment of tendering implementation. Journal of Cleaner Production, 248, 119209.
  • Bozkurt, R. (1998). Kalite iyileştirme araç ve yöntemleri (630). Basım Yeri: Milli Prodüktivite Merkezi Yayınları.
  • Georgopoulou, E., Lalas, D., & Papagiannakis, L. (1997). A multicriteria decision aid approach for energy planning problems: The case of renewable energy option. European Journal of Operational Research, 103(1), 38-54.
  • Güler, Ö. (2009). Wind energy status in electrical energy production of Turkey. Renewable and sustainable energy reviews, 13(2), 473-478.
  • Haralambopoulos, D. A., and Polatidis, H. 2003. Renewable energy projects: structuring a multi-criteria group decision-making framework. Renewable energy, 28(6), 961-973.
  • IEA. 2013. CO2 Emissions From Fuel Combustion (2013 Edition).
  • Irfan, M., Zhao, Z. Y., Rehman, A., Ozturk, I., and Li, H. 2020a. Consumers' intentionbased influence factors of renewable energy adoption in Pakistan: a structural equation modeling approach. Environmental Science and Pollution Research, 1-14.
  • Jabeen, G., Yan, Q., Ahmad, M., Fatima, N., and Qamar, S. 2019. Consumers' intentionbased influence factors of renewable power generation technology utilization: a structural equation modeling approach. Journal of Cleaner Production, 237, 117737.
  • Kabak, M., & Dağdeviren, M. (2014). Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy conversion and management, 79, 25-33.
  • Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34(10), 1603-1616.
  • Kaya, T., and Kahraman, C. 2010. Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517-2527.
  • Kim, H., Park, E., Kwon, S. J., Ohm, J. Y., & Chang, H. J. 2014. An integrated adoption model of solar energy technologies in South Korea. Renewable Energy, 66, 523-531.
  • Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., and Bansal, R. C. 2017. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609.
  • Lee, H. C., & Chang, C. T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews, 92, 883-896.
  • Ligus, M., & Peternek, P. (2018). Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia, 153, 101-106.
  • Nigim, K., Munier, N., & Green, J. (2004). Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources. Renewable energy, 29(11), 1775-1791.
  • Özcan, E. C., Ünlüsoy, S., & Eren, T. (2017). A combined goal programming–AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews, 78, 1410-1423.
  • Polatidis, H., Haralambopoulos, D. A., Munda, G., Vreeker, R. 2006. Selecting an appropriate multi-criteria decision analysis technique for renewable energy planning. Energy Sources, Part B, 1(2), 181-193.
  • Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019). A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production, 238, 117936.
  • Ren, J., & Sovacool, B. K. (2015). Prioritizing low-carbon energy sources to enhance China’s energy security. Energy conversion and management, 92, 129-136.
  • Solangi, Y. A., Tan, Q., Mirjat, N. H., & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 236, 117655.
  • Stanek, W., Mendecka, B., Lombardi, L., & Simla, T. (2018). Environmental assessment of wind turbine systems based on thermo-ecological cost. Energy, 160, 341-348.
  • Štreimikienė, D., Šliogerienė, J., & Turskis, Z. (2016). Multi-criteria analysis of electricity generation technologies in Lithuania. Renewable energy, 85, 148-156.
  • Şengül, Ü., Eren, M., Shiraz, S. E., Gezder, V., & Şengül, A. B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable energy, 75, 617-625.
  • Yücenur, G. N., Çaylak, Ş., Gönül, G., & Postalcıoğlu, M. (2020). An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy, 145, 2587-2597.
  • Zheng, G., & Wang, X. (2020). The comprehensive evaluation of renewable energy system schemes in tourist resorts based on VIKOR method. Energy, 193, 116676.

Determining the Importance Order of The Criteria Affecting the Problems of Regional Selection in Renewable Energy Studies

Year 2022, Volume: 14 Issue: 2, 475 - 491, 31.07.2022
https://doi.org/10.29137/umagd.1034298

Abstract

In recent years, environmental concerns and depleted resource reserves that have arisen with the increasing population have increased the need for renewable energy sources. The fact that fossil fuels currently used both push the environment to emission pollution and are an exhaustible resource have led to a decrease in their importance and a world order in which it is preferable not to use fossil fuels has been passed. For this reason, renewable energy sources are an important option for clean and sustainable energy production against fossil fuel consumption. Investing in renewable energy in countries and even regions in order to adapt to such a powerful transformation is the starting point of the study. It is becoming a universal problem for regions to decide on the right renewable energy source for existing factors. From this point of view, in this study, a study was conducted on determining the criteria that should be given importance in order to invest in the right regions of renewable energy resources. In the study, the criteria that affect the selection of 139 different renewable energy sources are reached by bringing together the criteria obtained as a result of the detailed literature review and with the help of expert opinions. With the evaluation made by people working in the field of renewable energy, the most important of these criteria, which exist in many, are determined; are grouped under the main dimensions of cost, environment, technical, social, risk and their definitions are given. The findings obtained are guiding for the problems of site selection, resource selection, investment region decision in renewable energy studies in the literature.

Project Number

MF.20.007

References

  • Akash, B. A., Mamlook, R., & Mohsen, M. S. (1999). Multi-criteria selection of electric power plants using analytical hierarchy process. Electric power systems research, 52(1), 29-35.
  • Al Garni, H., Kassem, A., Awasthi, A., Komljenovic, D., & Al-Haddad, K. (2016). A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia. Sustainable energy technologies and assessments, 16, 137-150.
  • Alizadeh, R., Soltanisehat, L., Lund, P. D., & Zamanisabzi, H. (2020). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, 137, 111174.
  • Alkan, Ö., & Albayrak, Ö. K. (2020). Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy, 162, 712-726.
  • Amer, M., & Daim, T. U. (2011). Selection of renewable energy technologies for a developing county: a case of Pakistan. Energy for sustainable development, 15(4), 420-435.
  • Barry, M. L., Steyn, H., and Brent, A. 2011. Selection of renewable energy technologies for Africa: Eight case studies in Rwanda, Tanzania and Malawi. Renewable Energy, 36(11), 2845-2852.
  • Beccali, M., Cellura, M., & Mistretta, M. (2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable energy, 28(13), 2063-2087.
  • Bento, N., Borello, M., & Gianfrate, G. (2020). Market-pull policies to promote renewable energy: A quantitative assessment of tendering implementation. Journal of Cleaner Production, 248, 119209.
  • Bozkurt, R. (1998). Kalite iyileştirme araç ve yöntemleri (630). Basım Yeri: Milli Prodüktivite Merkezi Yayınları.
  • Georgopoulou, E., Lalas, D., & Papagiannakis, L. (1997). A multicriteria decision aid approach for energy planning problems: The case of renewable energy option. European Journal of Operational Research, 103(1), 38-54.
  • Güler, Ö. (2009). Wind energy status in electrical energy production of Turkey. Renewable and sustainable energy reviews, 13(2), 473-478.
  • Haralambopoulos, D. A., and Polatidis, H. 2003. Renewable energy projects: structuring a multi-criteria group decision-making framework. Renewable energy, 28(6), 961-973.
  • IEA. 2013. CO2 Emissions From Fuel Combustion (2013 Edition).
  • Irfan, M., Zhao, Z. Y., Rehman, A., Ozturk, I., and Li, H. 2020a. Consumers' intentionbased influence factors of renewable energy adoption in Pakistan: a structural equation modeling approach. Environmental Science and Pollution Research, 1-14.
  • Jabeen, G., Yan, Q., Ahmad, M., Fatima, N., and Qamar, S. 2019. Consumers' intentionbased influence factors of renewable power generation technology utilization: a structural equation modeling approach. Journal of Cleaner Production, 237, 117737.
  • Kabak, M., & Dağdeviren, M. (2014). Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy conversion and management, 79, 25-33.
  • Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34(10), 1603-1616.
  • Kaya, T., and Kahraman, C. 2010. Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517-2527.
  • Kim, H., Park, E., Kwon, S. J., Ohm, J. Y., & Chang, H. J. 2014. An integrated adoption model of solar energy technologies in South Korea. Renewable Energy, 66, 523-531.
  • Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., and Bansal, R. C. 2017. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609.
  • Lee, H. C., & Chang, C. T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews, 92, 883-896.
  • Ligus, M., & Peternek, P. (2018). Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia, 153, 101-106.
  • Nigim, K., Munier, N., & Green, J. (2004). Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources. Renewable energy, 29(11), 1775-1791.
  • Özcan, E. C., Ünlüsoy, S., & Eren, T. (2017). A combined goal programming–AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews, 78, 1410-1423.
  • Polatidis, H., Haralambopoulos, D. A., Munda, G., Vreeker, R. 2006. Selecting an appropriate multi-criteria decision analysis technique for renewable energy planning. Energy Sources, Part B, 1(2), 181-193.
  • Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019). A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production, 238, 117936.
  • Ren, J., & Sovacool, B. K. (2015). Prioritizing low-carbon energy sources to enhance China’s energy security. Energy conversion and management, 92, 129-136.
  • Solangi, Y. A., Tan, Q., Mirjat, N. H., & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 236, 117655.
  • Stanek, W., Mendecka, B., Lombardi, L., & Simla, T. (2018). Environmental assessment of wind turbine systems based on thermo-ecological cost. Energy, 160, 341-348.
  • Štreimikienė, D., Šliogerienė, J., & Turskis, Z. (2016). Multi-criteria analysis of electricity generation technologies in Lithuania. Renewable energy, 85, 148-156.
  • Şengül, Ü., Eren, M., Shiraz, S. E., Gezder, V., & Şengül, A. B. (2015). Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable energy, 75, 617-625.
  • Yücenur, G. N., Çaylak, Ş., Gönül, G., & Postalcıoğlu, M. (2020). An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy, 145, 2587-2597.
  • Zheng, G., & Wang, X. (2020). The comprehensive evaluation of renewable energy system schemes in tourist resorts based on VIKOR method. Energy, 193, 116676.
There are 33 citations in total.

Details

Primary Language Turkish
Subjects Industrial Engineering
Journal Section Articles
Authors

Emel Yontar 0000-0001-7800-2960

Project Number MF.20.007
Publication Date July 31, 2022
Submission Date December 8, 2021
Published in Issue Year 2022 Volume: 14 Issue: 2

Cite

APA Yontar, E. (2022). Yenilenebilir Enerji Çalışmalarında Bölge Seçimi Problemlerini Etkileyen Kriterlerin Önem Sıralarının Belirlenmesi. International Journal of Engineering Research and Development, 14(2), 475-491. https://doi.org/10.29137/umagd.1034298

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