Advertisement

Development of a New Support Mechanism to Calculate Feed-in Tariffs for Electricity Generation from Renewable Energy Sources in Turkey

  • Mustafa Yurdakul
  • Yusuf Tansel İçEmail author
Original Research Paper
  • 12 Downloads

Abstract

Turkish government agencies support capital investments in electricity generation from renewable energy sources. When making support decisions related with renewable electrical energy sources, the government agencies should consider various issues such as renewability, cleanliness, origin of the source, supply security, cost per kilowatt hour (kWh), and total electricity generation capacity. The tariff mechanism being used in Turkey provides constant rates per kWh of electricity generated from renewable energy sources. The levels of the rates are determined to stimulate renewable energy sources’ usage. In this paper, instead of a constant rate, a feed-in tariff is calculated for each individual electricity generation project using renewable energy source and its level is increased according to the source’s desirability with respect to other renewable energy sources. Various criteria are taken into account in determination of electrical energy sources’ desirability. Furthermore, a combination of two multi-criteria decision-making (MCDM) approaches (the fuzzy versions of the analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)) is used in obtaining a ranking among alternative renewable electrical energy sources. The developed support model’s applicability is illustrated in this paper. The new model developed in this paper has many key benefits. For example, for an individual renewable electrical energy project, final cost per kWh can be calculated and multiplied by new Support Constant to calculate feed-in tariff purchase price per kWh. In another key benefit of the developed model, only local instead of state-wide renewable electrical energy projects can be compared within the AHP-TOPSIS decision hierarchy.

Keywords

Multi-criteria decision-making (MCDM) Renewable electrical energy sources Electrical energy planning Feed-in tariff calculation Fuzzy TOPSIS Fuzzy AHP 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. Balin A, Baraçli H (2017) A fuzzy multi-criteria decision making methodology based upon the interval type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey. Technol Econ Dev Econ 23(5):742–763CrossRefGoogle Scholar
  2. Camadan E (2011) An assessment on the current status and future of wind energy in Turkish electricity industry. Renew Sust Energ Rev 15(9):4994–5002CrossRefGoogle Scholar
  3. Chen S-J, Hwang C-L (1992) Fuzzy multiple attribute decision making. Springer-Verlag, GermanyCrossRefGoogle Scholar
  4. Dhull S, Narwal MS (2018) Prioritizing the drivers of green supply chain management in Indian manufacturing industries using fuzzy TOPSIS method: government, industry, environment, and public perspectives. Process Integr Optim Sustain 2:47–60CrossRefGoogle Scholar
  5. Gökçınar, R. E. (2015). Rüzgar Enerjisi Fayda-maliyet Analizi ve Hibrit Sistemler. Doctoral dissertation, Fen Bilimleri Enstitüsü, İTÜ, İstanbul, TurkeyGoogle Scholar
  6. Gözen M (2014) Renewable energy support mechanism in Turkey: financial analysis and recommendations to policymakers. Int J Energy Econ Policy 4(2):274–287Google Scholar
  7. Ic YT, Yurdakul M (2009) Development of a decision support system for machining center selection. Expert Syst Appl 36:3505–3513CrossRefGoogle Scholar
  8. Ic YT, Yurdakul M, Dengiz B (2013) Development of a decision support system for robot selection. Robot Cim-Int Manuf 29:142–157CrossRefGoogle Scholar
  9. Internet: Nuclear Power in Turkey (2017) http://www.world-nuclear.org/information-library/country-profiles/countries-t-z/turkey.aspx. Accessed 11 Sept 2017
  10. Ishfaq S, Ali S, Ali Y (2018) Selection of optimum renewable energy source for energy sector in Pakistan by using MCDM approach. Process Integr Optim Sustain 2:61–70CrossRefGoogle Scholar
  11. Kabak M, Dağdeviren M (2014) Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Convers Manag 79:25–33CrossRefGoogle Scholar
  12. Kahraman C, Kaya İ (2010) A fuzzy multicriteria methodology for selection among energy alternatives. Expert Syst Appl 37(9):6270–6281CrossRefGoogle Scholar
  13. Kaya T, Kahraman C (2010) Multi criteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: the case of İstanbul. Energy 35(6):2517–2527CrossRefGoogle Scholar
  14. Kaya K, Koç E (2015) Cost analysis of energy generation plants. Eng Mach 56(660):61–38 (In Turkish)MathSciNetzbMATHGoogle Scholar
  15. Klein SJW, Whalley S (2015) Comparing the sustainability of U.S. electricity options through multi-criteria decision analysis. Energy Policy 79:127–149CrossRefGoogle Scholar
  16. Kreith F, Goswami DY (eds) (2007) Handbook of energy efficiency and renewable energy. Crc Press. Taylor and Francis Group, USA.  https://doi.org/10.1201/9781420003482 Google Scholar
  17. Maxim A (2014) Sustainability assessment of electricity generation technologies using weighted multi-criteria decision analysis. Energy Policy 65:284–297CrossRefGoogle Scholar
  18. Parlaktuna M, Mertoglu O, Simsek S, Paksoy H, Basarir N (2013) Geothermal country update report of Turkey (2010–2013). European Geothermal Congress, ItalyGoogle Scholar
  19. Saaty TL (2006) Fundamentals of decision making with the analytic hierarchy process. The analytic hierarchy process series, Vol. 6. RWS Publications, USAGoogle Scholar
  20. Strantzali E, Aravossis K (2016) Decision making in renewable energy investments: a review. Renew Sust Energ Rev 55:885–898CrossRefGoogle Scholar
  21. Streimikiene D, Balezentis T, Krisciukaitiene I, Balezentis A (2012) Prioritizing sustainable electricity production technologies: MCDM approach. Renew Sust Energ Rev 16:3302–3311CrossRefGoogle Scholar
  22. Torabzadeh, Khorasani S (2018) Green supplier evaluation by using the integrated fuzzy AHP model and fuzzy copras. Process Integr Optim Sustain 2:17–25CrossRefGoogle Scholar
  23. Torrini FC, Souza RC, Oliveira FLC, Pessanha JFM (2016) Long term electricity consumption forecast in Brazil: a fuzzy logic approach. Socio Econ Plan Sci 54:18–27CrossRefGoogle Scholar
  24. United Nations (1987). Our Common Future. Report of the World Commission on Environment and Development. UN Documents: Gathering a Body of Global Agreements has been compiled by the NGO Committee on Education of the Conference of NGOs from United Nations web sites with the invaluable help of information & communications technologyGoogle Scholar
  25. Yumurtacı, Z., Bekiroğlu, N. (2011). Reneavable Energy Resourcess and Technologies. International Eco Technologies and Ecologic Facilities Symposium. İstanbul, Turkey. (In Turkish)Google Scholar
  26. Yurdakul M, Ic YT (2005) Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. Int J Prod Res 43(21):4609–4641CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringGazi UniversityAnkaraTurkey
  2. 2.Department of Industrial EngineeringBaskent UniversityAnkaraTurkey

Personalised recommendations