Use of an optimization model for optimization of Turkey’s energy management by inclusion of renewable energy sources

  • C. O. IncekaraEmail author
Original Paper


In this study, Turkey’s power generation plan between 2018 and 2035 is obtained by using a new mathematical model that aims to make a generation expansion planning to have an acceptable level of a pollutant in the air considering the UN Framework Convention on Climate Change and Kyoto Protocol’s responsibilities of Turkey. The used method is a new fuzzy multi-objective linear programming (MOLP) method by considering the energy objectives of Turkey’s Ministry of Energy (MoE:MENR) and private sector’s energy targets. The multi-objective model aims are (1) energy generation cost minimization of energy production considering all energy-related costs in Turkey, (2) greenhouse gases emission’s reduction, (3) minimizing the imported energy, (4) maximizing efficiency of power plants and (5) minimizing the use of fossil fuels in power plants. By solving this mathematical model, Turkey’s 18-year power generation plan between the years 2018 and 2035 based on mainly renewable sources is formulated, and by 2035 the percentage of renewable energy sources in Turkey’s power generation is increased by 77% which includes 24.7% solar energy, 19.1% wind energy, 18.5% hydro energy, 12.3% biomass under high-demand scenario.


Generation expansion planning (GEP) Renewable energy Climate change Fuzzy MOLP modeling Optimization Turkey 



The authors wish to thank all who assisted in conducting this work.


  1. Antunes CH, Martins AG, Brito IS (2004) A multiple objective mixed integer linear programming model for power generation expansion planning. Energy 29:613–627CrossRefGoogle Scholar
  2. Arnette A, Zobel CW (2012) An Optimization model for regional renewable energy development. Renew Sustain Energy Rev 16:4606–4615CrossRefGoogle Scholar
  3. Borges AR, Antunes CH (2003) A fuzzy multiple objective decision support model for energy-economy planning. Eur J Oper Res 145:304–316CrossRefGoogle Scholar
  4. BOTAS (2010) Annual report, 34–42Google Scholar
  5. BOTAS (2012) Annual report, 79–83Google Scholar
  6. Buehering WA, Hamilton BP, Guziel KA, Cirillo RR (1991) Energy and power evaluation program (ENPEP); An integrated approach for modeling national energy systems. Argonne National Laboratory, 56Google Scholar
  7. Cormio C, Dicorato M, Minoia A, Trovato M (2003) A regional energy planning methodology including renewable energy sources and environmental constraints. Renew Sustain Energy Rev 7:99–130CrossRefGoogle Scholar
  8. Deshmukh SS, Deshmukh MK (2009) A new approach to micro-level energy planning—a case of northern parts of Rajasthan, India. Renew Sustain Energy Rev 13:634–642CrossRefGoogle Scholar
  9. EU Directive 2012/27/EU (2012) Energy efficiency. Amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/ECGoogle Scholar
  10. European Environment Agency (2015) Sulphur dioxide -SO2 emissions reportGoogle Scholar
  11. Green Book (2010) European CommissionGoogle Scholar
  12. Incekara CO (2017) Developing energy optimization models for the establishment of Turkey’s sustainable strategic energy policies and its related implementation steps, PhD Thesis, Çukurova University, AdanaGoogle Scholar
  13. Incekara CO, Ogulata SN (2017) Turkey’s energy planning considering global environmental concerns. Ecol Eng 102:589–595CrossRefGoogle Scholar
  14. Iniyana S, Suganthi L, Samuel A (2006) Energy models for commercial energy prediction and substitution of renewable energy sources. Energy Policy 34:2640–2653CrossRefGoogle Scholar
  15. International Energy Agency (2013) World energy outlook databaseGoogle Scholar
  16. International Energy Agency (2016) World energy outlook special report, 25–26Google Scholar
  17. Jana C, Chattopadhyay N (2004) Block level energy planning for domestic lighting—a multi objective fuzzy linear programming approach. Energy 29:1819–1829CrossRefGoogle Scholar
  18. Kim SC, Min KJ (2004) Determining multi criteria priorities in the planning of electric power generation: the development of an analytic hierarchy process for using the opinions of experts. Int J Manag 21(2):186–193Google Scholar
  19. Lin QG, Huang GH (2010) An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level. Energy Policy 35:2270–2280Google Scholar
  20. Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefGoogle Scholar
  21. Mavrotas G, Diakoulaki D, Papayannakis L (1999) An energy planning approach based on mixed 0-1 multiple objective linear programming. Int Trans Oper Res 6:231–244CrossRefGoogle Scholar
  22. Mezher T, Chedid R, Zahabi W (1998) Energy resource allocation using multi objective goal programming: the case of Lebanon. Appl Energy 61:175–192CrossRefGoogle Scholar
  23. Pokharel S, Chandrashekara M (1998) A multi objective approach to rural energy policy analysis. Energy 23(4):325–336CrossRefGoogle Scholar
  24. Russell SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resources Plan Manag 122(3):165–170CrossRefGoogle Scholar
  25. San Cristóbal JR (2012) A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain. Renew Sustain Energy Rev 16:4461–4464CrossRefGoogle Scholar
  26. Tabucanon MT (1988) Multiple criteria decision making in industry. Elseiver, Amsterdam, pp 37–42Google Scholar
  27. Turkey Ministry of Energy and Natural Resources (2009) Security of energy market supply strategy documentGoogle Scholar
  28. Turkey Ministry of Energy and Natural Resources (2015) Turkey’s electricity statistics, 42–47Google Scholar
  29. Turkey Ministry of Energy and Natural Resources (2016) Turkey’s electricity statistics, 37–41Google Scholar
  30. Turkish Electricity Transmission Company (TEIAS) (2013) Turkish electricity energy generation capacity projections: 10 years, 32–44Google Scholar
  31. UNFCCC (1997) Kyoto Protocol to the United Nations framework convention on climate change. Turkey Ministry of Energy and Natural Resources, Turkey’s electricity statistics. United Nations, NewYorkGoogle Scholar
  32. Voss A, Schlenzig C, Reuter A (1995) MESAP III: a tool for energy planning and environmental management—history and new developments, Konferenzen des Forschungszentrums, JulichGoogle Scholar
  33. Wang J, Jing Y, Zhang C, Zhao J (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13:2263–2278CrossRefGoogle Scholar
  34. WEO (2009) World energy outlook, 122–135Google Scholar
  35. Zadeh LA (1965) Fuzzy Sets. Inf Control 8:338–353CrossRefGoogle Scholar
  36. Zeleny M (1986) Multiple criteria decision making. Mc-Graw-Hill, New York, pp 13–47Google Scholar
  37. Zimmermann HJ (1976) Description and optimization of fuzzy systems. Int J Gen Syst 2:209–215CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.BOTASAnkaraTurkey

Personalised recommendations