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Economically optimized electricity trade modeling: Iran-Turkey case

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  • Shakouri G., H.
  • Eghlimi, M.
  • Manzoor, D.

Abstract

The advantages of power trade between countries, which are attainable for various facts, are distinguished now. Daily differences in the peak-load times of neighboring countries commonly occur for differences in the longitudes of their location. Seasonal differences are also caused by differences in the latitudes leading to different climates. Consequently, different load curves help to have such a production schedule that reduces blackouts and investments for power generation by planning for a proper trade between countries in a region. This paper firstly describes the methodology and framework for the power trade and then the results of an optimal power trade model between Iran and Turkey, which shows a potential benefit for both countries by peak shaving, are presented. The results, in the worst case design, represent optimality of about 1500Â MW electricity export from Iran to Turkey at the Turkish peak times, as well as 447Â MW electricity import from Turkey at the Iranian peak times. In addition, results derived from running a Long-Run model show that there will be greater potential for power export from Iran to Turkey, which is a guideline of an energy conservation strategy for both countries in the future.

Suggested Citation

  • Shakouri G., H. & Eghlimi, M. & Manzoor, D., 2009. "Economically optimized electricity trade modeling: Iran-Turkey case," Energy Policy, Elsevier, vol. 37(2), pages 472-483, February.
    • Handle: RePEc:eee:enepol:v:37:y:2009:i:2:p:472-483
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    References listed on IDEAS

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    1. Peter A. Schulkin, 1993. "Analysis," Challenge, Taylor & Francis Journals, vol. 36(5), pages 59-60, September.
    2. James M. Griffin, 1993. "Methodological Advances in Energy Modelling: 1970-1990," The Energy Journal, , vol. 14(1), pages 111-124, January.
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    Cited by:

    1. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    2. Jiahai Yuan & Yurong Zeng & Xiaoxuan Guo & Yu Ai & Minpeng Xiong, 2018. "Electric Power Investment Risk Assessment for Belt and Road Initiative Nations," Sustainability, MDPI, vol. 10(9), pages 1-21, August.
    3. Selçuklu, Saltuk Buğra & Coit, D.W. & Felder, F.A., 2023. "Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty," Energy Policy, Elsevier, vol. 173(C).
    4. Dagoumas, Athanasios S. & Koltsaklis, Nikolasos E. & Panapakidis, Ioannis P., 2017. "An integrated model for risk management in electricity trade," Energy, Elsevier, vol. 124(C), pages 350-363.
    5. Leila Mirtajadini & Shamsollah Shirin Bakhsh & Mir Hossein Mousavi & Kioumars Heydari & Saman Yousefvand, 2023. "Prediction of Electricity Trade Partners Based on the Network Theory: The West Asia Community," Foreign Trade Review, , vol. 58(4), pages 544-557, November.
    6. Srinivasan, Sunderasan, 2013. "Electricity as a traded good," Energy Policy, Elsevier, vol. 62(C), pages 1048-1052.
    7. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    8. Nikolaos E. Koltsaklis & Athanasios S. Dagoumas, 2021. "A power system scheduling model with carbon intensity and ramping capacity constraints," Operational Research, Springer, vol. 21(1), pages 647-687, March.

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