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Electricity supply and demand dynamics in Iran considering climate change-induced stresses

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  • Zamanipour, Behzad
  • Ghadaksaz, Hesam
  • Keppo, Ilkka
  • Saboohi, Yadollah

Abstract

The impacts of climate change such as water scarcity and air temperature increase affect the development of the electricity supply system. In countries like Iran, where cooling demand in the residential sector is high and water availability is fairly low, this can create significant challenges. In this study, an econometric approach was used to project the electricity demand for four sectors in Iran, considering the effect of changing air temperature. Also, the impact of water scarcity on the optimal expansion of the power system was investigated using TIMES, a bottom-up energy system optimization modeling platform, to build a model for the Iranian electricity system with each power plant unit modeled separately. The option of switching cooling technology was also included in the modeling. The results show that a fuel price increase alters the optimal capacity and cooling technology portfolio substantially. Renewables become more desirable under the scenarios in which the fuels and water prices, as well as the carbon taxes, are high. The emissions increase in all scenarios, although the carbon tax moderates this increase. The water withdrawal declines in the high water price scenarios. The sensitivity analysis of water price revealed the effect of this parameter on water withdrawal.

Suggested Citation

  • Zamanipour, Behzad & Ghadaksaz, Hesam & Keppo, Ilkka & Saboohi, Yadollah, 2023. "Electricity supply and demand dynamics in Iran considering climate change-induced stresses," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222030043
    DOI: 10.1016/j.energy.2022.126118
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    2. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).

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