No abstract

No abstract

One of the most sensitive factors affecting hydropower generation is climate change.The objective of this study is to forecast the hydropower generation under the influence of climate change for the next 50 years. For this purpose, the GFDL-CM3 model is used under three scenarios: RCP2.6, RCP4.5, and RCP8.5 to predict precipitation and temperature. Any change in the inlet flow to the turbine will cause changes in the hydropower output. Therefore, the more accurately the flow is estimated, the hydropower generation is forecasted more accurately. In this research, a hydrological-neural network hybrid model for flow prediction is presented in which the Improved Sparrow Search Algorithm (ISSA) has been used. The results forecasting climate parameters showed that the average annual rainfall, runoff, and evaporation have a downward trend compared to the control period. The minimum and maximum annual temperatures have an increasing trend compared to the control period. To evaluate the hydropower generation Aras dam, the HEC-Ressim reservoir model is utilized. By comparing the average annual hydropower with the control period, it is known that the power generation will decrease in future years. The average annual electricity generated by hydropower under the scenario RCP2.6 and RCP4.5 and RCP8.

No abstract

No abstract

No abstract

No abstract

No abstract