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Providing clean water for energy generation is of particular importance. Due to limited water resources, hydropower generation is facing problems in today’s world societies. Due to this issue, the purpose of this study is to investigate the optimal management of water resources and its impact on optimal hydropower generation. The innovation of this research is the use of a new version of Developed Wildebeest Herd Optimization (DWHO) to forecast hydropower generation. This proposed algorithm solves optimization problems and increases the accuracy of the results obtained for reservoir operation to generate power. The results of this study showed that the developed algorithm has the highest convergence speed and utilizes minimum time-consuming mathematical processes to reach the global solution and prevents trapping in local solutions. The results related to the estimation of power showed that the DWHO method produces about 17% more electricity than other compared algorithms. Also, the highest reliability index 89.7% and resilience index 68.1% and the lowest vulnerability index 12.8% belong to the DWHO method.

In this study, an attempt is made to manage the gap between energy demand and energy supply by predicting hydropower production, energy demand, and greenhouse gas emissions. The interaction between climatic, hydrological, and socio-economic parameters creates a nonlinear and uncertain relationship. The complexity of this nonlinear relationship necessitate the use of ANN to estimate energy demand. To predict energy demand, ANN model is used along with improved Electromagnetic Field Optimization (IEFO) algorithms. The results show, hydroelectric generation in the near future under RCP2.6, RCP4.5, and RCP8.5 is decreased 10.981 MW, 12.933MW, and 14.765MW and in the far future decreased 21.922 MW, 23.649 MW, and 26.742 MW. The energy demand increases in the near future 513 MW and far future 1168 MW. According to forecasting hydropower generation and energy demand, the gap between the demand-supply will increase. Also, the greenhouse gases emissions is increase due to the increase in fossil fuel consumption.

The world’s climate has changed dramatically in recent years due to the development of the industry. Climate change can significantly affect hydropower plants. One of the negative effects of climate change is the reduction of hydropower generation. Therefore, to better management of power supply and demand, the climate change effect on hydropower plants should be examined. The main purpose of this study is the prediction of future hydropower generation (2021–2050) in terms of climate change. One of the factors sensitive to climate change in hydropower plants is the amount of input flow to the reservoir. In this study, a method has been used that can increase the accuracy of flow estimation. This innovation is the use of the ANN model under the new version of the Developed Crow Search Algorithm (DCSA). This algorithm increases the accuracy of prediction by solving optimization disadvantages such as getting stuck in the optimal location, the imbalance between exploitation and exploration at different levels The results showed that the DCSA algorithm with minimum error (MSE = 1.06) and maximum correlation (R2 = 0.88) compared to other algorithms has the best performance. The results of the prediction of climate change under RCPs scenarios showed that the average annual power generation will decrease under RCP2.6 and RCP4.5 and RCP8.5 about 10.74% and% 16.38 and 22.25% respectively. Also, the average annual power generation under scenarios RCP2.6, RCP4.5, and RCP8.5 by 2050 are 740.33 MW, 603.12 MW, and 585.77 MW, respectively.

In the study area, due to the impact of climate change on hydropower generation and energy demand has been investigated the impact of climate change on hydropower generation, and the changes between demand and supply. To predict hydropower generation is utilized Representative Concentration Representative (RCPs) scenarios. Due to the effect of various influential factors such as climate, economy, and social on energy demand, in this study, a proposed model is used to increase the accuracy of energy demand forecasting. This model is an optimal Artificial Neural Network (ANN) model using the Developed Pathfinder Algorithm (DPA) The results of forecasting the climatic parameters showed that in the next 25 years, the temperature will increase and rainfall will decrease. Changes in hydropower generation under RCP2.6 and RCP 8.5 163.3 MW and 188.3 MW will be less than the base time. The results of the energy demand simulation showed that due to the impact of population, Gross Domestic Product (GDP), and rising temperatures, society would face an increasing trend of energy demand. However, this demand will be higher in some seasons due to the increase in the consumption of cooling devices. The electricity demand increased by about 91.42 MW compared to the base period. Examining the changes in demand and energy production, it was found that energy demand would be higher than energy production. Therefore, there is probably a shortage of energy in the future.

The objective of this study is to estimate the river’s capacity power generation and identify proper sites to install small hydropower stations The innovation in this study is estimating the river flow rate using different versions of the Soil and Water Assessment Tools (SWAT) model. Evaluating the flow rivers using different models helps to increase the accuracy of estimating river discharge and it can be effective in accurately identifying the river’s hydroelectric potential. In this investigation, an Artificial Neural Network (ANN) alongside the SWAT model has been used to increases the model’s ability to estimate streams flow. Also, an optimized algorithm called Improved the Pathfinder Optimizer (IPFO) have been utilized to reduce error during the learning process. The results of this study showed that among the three proposed models, SWAT-ANN-IPFO has the best estimate for river flow. Then, using the SWAT-ANN-IPFO model and Geographic Information system (GIS) software assessed the power potential sites along the rivers. In this study, the selection of proper sites for the installation of small hydropower stations based on physiographic and hydrological characteristics has been evaluated. Two criteria, slope and discharge for this purpose have been investigated. In this study, in total, 2,031 suitable points identified for installing hydroelectric power stations. The class 4 waterways are the most prone to installing hydroelectric power plants, and the identified sites are almost 846 because it has more discharge volume and more head differences than other order streams.