Abstract
Hydropower, which is the most extensively used renewable energy, is sensitive to the change of streamflow under the great impact of precipitation. According to the relationship between the hydropower station generation and local precipitation, the impact of precipitation on hydropower can be analyzed. In this paper, the GCM and RCM simulations in precipitation are compared firstly, and the high-resolution precipitation data are then calculated by stepwise clustering analysis (SCA) statistical downscaling method. Secondly, based on the hydropower potential (HP), the hydropower response model driven by precipitation (HRMDP) is established. Finally, the simulated generation of a hydropower station in Dadu River basin is used as a case to validate this proposed model. The results show that precipitation will increase by around 42% from May to August in study region, while it will decrease by 40 % in other months in RCP4.5. For different periods of reservoir scheduling, the precipitation will increase by about 40% in the Neutral I and Wet period, while it will decrease by around 30% in other periods, which will lead to the shortening of the peak period of hydropower generation and the peak value will be decreased. Correspondingly, the results show power generation will decrease by around 12% from June to December and increase by around 4% in the rest months. On the other hand, owing to the changes in precipitation, the future power generation will increase by 25% in Neutral I and decrease by 13.5% in other periods, but the total hydropower generation will remain.
Highlights
According to the IPCC AR5, with the increase of global populations and economies, the man-made emissions of greenhouse gases have remained rising and reached the industrial history peak at the beginning of the 21st century (IPCC, 2013)
Compared with the results of CN05, global climate model (GCM) underestimates the precipitation in most regions of the basin but overestimates the precipitation in the middle of basin
We validated the performance of hydropower response model driven by precipitation (HRMDP), and the results show that the HRMDP model can reflect the relationship between the precipitation and power generation, with the correlation coefficient of 0.95 and RMSE of 611 kWh
Summary
According to the IPCC AR5, with the increase of global populations and economies, the man-made emissions of greenhouse gases have remained rising and reached the industrial history peak at the beginning of the 21st century (IPCC, 2013). A hydropower station in Dadu River basin is taken as a case to quantitatively analyze future climate change influence on hydropower generation. The first step is collecting precipitation and power generation data, which include large-scale HadGEM2-ES climate data, observation of meteorological stations and generation data of hydropower stations. Some independent variables in large scale grids in PRECIS RCM, such as precipitation, temperature, humidity and pressure are extracted firstly, afterward, the precipitation in station scale is simulated and projected by the SCA statistic downscaling method. The observation is used to validate the results of RCM and build the HRMDP It is from the real-time monitoring stations, including daily precipitation, near-surface temperature, surface relative humidity and surface wind speed. Based on the projection in precipitation from dynamical and statistical downscaling results, the influence of precipitation on power generation from 2025 to 2035 will be analyzed
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