Abstract
Changes in rainfall and streamflow due to climate change have an adverse impact on hydropower generation reliability and scheduling of cascade hydropower stations. To estimate the impact of climate change on hydropower, a combination of climate, hydrological, and hydropower scheduling models is needed. Here, we take the Jinsha River as an example to estimate the impact of climate change on total power generation of the cascade hydropower stations and residual load variance of the power grid. These two goals are solved by applying an improved multi-objective cuckoo search algorithm, and a variety of strategies for the optimal dispatch of hydropower stations are adopted to improve the efficiency of the algorithm. Using streamflow prediction results of CMIP5 climate data, in conjunction with the Xinanjiang model, the estimated results for the next 30 years were obtained. The results indicated that the negative correlation between total power generation and residual load variance under the RCP 2.6 scenario was weaker than that under the RCP 8.5. Moreover, the average power generation and the average residual load variance in RCP 2.6 was significantly larger than that in RCP 8.5. Thus, reducing carbon emissions is not only beneficial to ecological sustainability, but also has a positive impact on hydropower generation. Our approaches are also applicable for cascade reservoirs in other river catchments worldwide to estimate impact of climate change on hydropower development.
Highlights
To evaluate the performance of the GMoCS, the optimal results of the cascade hydropower stations were compared with those obtained using multi-objective cuckoo search algorithm (MoCS)
We focus on the impact of climate change on two objectives: total the power generation objective in the solution set, respectively; f and f are the power generation and residual load variance
Due to thevalue different climate scenarios selected, larger, the reduced power generation due to the increase in the residual load variance was there is a difference in the predicted results
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland.Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).Hydropower is one of the most important renewable, and environmentally sustainable energy resources. It can store energy at low costs, maintains lower operating and maintenance costs, and can operate with great stability [1,2]; thus, when integrated with intermittent energy sources, such as solar and wind, can make a significant contribution to the consistency of the energy grid [3,4]. As shown in the 2019 BP Statistical Review of
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