Improving water utilization and hydropower generation without increasing flood risk

Significance 

Production and conversion of energy are responsible for approximately 29% of the global greenhouse gas emissions. Fossil fuels are the most dominant energy carriers and supply about 80% of the world’s energy use. Research has shown that renewable energy technologies have considerable environmental benefits and greenhouse gas emissions from hydro-power and wind energy are comparatively less when compared to fossil electricity generation methods. It is therefore beneficial to use renewable energy sources such as solar, wind, and hydropower as substitutes of the fossil electricity generation methods.

In a move to control as well as promote water resources utilization along with hydropower generation, a series of large dams have been set up for flood control, hydropower production,  water supply, and navigation. It is therefore necessary to pay attention to the operation of cascade-reservoir systems to sufficiently address the flow variability for promoting resource management and get maximum benefits of all the objectives. Flood Limiting Water Level is an important parameter of multi-purpose reservoir operation and has a critical role in weighting against hydro-power generation and flood control risk.

Static operation of annual Flood Limiting Water Level can provide sufficient storage for flood control safety, but generally leads to excess spilled water during flood season and shortage of water after the flood subsides. However, with the development of hydro-meteorological forecasting methods, dynamic Flood Limiting Water Level operation can adequately address water utilization benefits without increasing flood risk.

Fi-John Chang, Yanlai Zhou, Alexander B. Chen at National Taiwan University in collaboration with Shenglian Guo, and Pan Liu at Wuhan University in China proposed an adept multi-objective dynamic operating Flood Limiting Water Levels methodology that was based on an evolutionary algorithm with a modified aggregation-decomposition method for optimal operation of three mega cascade reservoirs. The modified aggregation-decomposition method was applied to identifying the upper boundary of Flood Limiting Water Levels of cascade reservoirs. Their research work is published in peer-reviewed journal, Energy.

The research team applied a non-dominated sorting genetic algorithm-II (NSGA-II) to counterbalance the risks of flood control as well as hydropower generation in the dynamic operation of cascade reservoirs. They selected three mega cascade reservoirs in the Yangtze River as a case study. They then compared the optimal solution of the cascade reservoirs operation with those of single reservoir operation.

The authors observed that the minimum power generation risk solution improved largely the water utilization and hydropower generation by 5.43% and 6.48% (3.71billion kW·h/year ), respectively, and mitigated the power generation risk by 5.8%. They also observed that the minimum flood control risk solution could reduce the flood risk and power generation risk by 0.4% and 0.7%, respectively, and could improve the water utilization and hydropower generation by 0.64% and 2.18% (1.25 billion kW·h/year), respectively.

The study successfully demonstrated the proposed method by the authors could improve water utilization and hydropower generation without increasing flood risk. Improving hydropower generation will provide extensive economic benefits and improve the growth in social wellbeing and minimize environmental impacts. Therefore, the proposed method will be a good basis of analysis of a great number of under constructed and constructed mega dams in Yangtze River in order to overcome the bottleneck of new energy development as well as water resources sustainability management.

water utilization and hydropower generation without increasing flood risk Advances in Engineeringwater utilization hydropower generation without increasing flood risk Advances in Engineering

NSGA-II solutions for main-flood season in TGR (Three Georges Reservoir).

FCR: flood control risk; PGR: power generation risk. FLWL: Flood Limiting Water Level

About the author

Fi-John Chang is a distinguished professor in the Department of Bioenvironmental Systems Engineering at National Taiwan University. He received his PhD degree from Purdue University. His research focuses on the application of AI techniques/artificial neural networks in hydro cycle, regional flood forecasts, water resources management, and water-food-energy nexus issues.

About the author

Shenglian Guo is a Professor in the State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China. He received his MS and PhD degrees from National University of Ireland, Dublin, Ireland. His research interests include the global climate change impact assessment, hydrology forecast, reservoir operation as well as nature and human-induced water cycle.

About the author

Yanlai Zhou is a Post-doctor Research fellow in the department of Bioenvironmental Systems Engineering at National Taiwan University, Taipei, Taiwan, ROC. He received his MS and PhD degrees from Wuhan University, Wuhan, China. His research interests include the AI, machine learning, flood frequency analysis, AI-based hydrology forecast and reservoir operation.

About the author

Pan Liu is a Professor in the State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China. He received his MS and PhD degrees from Wuhan University, Wuhan, China. His research interests include the hydrology forecast, reservoir operation and multiple energy operation.

Reference

Yanlai Zhou, Shenglian Guo, Fi-John Chang, Pan Liu, Alexander B. Chen. Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs. Energy, volume 143 (2018), pages 785-796.

 

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