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The evolutionary algorithms can solve reservoir operation with a fast convergence rate whereas the major impediments in handling the joint operation of mega cascade reservoirs easily trigger the technical bottlenecks, i.e. trapping into a local optimum, instability and loss of good solutions. This study proposes a methodology that fuses three auxiliary strategies into the Kidney Algorithm (KA) to optimize the hydropower output for conquering the bottlenecks in the KA concerning the joint operation of six mega cascade reservoirs located in the Jin-Sha River basin in China. The proposed theme would contribute to the application of the state-of-the-art evolutionary algorithms in boosting the cleaner hydropower production of mega cascade reservoirs. The three auxiliary strategies are that: firstly, the exploration and exploitation strategy is employed to stimulate the movement of solutions to surmount technical drawback of trapping into a local optimum; secondly, the adaptive strategy is used to automatically adjust algorithm parameter values to overcome the instability problem; lastly, the elitism strategy is introduced to preserve the best solution at every epoch to avoid the loss of good solutions. Our methodology, without expanding or upgrading hydraulic infrastructures, can increase the hydropower production of the six mega cascade reservoirs by 7.8%, as compared with the standard operation policy. The hydropower production can reach 4.8 billion kW·h/year, which can decrease 3.77 billion kg/year in CO 2 emission, and bring 217.44 million USD/year in hydropower benefits. The improved KA can considerably increase the reliability and resilience of hydropower output as well as largely decrease the vulnerability of hydropower output. The results suggest that our methodology can stimulate hydropower output to yield more benefits regarding cleaner production, carbon emission reduction and sustainability. Image 1 • Hybrid of three auxiliary strategies conquers bottlenecks of Kidney Algorithm. • Improved Kidney Algorithm optimizes joint operation of mega cascade reservoirs. • Optimal reservoir operation benefits cleaner production and reduces CO2 emission. [ABSTRACT FROM AUTHOR]

Highlights • Propose successive approximation to solve curse of dimensionality raised in NSGA-II. • NSGA-II with successive approximation optimizes 21 mega-reservoirs’ joint operation. • Lift synergy of hydropower output, flood control, water supply and CO 2 mitigation. Abstract The high complexity of multi-objective joint reservoir operation imposes challenging barriers to the pursuit of optimal hydroelectricity output. Inasmuch as multi-objective evolution optimization algorithms, including the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), are trapped in the curse of dimensionality which could not be effectively solved the multi-objective operation of more than ten reservoirs. This study proposes a methodology that integrate the NSGA-II with a successive approximation approach to optimize the hydropower output for conquering the curse of dimensionality under the joint operation of 21 mega cascade reservoirs located in the Upper Yangtze River Basin of China. The successive approximation approach could effectively decompose the mutually related M-dimensional problem into M individual one-dimensional problems, which ingeniously overcomes the curse of dimensionality. The proposed model is anchored with strategies of advancing impoundment timings and raising water levels of cascade reservoirs. We show that our methodology, without adding or upgrading hydraulic infrastructures, empowers the joint operation to reach 110.79 billion kW·h/year (9.8% improvement) in hydropower output, which could reduce 86.97 billion kg/year in CO 2 emission, and to provide 44.97 billion m3/year in water supply with flood risk less than 0.016. The results suggest that our methodology can spur hydroelectricity output to support China’s tactics in fulfilling the pledge of carbon emission reduction and non-fossil energy expansion to 20% by 2030. [ABSTRACT FROM AUTHOR]

• Propose a multi-objective method to raise mutual benefits of water-food-energy nexus. • Floating photovoltaic deployment on ponds drives up renewable energy output. • Hybrid hydro-floating photovoltaic power generation raises energy efficiency. • Energy-driven solutions improve water-food-energy nexus management. Hybrid hydropower and floating photovoltaic power generation has far-reaching effects on the intertwined water, food and energy (WFE) nexus, but the complementary operation is fundamentally challenging especially under high uncertainties of hydro-meteorological conditions. This study proposed an artificial intelligence-based WFE system-overarching solution driven by hybrid hydro-floating photovoltaic power generation for promoting nexus synergies. A multi-objective optimization model grounded upon the Grasshopper Optimization Algorithm was developed to simultaneously maximize hydro-floating photovoltaic power output, the ratio of water storage to reservoir capacity, and the ratio of water supply to water demand. The Shihmen Reservoir watershed and its WFE system in northern Taiwan constituted the case study. The results demonstrated that the proposed optimization model could significantly improve synergistic benefits of the WFE nexus by reaching 13%, 13.3% and 15.1% in water storage, food production and hydro-floating photovoltaic power output, respectively. The optimal tilt angles of floating photovoltaic installation would vary between −11.9° (Summer) and 44.3° (Winter). This study opens up new perspectives on green energy production expansion while stimulating WFE nexus synergies in support of policy-makers with feasible schemes on floating photovoltaic deployment in the interest of social sustainability. In consequence, new niches are exploited for floating photovoltaic deployment and give rise to impact mitigation concerning hydro-meteorological uncertainties on WFE nexus management. [ABSTRACT FROM AUTHOR]

The ability of hydrological models to forecast runoff depends largely on the quality model forcing datasets. A number of global and regional distributed datasets hold great promise for hydrologic prediction by providing a more comprehensive spatial and temporal coverage. While these datasets are now more commonly used for hydrologic simulation, a significant degree of sensitivity to precipitation amount and timing is observed. In this study, Statkraft's Hydrologic Forecasting Toolbox (Shyft) is used to evaluate simulations forced with observations and three different distributed forcing datasets: I) ERA Interim, ii) Water and Global Change (WATCH) Forcing Data ERA-interim (WFDEI), and iii) COordinated Regional Climate Downscaling EXperiment with contributing institute Rosssy Centre, Swedish Meteorological and Hydrological Institute (CORDEX-SMHI)) over the Narayani catchment, Nepal. Not only does this provide an opportunity to evaluate variability and uncertainty resulting from the ensemble of forcing data, but it also demonstrates the capability of these data in data sparse regions. The fidelity of streamflow simulation is greatest when using observed and WFDEI forcing datasets. Results demonstrate the successful application of global gridded datasets in regional watershed-scale modeling. The distributed hydrologic simulations show good streamflow simulation performance based on statistical model evaluation techniques. Results are also promising for inter-annual variability. This study shows that WFDEI datasets hold great potential for understanding the hydrology of data-sparse Himalayan regions and indicate that CORDEX-SMHI and ERA-I derived precipitation products will require further validation, bias correction, and algorithm improvements, particularly over the high mountain regions. [ABSTRACT FROM AUTHOR]