In comparison to traditional fossil fuels, hydropower has the potential to significantly reduce greenhouse gas emissions and play a crucial role in promoting a low-carbon energy structure transformation. However, the reliability and stability of the hydropower system in the warming future remain unclear. Here, we evaluate the impact of future climate change on hydropower production, regional electricity demand, and energy system supply-demand balance in the Yangtze River Basin (YRB), which is China's largest hydropower production base. We have utilized two indexes, i.e., Energy Production Drought (EPD) and Energy Supply Drought (ESD), to characterize the changes in the hydropower energy system. EPD refers to a series of days with low hydropower production, while ESD refers to a series of days with mismatched production/demand. We utilize 15 global climate models from CMIP6 to force the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) land surface model with consideration of reservoir regulations, to estimate the generation capacity of 86 mainly hydropower plants in YRB. In addition, an empirical electricity demand model considering socio-economic and climate factors is adopted to evaluate the changes in electricity demand in the receiving areas of southern China. Under climate change, the projected hydropower generation in the YRB is expected to increase throughout the 21st century. However, the future electricity demand will also rise due to GDP growth. Climate change will alter the distribution of seasonal electricity demand, resulting in an increasing mismatch between electricity demand and hydropower supply. Therefore, hydropower EPD and ESD are also being investigated, and the study is crucial for understanding future changes in the electricity supply and demand balance, as well as mitigating the impact of global warming.

How to cite: Liu, X. and Yuan, X.: Future changes in hydropower energy system in the Yangtze River Basin under different warming levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-264, https://doi.org/10.5194/egusphere-egu24-264, 2024.

El Niño events pose a significant threat to water security in the Godavari river basin (GRB), leading to adverse impacts on water-energy nexus. To enhance long-term energy security, we developed an integrated framework combining the hydrological model, variable infiltration capacity (VIC) model, and geospatial tools to identify potential sites for run-of-river small hydropower (RoR-SHP) plants. The study utilized long-term (1951-2020) daily streamflow data, simulated with the VIC model for design discharge computation at 30%, 75%, and 90% flow dependability. The analysis revealed considerable potential for RoR-SHP development within the GRB, identifying 226 initial sites based on the head along the river, with a combined power and annual energy generation estimate of 92 MW and 0.4 TWh/yr, respectively, at 90% flow dependability. After meticulous screening, 11 potential sites based on the head and the power potential were identified. The detailed analysis during El Niño years demonstrated a decline of approximately 46%, 38%, and 18% in total annual energy at 30%, 75%, and 90% flow dependability, respectively, compared to normal years. Consequently, we proposed nine potential sites based on the head, power potential, and viability under El Niño for RoR-SHP development, capable of maintaining the firm power even during El Niño years. Our findings highlighted the increased risk of power shortages in the GRB during El Niño years, emphasizing the imperative need for implementing water-energy nexus strategies to cope with the risks associated with El Niño events.

How to cite: Kasiviswanathan, K. S. and Thakur, C.: An Integrated Hydrological Modeling Framework for Enhancing Water-Energy Nexus during El Niño Events in the Godavari River Basin, India , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1567, https://doi.org/10.5194/egusphere-egu24-1567, 2024.

Reservoirs are an important source of emissions of carbon-based greenhouse gases (GHGs). China has about tens of thousands of reservoirs, about half of the world's total reservoirs, and therefore needs a more accurate estimate of current carbon emissions from reservoirs. This study utilizes the Greenhouse Gas from Reservoirs (G-res) model to assess CO₂ and CH₄ fluxes for 1479 reservoirs in China. The findings reveal that Chinese reservoirs contribute 0.156 Tg CO₂ eq yr⁻¹ in CO₂ emissions and 6.657 Tg CO₂ eq yr⁻¹ in CH₄ emissions. Across the nine main river basins in China, negative CO₂ diffusive emissions from reservoirs are observed where large size reservoirs were attributed specifically the Northern inland and Xinjiang basin, southwest international basin, Yangtze basin, and Pearl basin. Similarly, CH₄ fluxes through degassing and ebullition diffusion pathways exhibit a decreasing trend from small to large in the categorisation according to storage capacity. The findings in this study investigated significant GHG emissions from reservoirs in China, but also highlighted the different circumstances under which certain large reservoirs have the potential to act as CO₂ carbon sinks. In order to reduce greenhouse gas emissions, it is crucial to strategically review hydropower planning, in which the cumulative effects of small reservoirs and the large impacts of large reservoirs should be considered.

How to cite: Wang, Z., Feng, M., Chan, F., and Johnson, M.: Assessing Carbon Emissions from Reservoirs in China: Insights from the G-res Model and Implications for Hydropower Planning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2281, https://doi.org/10.5194/egusphere-egu24-2281, 2024.

Alpine hydropower reservoirs play a crucial role in the energy system as a source of renewable energy and energy storage, as well as in water management mitigating the impacts of extreme events and augmenting freshwater availability. The effective operation of hydropower reservoirs requires knowledge of the expected inflows, and the inflows prediction methods usually require the historical series of observed inflows. The reservoir inflow is often estimated because it is hardly measurable due to its spatial distribution along the reservoir sides. However, traditional methods such as the Simple Water Balance for estimating inflows can yield fluctuating and potentially negative results due to errors in water level measurement and stage-storage relationships. This study focuses on the estimation of inflow to ten reservoirs belonging to three different hydropower cascade systems situated in the Italian Alps. Two new methodologies to estimate reservoir inflow are proposed. The first (Optimized Inflow Estimation from Water Balance, OIEWB) consists of an optimization-based method and extends a known literature optimization technique to cascade reservoirs. In particular, the OIEWB method estimates the inflows to cascade reservoirs solving a bi-objective optimization problem aiming to minimize both the differences between consecutive inflow and the differences between observed and estimated water levels. It also includes an automatic calibration of the weight of the objectives according to the physical characteristics of each reservoir, avoiding any a priori calibration. The second (Filtered Inflow Estimation from Water Balance, FIEWB) consists of a low-pass filter shaped as a piecewise linear function whose slope is defined, again, by the physical characteristics of each reservoir. The low-pass filter is applied to the SWB cascade reservoir inflow to remove the high-frequency fluctuations that can be generated by measurement and estimation errors. The proposed procedures have been compared with the traditionally used ones in terms of Inflow Variability (the difference between inflow at two consecutive time steps) and Storage Error (the difference between the estimated reservoir storage and the observed one). Results show that both the OIEWB and FIEWB methods generate smoother inflows compared to the SWB, reducing the average Inflow Variability standard deviation, of 86.6% and 79.3%, respectively. However, the FIEWB does not guarantee the positivity of the inflows and can lead to large Storage Errors. The OIEWB method has been found to be more flexible and automatically adaptable to reservoirs with a wide range of physical characteristics. Nevertheless, a relationship between the OIEWB and the FIEWB has emerged. This relationship can be used to design new low-pass filters that can emulate the behavior of the OIEWB, combining the flexibility of the latter with the simplicity of the FIEWB. By contributing to provide more accurate and reliable inflow predictions, the proposed methodologies reveal their utility in optimizing cascade reservoir operation, thereby facilitating better decision-making.

How to cite: Crippa, N., Marzaroli, P., and Tarabini, M.: Automatic estimation of reservoir inflows of Alpine hydropower cascade systems using level and outflow data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3867, https://doi.org/10.5194/egusphere-egu24-3867, 2024.

The role of hydropower as a renewable and balancing power source is expected to significantly increase in a scenario of Net Zero Emissions by 2050. As a common phenomenon in hydropower plants, hydropeaking will become more prominent, resulting in additional stresses on the ecological status of rivers. Here we propose a novel engineering approach to operate auxiliary reservoirs, termed re-regulation reservoirs to address the challenges posed by hydropeaking on river flow regimes. A re-regulation reservoir aims at smoothing flow fluctuations caused by hydropeaking by diverting and retaining parts of high flows and returning them back to river corridors during low flows. The regulatory performance of re-regulation reservoirs is a function of its geometry and volume availability, and It is defined and optimized by restricting the thresholds of various flow components.

In this study we developed a methodology and an open-access algorithm to operate re-regulation reservoirs using data from Kemijoki River, one of the most regulated rivers in Finland. The theoretical foundation of the algorithm was based on two main objectives, with the first aiming to reduce the hourly peak flow and increase the minimum hourly flow induced by hydropeaking. While the second objective aims to reduce the up- and down- ramping rates to increase the timespan of water level changes in the river’s corridor. Thus, the algorithm establishes a hierarchy of conditions to restrict peak flow, minimum flow, up-ramping rates, and down-ramping rates. However, as the ideal flow conditions for various ecosystem services may be different, a range of thresholds was utilized in each of the algorithm’s conditions resulting in thirty-five possible hydropeaking mitigation scenarios.

In all of the thirty-five tested scenarios, the re-regulation reservoir limits peak and minimum hourly flows and ramp rates according to thresholds defined by the algorithm. The results demonstrated that in most cases the required volume of the re-regulation reservoir increased as the thresholds for flow components became more stringent. However, for some scenarios this trend was not observed, indicating that matching the peak and minimum hourly flow with the ramping rates thresholds is required to achieve optimal re-regulation reservoir design. Nonetheless, our calculations show clear theoretical possibilities for regulating hydropeaking with re-regulation reservoirs.

Compared to other mitigation measures, such as the installation of downstream flow control devices or modifying the operation of hydropower facilities, re-regulation reservoirs offer greater flexibility and adaptability to changing environmental conditions, power, and water demand without increasing the operational cost of power systems.

How to cite: Mchayk, A., Torabi Haghighi, A., Marttila, H., and Klöve, B.: Hydropeaking Mitigation with Re-Regulation Reservoirs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6751, https://doi.org/10.5194/egusphere-egu24-6751, 2024.

Recent developments in eastern Europe, increasing climate disasters and the continuous threat of volcanic eruptions have revealed the vulnerability of present energy and water supply systems. To enhance the resistance of existing water and energy infrastructure, holistic monitoring relies on decentralized energy production. Energy harvesters (EH) utilize kinetic energy in existing water pipelines to produce an electric current to power sensors and other components related to water infrastructure monitoring, replacing vulnerable and cost intensive diesel generators.  EHs could represent an ideal solution to provide reliable and continuous power to decentralized monitoring systems. To characterize and assess the environmental impacts of EH, a complete life cycle assessment (LCA) was conducted using GaBi software and the ecoinvent database, as well as data collected from various case studies. This LCA focuses on EH in existing water facilities and networks and includes manufacturing, transport, usage, and decommission stages for the EH. In modeling this technology from cradle-to-grave, a more complete understanding of its environmental impacts can be obtained. Special focus in this LCA was given to the allocation of impacts to services provided by water networks, e.g., drinking water supply, heat supply and water purification. Preliminary results suggest that a scale up of these harvesters could bring their global warming potential – measured in g CO2, eq/kWh – down to a level that is competitive with conventional hydropower while having significantly less impacts on surrounding natural areas. Our results focus on the case study in Iceland, the district heating system in Reykjavik. The preliminary results suggest that most impacts stem from the production of the material needed for the harvester, with little coming from the operation phase. As discussed above, EHs could provide a solution to decentralized monitoring systems. One application being explored for these harvesters is to power sensors along the existing water facility network, thus adding not only to the reliability of power supply, but to the overall reliability of the water network and provided a cleaner source of power than traditional diesel generation. If considered as part of an allocation LCA, these emissions savings constitute an additional reduction in the harvesters’ impacts. Essentially, the results of this LCA suggest that EH in existing water systems represents a crucial element in the low-carbon energy transition. EH could increase resiliency and energy security, while tapping into already existing water supply networks, ideally without adverse effects on these systems. While our results focus on a case study in Iceland, we plan to apply the approach to drinking water supply systems in Ferlach, Austria and Izmir Turkey, water purification in Padova, Italy and natural currents in the lagoon of Venice, Italy. The ensemble of the results from all case studies could reveal the full potential of EH across Europe.

How to cite: Bronkema, B., Finger, D. C., Gudlaugsson, B., and Gezer, D.: A Life cycle assessment of energy harvesters in existing European water networks for distributed network monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8003, https://doi.org/10.5194/egusphere-egu24-8003, 2024.

Hydropower is considered as an important source of renewable energy due to its flexibility and storage capabilities. However, hydropower faces significant challenges with climate change and especially the increasing risks of extreme weather events such as droughts.

In this study, we analysed the impact of historical droughts on hydropower at a global scale by developing a hybrid model that combines a physically based hydropower model with a machine learning model. This integrated approach enables us to capture important features affecting hydropower generation beyond water availability, considering the details of local specific conditions at hydropower plant sites while it can be applied across the globe. A new open-source global dataset is developed that contains key information of the hydropower plant characteristics and their reservoir attributes by merging various plant sources with a global reservoir database. The hybrid model is trained against observed monthly hydropower generation data at the power plant level. By employing this approach, we aim not only to enhance the realism of simulating hydropower output compared to the simplistic physically based equation but also to leverage the flexibility of machine learning. Additionally, this method enables us to circumvent detailed power system modelling which requires significant computing power and extensive data.

We found that the performance of our hybrid hydropower model surpasses the simple physics-based hydropower equation at most hydropower plant sites. Key findings highlight the significant losses of hydropower generation during major historical drought events across the globe.

How to cite: Shah, J., Hu, J., Edelenbosch, O., and van Vliet, M.: Global loss in global hydropower supply under droughts using a hybrid model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8551, https://doi.org/10.5194/egusphere-egu24-8551, 2024.

Hydropower stands out as an economical, reliable, sustainable, and renewable source of energy. It has been the leading source of renewable energy across the world, generating more than 15 % of total electricity in 2022. Therefore, it will likely play a crucial role as the energy system shifts towards a carbon-free future. The turbine system is at the heart of the hydropower plant and converts flowing water into mechanical energy. Remarkably, around 154 gigawatts, or one-fifth of the installed hydropower turbines, will be more than 55 years old by 2030 globally. Modernising these aged turbines is essential for sustaining optimal plant performance and this will create opportunities to retrofit hydropower facilities to improve their adaptability to changing hydrological conditions. A well-defined methodology is necessary to evaluate feasibility and select optimal solutions for upgrades. 

This study addresses this critical necessity in the context of run-of-river (RoR) hydropower plants with the HYPER_OP toolbox to efficiently evaluate and choose optimal turbine replacement or upgrade options. HYPER_OP provides operational optimization capabilities coupled with design flexibility and expanded simulation features for complex turbine configurations. It facilitates the selection of turbine systems featuring large and small turbines. The effectiveness of this toolbox is illustrated through the  case study of the Bonnington RoR hydropower plant, commissioned in 1927 on the upper reaches of the River Clyde in Scotland, United Kingdom. Bonnington RoR features a pair of two identical Francis turbines, each designed for a discharge of 12 m³/s and equipped with an installed capacity of 5.5 MW.

Our analysis indicates that, by prioritising Net Present Value (NPV) maximisation through a single objective function and considering historical discharge records, HYPER_OP offers a novel configuration featuring non-identical Francis turbines with design discharges of 16.13 and 9.13 m³/s.  Optimal design increases power production by approximately 3.4 GWh (~7 %) annually by providing operational flexibility and retaining high efficiency over a range of discharge values. The optimal design yields an NPV of approximately 3 million dollars (USD), factoring in the additional energy increase as revenue, turbine replacement cost, and lifetime operation cost. The payback period for this investment is projected to be 15 years when considering only the additional energy as revenue. It's worth highlighting that the optimised design notably outperforms the current configuration, particularly in response to variable streamflows, including both high and low flows. Therefore, optimal design is expected to be less vulnerable to climate change due to higher efficient configuration. 

How to cite: Brown, S., Yildiz, V., and Rougé, C.: Modernising RoR Hydropower: A Study on Retrofitting Aged Turbines for Optimal Performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9612, https://doi.org/10.5194/egusphere-egu24-9612, 2024.

In Iceland, hydropower represents around 72% of the gross electricity generation annually, with energy production capabilities around 13.8 TWh/a. Most of the hydropower infrastructure is in the central highlands, relying on water resources temporarily stored as snow and ice. These resources are vulnerable to climate change, projected to undergo substantial changes in the coming decades. Changes in flow volumes, seasonality of flow and extremes will have a strong impact on the hydropower system in Iceland as over 50% of inflow energy to the system originates from glacier ablation during summer. The high natural climate variability and energy system isolation pose a risk to the energy security of the power system as droughts and cold periods are usually not foreseen with great advance. Changes in hydrological flow dynamics, e.g.: onset of snow- and glacier melt, melt magnitudes and precipitation patterns pose a series of challenges for the hydropower system.

In glacier-dominated catchments, climate warming will initially increase glacier meltwater runoff to a maximum and then runoff will reside as the glacier area and volume decrease over time. The timing of the discharge peak is influenced by the catchment topoclimate characteristics and location. Understanding and quantifying these changes is important both for operational control and planning of energy infrastructure on shorter timescales (days, months, years) and climate change adaptation, on longer time scales, for both current energy projects as well as future development to maximize efficient water resource utilization.

To assess the impacts of changes in inflow dynamics on the hydropower system, hydrological models were developed to create inflow scenarios. Historical inflows were first reconstructed, followed by a construction of future runoff scenarios using climate projections and different glacier geometry evolution. This allowed for the assessment of meltwater-induced changes in runoff, although generally increasing in the next decades, certain areas are closer to reaching maximum meltwater production and will decrease in the coming decades. In all cases meltwater-induced increase in runoff is temporary, while large uncertainties exist with the timing of maximum peak inflow.

Utlization of the inflow scenarios created include current day operation to optimize reservoir management strategies and the design of future power projects, including refurbishments and capacity increases. This accommodates the expected increased flow rates 10–50 years into the future.

How to cite: Gunnarsson, A., Helgason, H. B., Sveinsson, Ó. G. B., and Tómasson, G. G.: Climate change, water resources and the hydropower system in Iceland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9926, https://doi.org/10.5194/egusphere-egu24-9926, 2024.

Linking integrated water-energy simulation with multi-objective search algorithms provides a practical design tool for interdependent river basins and power systems. However, this approach is typically limited by the computational resources required to complete the many thousands of simulations to discover efficient solutions. We introduce an artificial neural network-based power system emulator to enable optimized design of large-scale detailed multi-sector water-energy systems. The proposed framework links an integrated power system emulator and river system simulator to an AI-driven multi-objective search design process. We compare optimized designs using both the power system emulator and simulator to check the emulators’ computational speed and accuracy. The framework is applied to the Sudanese power system and its link to the Eastern Nile river basin, to investigate how optimized operational strategies of the Grand Ethiopian Renaissance Dam (GERD) could affect Sudan’s resource systems. Results are similar for the power system emulator and simulator, showing the emulator helps to significantly reduce the computational cost of using sophisticated multi-sector policy design approaches.

How to cite: Ashraf, A., Etichia, M., Basheer, M., and Harou, J.: Machine learning power system emulation for rapid screening of multi-sector policies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20054, https://doi.org/10.5194/egusphere-egu24-20054, 2024.