Water Power Technologies Office Announces More Than $1.7 Million for Hydropower and Marine Energy Projects at U.S. National Laboratories

INL will work with Washington State University, the U.S. Bureau of Reclamation, and American Whitewater to leverage existing meteorological, snow, and stream datasets throughout the western United States to provide more accurate streamflow forecasting. This forecasting will inform WPTO’s tools related to streamflow estimates and power generation, such as those that help developers predict regional water availability and streamflow timing. Streamflows have changed over time due to shifts in snowmelt as a result of climate change. More accurate streamflow forecasting can help hydropower operators better manage water supply and protect downstream water users from these shifts.  

INL will identify the costs and benefits of hydropower modernization projects and perform a supply chain assessment to help identify timelines for hydropower plant modernization throughout Grant County, Washington. This work will inform the development of a strategic planning tool that owners and operators can use to implement modernization projects at their facilities across the nation.  

INL aims to gauge the potential scope and impact of adding governor systems—which allow hydropower units to maintain power output and generator speed autonomously—to plants that do not have them. Researchers will also evaluate generator speeds and power systems in plants with governors. Upgrading plants with governors that are sensitive to generator speed will enhance power system flexibility and enable the plant to perform black starts, restoring electricity after a disruption without support from an outside power source. 

ORNL will create a modern methodology to ensure hydropower facilities have the capacity to manage flood risks during extreme storm events driven by global warming. This work will give hydropower operators more robust and forward-thinking planning options to help prepare for future extreme weather events.  

ORNL will determine if low-cost sonar systems and open-source software can be used to develop predictive models of fish habitats in river systems with hydropower facilities. The resulting information can inform regulatory decision-making processes and help hydropower operators mitigate the potential environmental impacts of hydropower facilities.  

PNNL will assess public perceptions of water power stories written by both human and artificial intelligence sources to understand the limitations of generative artificial intelligence as a science communication tool. Research from this project will help water power experts better understand the practical and socio-ethical implications of using artificial intelligence to communicate and engage with the public.  

PNNL will work with Seattle City Light to assess the impact of atmospheric river events—a narrow region of concentrated moisture from the tropics—on Pacific Northwest watersheds where hydropower reservoirs are located. This research will help hydropower owners and operators better understand, prepare for, and manage the impacts of extreme weather events like atmospheric rivers. 

PNNL will partner with Voith Hydro and the Bonneville Power Administration to determine if an emerging technology known as additive friction surfacing (AFS) can help increase the resilience and efficiency of hydropower components without significantly increasing costs. The AFS process is a technique for coating materials, including steel and other metals, to improve the performance or lifespan of an underlying metallic surface. This research could result in greater power generation efficiency and reduce impacts of supply chain issues associated with sourcing materials.  

PNNL will use artificial intelligence-based image analysis to standardize and improve methods of assessing any injuries before and after fish pass through hydropower turbines. This research will reduce stress to fish during assessments and advance environmentally sustainable hydropower practices. 

PNNL will design, build, and present a mobile platform to teach students, professionals, and stakeholders how to use a control system in a hydroelectric dam. This project will offer hands-on learning opportunities related to cybersecurity, industrial networking, and dams, and help develop a new generation of hydropower experts. 

PNNL will investigate the use of self-healing cements for the repair of concrete typically used in hydropower dams. These cements could help reduce maintenance time and associated plant down time at hydropower facilities compared to current concrete repair methods. 

INL will continue work at Shoshone-Bannock High School on the Fort Hall Reservation where researchers engaged with students on hydropower development and its cultural implications on the environment. This project will support cultural understanding and the development of the clean energy workforce.

Reservoir sedimentation can impact the storage of water at hydropower facilities, potentially impacting performance. To help reduce these impacts, ORNL will continue previous work reviewing existing sedimentation datasets for gaps by collecting additional sources to merge with hydropower infrastructure data into a public comprehensive dataset. The new dataset will estimate current storage loss due to sediment at hydropower facilities across the United States, providing valuable information for dam and reservoir managers.

ORNL will leverage a machine learning model to develop a practical, open-source streamflow forecasting tool. The project will help hydropower owners and operators gain a greater understanding of streamflow predictability and support climate-resilient hydropower operations.  

PNNL will build upon its successful STEM ambassadors program to inspire the future water power workforce through the creation of a series of short, educational videos highlighting careers in water power. This work will target multiple locations through Washington to increase outreach and engagement and support the development of a diverse hydropower workforce.  

PNNL will continue its effort to demonstrate how forest management—or thinning—can reduce risk from wildfires, increase annual streamflow volume, and improve summer low streamflow within the Columbia River Basin. This project can help hydropower owners and operators better understand if forest thinning can help mitigate negative changes in seasonal river flow patterns under changing climate conditions.  
 

Sandia will evaluate whether embedding fiber optic sensors in mooring systems—or the systems that keep marine energy devices in place—is a practical approach to monitor structural health and predict maintenance needs of marine energy devices. 

Sandia will develop low-cost, low-maintenance, and non-toxic marine energy harvesting materials for directly converting wave movement and water flow to electricity. New materials could significantly improve how efficiently energy can be harvested. 

NREL will develop robotics training modules for use in a simulated ocean energy environment created with a web-based, open-source physics simulator. This project will help community colleges, trade schools, and marine energy developers train job seekers on how to use remotely operated vehicles for the deployment, operations, maintenance, and de-commissioning of wave and tidal energy devices. The project will also support research and development into autonomous underwater vehicles for marine energy.  
 

PNNL will collaborate with the Atlantic Marine Energy Center on a survey to gather public perceptions of marine energy technologies and their environmental and social impacts. This input will inform a map that may help identify where there is public support for marine energy. 

The growing electric bike industry uses mechanical components that mainly convert electrical energy into mechanical energy, but those components are also capable of generating electric energy from a mechanical input such as tides. In this project, PNNL will determine the cost savings and efficiency gain of using the e-bike industry supply chain as an alternative source for hardware for small-scale tidal turbines. This project could help solve issues related to cost and availability when developing or deploying marine energy devices.  

Conveying to the public what emerging marine energy technologies might look like is challenging. PNNL will poll experts to better understand what marine energy-related images would be most useful and then create a series of publicly accessible illustrations and graphics. This project will offer more visual explanations of marine energy research and development projects.  

Sandia will release an open-source solver code that enables modern and complex workflows, including machine learning, in the design and optimization of wave energy converters. The solver code will also be portable, meaning that high-performance computing will be more accessible to everyday users through cloud computing. This project could advance new methods in marine energy design. 

NREL will build upon previous work to design, model, build, and test an integrated combined energy electronics system, which is a hybrid energy system that allows for multiple renewable generation sources to be used. The technology was designed specifically to address the power needs of emergency responders and could help replace fossil fuel generators for disaster-relief scenarios and other applications.  

NREL previously designed a marine energy conversion (MEC) technology inspired by kelp that can generate electricity from bending, twisting, stretching, compressing, and swaying motions caused by currents and waves. This project will continue to design and develop several elements of the technology to create a fully functional, market-ready device that could be used to power ocean sensors.  

NREL will build upon previous research into triboelectric nanogenerators (TENGs), which can harvest energy from low frequency waves coming from all directions. This project will significantly increase the power output of their first-generation TENG prototype by stacking printed circuit boards to elongate the device. 

ORNL will optimize their previously developed wave energy converter model by reducing the number of power conversion stages. This project aims to improve the efficiency and reduce the cost of future grid-connected wave energy systems by streamlining the power conversion process.  

PNNL will partner with Oscilla Power to better understand the longevity of seals used in wave energy power converter systems. The team will perform laboratory testing on seal materials, using the results to update a previously created database and to estimate the lifespan of seals under realistic wave energy converter scenarios. By estimating the lifespan of these seals, researchers could help reduce operation and maintenance costs for wave energy converters.