November 11, 2024
News Release

Hydropower Digital Twins Solution Helps with Operator Challenges

Dam operators can create a virtual version of their dam’s turbines to identify potential maintenance problems before they arise

News & Media Contact: JoAnna Wendel, PNNL
Alder Dam with a data graphic overlay

Composite image by Kelly Machart | Pacific Northwest National Laboratory

RICHLAND, Wash.— No two hydropower facilities are the same. They all work similarly by harnessing the power of rushing water to spin turbines and generators, which in turn creates electricity. But their differences—in size, age or mechanical parts—means there’s no one answer that can solve every problem that arises.

“Hydropower facilities are like snowflakes; even individual turbines within a plant are unique due to their individualized construction and varying upgrades over the years,” said Nathan Fletcher, senior hydropower engineer at the Department of Energy’s Pacific Northwest National Laboratory. 

Managing all that upkeep for a hydropower facility is cumbersome and complicated, Fletcher continued. With so many components to oversee, he’s heard dam operators say they wished for a way to track potential problems within a dam’s machinery long before they can hamper operations.

Enter hydropower digital twins: a virtual representation of real-life turbines. 

After hearing dam operators’ repeated frustrations, experts from multiple domains collaborated and developed a platform called Digital Twins for Hydropower Systems in 2023 to reduce outages and extend the lifespan of a dam. With new updates released in September 2024, dam operators can now use the dashboard to adjust factors that can potentially wear down a turbine’s efficiency, like unexpected electricity demand or extreme water level changes.

Dam operators can customize their digital twin to reflect the uniqueness of each facility; they just need to upload their facility’s data and the Digital Twins for Hydropower Systems dashboard handles the analysis.

“Each dam requires a unique maintenance strategy to improve efficiency, and the new digital twins platform can provide those solutions,” said Chitra Sivaraman, PNNL principal investigator of the project. “The platform is both extensible and scalable—capable of adapting to new facilities, data and models.”

Digital twin models also tackle another challenge. “The average age of the nation’s dams is around 60 years, meaning multiple generations of employees have worked on each turbine. And knowledge is inevitably lost as seasoned employees retire and new employees join the team,” said Scott Warnick, electrical and automation systems engineer at PNNL and the technical lead on the digital twins project. Hydropower digital twins can record and simulate all changes made to the dam over subsequent years—passing down knowledge and helping future generations make decisions.

Modernizing hydropower

“The digital twins solution enables hydropower operators to simulate different scenarios, such as low water flow or varying water levels, and predict future performance or maintenance needs,” Warnick said. 

To build a digital twin that accurately represents real life, the team used real-time data from a hydropower generation unit at Alder Dam on the Nisqually River in western Washington state, operated by Tacoma Public Utilities. 

Leading the modeling development is Hong Wang, principal investigator of the project at DOE’s Oak Ridge National Laboratory. He and the team collected data, such as the river’s pressure as it enters the hydropower facility, how fast the turbines spin and how much power the dam generates over time. 

In the original version of the digital twin, dam operators could only observe how normal or expected conditions affected the dam’s mechanical parts. In version 2.0, operators have more control. They can adjust water levels, flow rates and turbine speed that might change based on weather, droughts or energy demand. With the ability to simulate both normal conditions and higher or lower water flow, the dam operators can home in on potential problems before they arise.

“People in operations and maintenance can perform trials on a digital twin instead of risking expensive equipment, making sure decisions can be made with confidence,” Warnick said. 

With support from DOE's Water Power Technologies Office, researchers at PNNL and ORNL are executing a multi-year project to design, develop, prototype and demonstrate a digital twin for hydropower operators. (Video: Pacific Northwest National Laboratory)

Integration with renewable energy

The updated digital twins model addresses another emerging need. As the nation moves towards a sustainable grid and embraces more wind and solar energy, hydropower systems must be adaptable and responsive to support a stable grid. 

“This platform is capable of extending the lifespan of the nation’s dams while at the same time integrating additional sources of renewable energy to the grid,” Sivaraman said.

For instance, energy demand rises in the evenings as people come home from work and turn on the TV or run the dishwasher or laundry machine. At the same time, the sun is setting, so solar power generation starts to fall. What’s more, sometimes the wind doesn’t blow—this can be particularly challenging during extremely hot days, when power is needed for air conditioning. During times of little sun or wind, hydropower dam operators can fill the gap in generation by quickly starting turbines. 

But despite the benefit of an easily ramped-up renewable energy source, “too much use can age a dam’s components quicker,” Fletcher said. “The key is generating enough energy when needed without overburdening the turbines themselves.”

With a digital twin solution, operators can simulate and reviewreal-world power demand fluctuations. If the model shows that the conditions are optimal to run the turbines, operators can feel confident about proceeding, which maximizes revenue.

“The digital twins dashboard paves the way for the digitalization of hydropower systems—providing a critical tool for operators to simulate and optimize grid operation for increased penetration of renewables, such as solar and wind,” Wang said.

Expanding the reach

With continual collaboration with TPU, the team gains insight into ways to improve the model. While helping TPU operate more efficiently, updates to the dashboard mean it can represent a wider diversity of turbines. 

“The PNNL and ORNL teams have the mathematical and practical skills needed to solve complex digital twin problems,” said Greg Kenyon, automation engineering manager at TPU. 

The team is also working with Chelan County Public Utility in north-central Washington state to collect and analyze years of operation data records from the Rocky Reach Dam to develop a digital twin. Just like for Alder Dam, Chelan County’s hydropower digital twins will provide operators with the ability to review performance monitoring and analysis, perform predictive maintenance and optimize energy production—all at zero cost.

“The digital twin will help minimize the risk to perform on the real operation, such as load rejection, over-speed test and vibration at the unit start or stop stage,” said Wenbo Jia, Chelan County Public Utility’s senior mechanical engineer. 

The team anticipates future projects expanding on the application of digital twins.

“We are building the basics now, focusing on turbines and rotors. But we aim to address broader concerns, such as biological buildup like sludge in coolers, along with the challenge in making them more environmentally friendly which are now common worries for many utilities,” Fletcher said.

Kenyon’s vision for hydropower systems is a data-driven future where data analytics and predictive maintenance algorithms drive asset management. “It is one where there are no unplanned outages and lost revenue but rather outages determined by data-driven maintenance schedules and equipment replacements,” he said.  

To get started using the new hydropower digital twins dashboard, dam operators can sign up for an account and work with the PNNL and ORNL team to share their facility’s historical data.

The project is funded by DOE’s Water Power Technologies Office.

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