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DOE Announces $34 Million For 12 Projects Across 11 States To Strengthen America’s Aging Power Grid

Jan. 18, 2024
The selected projects will develop cost-effective, high-speed, and safe undergrounding technologies to help upgrade the nation’s grid infrastructure.

The U.S. Department of Energy (DOE) has announced $34 million for 12 projects across 11 states to strengthen and modernize America’s aging power grid by developing cost-effective, high-speed, and safe undergrounding technologies, under the Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security (GOPHURRS) program.

The selected projects, managed by DOE’s Advanced Research Projects Agency-Energy (ARPA-E), will work on innovative solutions to help upgrade and expand the nation’s grid infrastructure by lowering costs, reducing inefficiencies, mitigating disruptions from extreme weather events and adopting renewable clean energy resources. 

The electric power distribution system in the U.S. includes 5.5 million line-miles with over 180 million power poles, which are not only prone to damage by weather and its effects but also account for a majority of power outages in the country each year. 

The selected projects are as follows:

Arizona State University (Tempe, AZ) has been awarded $4,263,082 to develop a water-jet underground construction tool to deploy medium-voltage electrical cables and conduits simultaneously underground with a lower risk to existing utilities by eliminating the need for a hard drill bit, reducing cost and lowering schedule impacts from reaming and duct pulling tasks.

GE Vernova Advanced Research (Niskayuna, NY) has been awarded $3,674,998 to develop a robotic worm tunneling construction tool to dig and install conduit and cables for underground distribution powerlines in a single step. GE’s system will mimic the natural movement of earthworms and tree roots to install 1,000 feet of cable and conduit in two hours with unmatched flexibility. The tool is expected to deploy from a standard pickup truck and eliminate the cost, complexity, and surface disruption compared with conventional approaches.

Melni Technologies (Twin Falls, ID) has been awarded $2,000,000 to redesign and develop medium-voltage power cable splice kits requiring fewer steps and streamline connections to reduce human errors and boost the reliability of underground electrical power distribution systems. Melni’s proposed kits are expected to be installed in 10-15 minutes, which is 3-4 times faster as compared to conventional kits, and reduce failures and mistakes up to 90% by eliminating installation steps and potential human errors.

Oceanit (Honolulu, HI) has been awarded $3,276,836 to develop a look-ahead subsurface sensor system, which is capable of sensing what lies beyond a drill bit, taking advantage of unmanned aerial vehicles and electromagnetic resistivity techniques to avoid damaging existing utilities when undergrounding powerlines. The system would use machine learning interpretation and high-resolution imaging capabilities to provide real-time guidance for the drill path.

Pacific Northwest National Laboratory (Richland, WA) has been awarded $3,750,000 to develop an artificial intelligence system for processing geophysical survey data into digital twin, a model of a real-world physical product, and augmented reality in order to identify existing utilities and other subsurface obstacles before installing underground power distribution lines. The proposed system is expected to produce results quickly, providing near real-time subsurface mapping and utility identification, which will lead to cost savings and speeding up of burying power lines.

Prysmian Cables and Systems USA (Highland Heights, KY) has been awarded $4,500,000 to develop a hands-free power cable splicing machine operating in underground vaults to reduce the share of splicing-caused medium-voltage network failures from 60-80% to less than 5% and improve the workforce safety by reducing the time the underground cable splicing crews spend in underground vaults. 

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