Technology

Industry Collaboration Powers New Generation of Grid Emergency Control Technology – HPCwire

Summary

The scalable High-Performance Adaptive Deep-Reinforcement-Learning-based Real-Time Emergency Control (HADREC) platform—being further developed and tested under a three-year investment from the Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E)—uses a type of artificial intelligence (AI) called deep reinforcement learning, alongside high-performance computing, to automate decision-making and system responses within seconds of a disturbance.

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The scalable High-Performance Adaptive Deep-Reinforcement-Learning-based Real-Time Emergency Control (HADREC) platform—being further developed and tested under a three-year investment from the Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E)—uses a type of artificial intelligence (AI) called deep reinforcement learning, alongside high-performance computing, to automate decision-making and system responses within seconds of a disturbance.

Deep reinforcement learning improves on conventional reinforcement learning in its ability to better scale and quickly and effectively apply existing patterns to a real event’s unpredicted problems across thousands of system assets. Initial results show the HADREC technology will help reduce system reaction time 60-fold and improve system recovery time by at least 10%. This helps prevent cascading disruptions, thus allowing more efficient and resilient grid operation.

A three-year plan toward real-world system demonstration

The project’s collaborators are realizing the benefits of combining diverse perspectives and expertise from all angles of the problem while working efficiently toward a solution. During year one, the team established performance methods and benchmarks for the HADREC algorithms and began testing them using a mock system the size of the Texas grid. Once satisfied with algorithm performance, they moved testing to a more realistic, larger-scale system.

Now, as they enter year three, the team will focus on demonstrating the technology using actual utility and grid data. By the end of the project in 2022, the technology will be developed and sufficiently tested for integration with a real production system.

“Sometimes grid operators have traditional ways to solve a particular problem, but it is difficult and time-consuming and they still may not arrive at a feasible and effective answer,” said PNNL electrical engineer and collaborator Qiuhua Huang. “An ARPA-E project like this one pulls parties together to guide development from multiple perspectives, and combine that with the benefit of strong research capabilities to solve real-world problems more efficiently and effectively.”

Tapping the experience of industry and a national laboratory

Working alongside PNNL on the project are utility vendor and technology developer V&R Energy, investor-owned utility PacifiCorp, and a Google Research team with expertise in machine and reinforcement learning. Each holds a unique piece of the puzzle.

Based in Portland, Oregon, PacifiCorp is one of the largest grid operators in the western United States. “The benefit for us in partnering with a national laboratory such as PNNL is the contribution of new ideas, techniques, and tools to solve increasingly complex challenges,” said Song Wang, PacifiCorp transmission planning engineer. “In turn, we are able to share information with our research partners about the specific challenges we face in the utility industry to help effectively focus the team’s efforts toward solution development.”

For V&R Energy, the goal is to help provide effective solutions to the problems PacifiCorp and other grid operators face. “We have power systems that are under various stress conditions, which include different behavior patterns due to interconnection of …….

Source: https://www.hpcwire.com/off-the-wire/industry-collaboration-powers-new-generation-of-grid-emergency-control-technology/