Researchers at the Department of Energy’s Oak Ridge National Laboratory and partner institutions have launched a project to develop an innovative suite of tools that will employ machine learning algorithms for more effective cybersecurity analysis of the U.S. power grid.
Distributed energy resource systems, which can range from solar panels to electric vehicles to demand response programs, are reshaping traditional grid operations.
This evolution introduces new challenges for cybersecurity. The reliance on information and communication technologies to facilitate connectivity exposes utility systems to cyber threats, which can be effectively confronted using tools powered by artificial intelligence.
The new tool suite, known as AI-PhyX, streamlines collection and analysis of data to comprehensively tackle all facets of cyber resilience, including vulnerability analysis, attack detection, threat mitigation and system recovery. AI helps convert data into actionable information that enables system operators to make better decisions.
“As we witness the proliferation of distributed energy resources across the grid, our focus lies in fortifying the cyber resilience of these systems,” said Jamie Lian, who leads ORNL’s Grid-Interactive Controls group. “We aim to harness the power of AI to bolster cybersecurity measures and ensure secure and reliable operations.”
Partners across national laboratories, academia and industry have joined forces to develop this tool suite. The research and development will be conducted in collaboration with DOE’s National Renewable Energy Laboratory, the University of Connecticut, Pennsylvania State University and Siemens Corp. Once the tool suite is developed, it will be demonstrated using system data provided by utility partners, such as EPB of Chattanooga, aiming for improved utility acceptance.
Researchers are developing a workflow that integrates diverse cybersecurity applications into one platform to streamline the training and operation of the system. The resulting tool suite helps solve challenges in data management that often lead to fragmented analysis and hamper deployment.
“This approach employs a common platform that uses a common data pool with common information sharing,” Lian said. “This increases functionality while reducing the deployment barriers for machine learning technologies, so utilities benefit from investing in AI.”
Distributed energy systems hold immense promise and could deliver benefits such as improved grid reliability, reduced electricity costs and decreased greenhouse gas emissions.
“Our work is to ensure these benefits can be fully realized,” Lian said. “By integrating cutting-edge AI methods together with the development of an easy-to-deploy tool suite, we can guarantee the secure and reliable operations of distributed energy systems, paving the way to a sustainable energy future.”
Development of the new tool suite is funded by the DOE Office of Cybersecurity, Energy Security and Emergency Response. In addition to Lian, Teja Kuruganti is co-leader of the project, and ORNL researcher Yan Liu is developing the software.
UT-Battelle manages ORNL for DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit https://energy.gov/science. — Reece Brown
