Dive Brief:
- Artificial intelligence can help manage the U.S. electric grid, including reducing emissions and lowering costs — but the nascent technology poses significant risks if deployed “naïvely,” the Department of Energy said in a pair of reports published Monday.
- Priority use cases for managing the U.S. electrical system include grid planning, permitting and siting, operations and reliability, and resilience, according to the “AI for Energy: Opportunities for a Modern Grid and Clean Energy Economy” report.
- Similarly, four broad categories of grid risk have emerged, DOE said in a report titled “Potential Benefits and Risks of Artificial Intelligence for Critical Energy Infrastructure.” Those risks include adversarial attacks against AI systems, unintentional failures of AI models, the use of AI to execute cyber or physical grid attacks, and supply chain compromises.
Dive Insight:
“Artificial intelligence holds both incredible promise and potential challenges for the U.S. energy sector,” Puesh Kumar, director of DOE’s Office of Cybersecurity, Energy Security, and Emergency Response, said in a statement.
The assessments published yesterday were directed by an executive order President Biden signed in October identifying the administration’s core principles regarding the development of AI. The order also directed DOE and the Department of Homeland Security to assess AI systems’ threats to critical infrastructure.
Potential risks associated with AI include “poisoning attacks,” where an attacker could “add, modify, or alter the data used to train an artificial intelligence model, in order to force the model to learn the wrong behavior. This can include modifying data on energy system operations,” according to the CESER report.
A poisoning attack could result in an AI model misunderstanding what normal energy systems operations look like, the report said. More sophisticated attacks could alter training data “so that a model meant to detect physical wear in oil and gas equipment never declares an equipment to need maintenance.”
AI could also allow less-sophisticated adversaries to carry out cyber or physical attacks on energy infrastructure, CESER warned.
The new technology could enable “autonomous control of devices for physical attacks,” the report said. “Adversaries could combine AI-enhanced capabilities with other technologies, such as unmanned drone systems, in order to execute remote physical attacks on energy infrastructure.”
There are also risks associated with the development of AI tools, the report found. Machine learning models created with “a mismatch between training data and real-world use” can skew outcomes in energy systems, CESER said. And the use of AI to make predictions about unexpected events, such as extreme weather, “can lead to unpredictable or inaccurate behavior.”
Ultimately, CESER concluded most of the risks “can be mitigated through best practices, putting appropriate protections around important data and models, and in some cases, funding further research on mitigation techniques.”
Beyond the risks, DOE officials say AI has the potential to help address climate change and improve grid resiliency.
“Artificial intelligence can help crack the code on our toughest challenges,” Secretary of Energy Jennifer Granholm said. The agency “is accelerating its AI work on multiple fronts to not only keep the U.S. globally competitive, but also to manage AI’s increasing energy demand.”
President Biden has set a goal for the U.S. electric system to be carbon-free by 2035. Achieving that, including managing large amounts of renewable and distributed generation, creates challenges that AI can help address, DOE concluded.
“AI offers significant opportunity to better maintain existing generation assets, forecast non-dispatchable generation and adjust flexible loads, and better inform operational safety,” the report said. Areas for use include matching load and supply; predictive, risk-based maintenance; and improvements to grid resilience.
“By predicting the impact of outages and optimizing the dispatch of repair crews, AI can help utilities restore power more efficiently,” the report said. It can also help with anomaly detection and equipment assessment.
“AI algorithms, particularly those based on machine learning, can infer the state of a system from limited measurements by leveraging historical data and probabilistic models, providing valuable insights where human analysis may be insufficient,” according to the report.
DOE also announced several AI-related initiatives alongside the release of the reports. The agency is: investing $13 million to build AI-powered tools to improve siting and permitting of clean energy infrastructure; establishing a new working group on powering AI and data center infrastructure; and over the next several months plans to convene utilities, clean energy developers, data center operators and regulators to discuss the impacts of load growth.
Experts say the power demands of artificial intelligence are growing rapidly.
Global power demand created by AI, excluding China, is estimated to reach 13.5 GW to 20 GW by 2028, Sreedhar Sistu, vice president of artificial intelligence offers for Schneider Electric, told a House subcommittee in October. Of that demand, 30-45% is estimated to be in the United States.
Administration officials say the power demands can be managed, while the technology transforms the grid.
DOE wants to utilize AI to “address pressing needs such as enhancing the cybersecurity of the power grid, engineering new materials for batteries, and building the next-generation of grid-scale storage solutions,” Helena Fu, DOE’s chief artificial intelligence officer and director of the Office of Critical and Emerging Technologies, said in a statement.
