A proposed federal research program to accelerate research into the durability and performance of long-duration energy storage is a critical step to meeting the Biden administration’s decarbonization goals, speakers said Thursday at a Department of Energy (DOE) panel. DOE officials said long-duration energy storage technology must be commercially ready, at scale, by 2030, in order to increase the share of renewables on the grid and meet the administration’s 100% clean electricity by 2035 goal. To learn more, read, “DOE eyes AI, machine learning to accelerate long-duration energy storage research.”
- In July, DOE announced a moonshot goal to reduce the cost of utility-scale, long-duration storage by 90% within a decade, backed by federal research, large-scale demonstrations and domestic manufacturing incentives
- Deputy Energy Secretary David Turk said bringing long-duration storage to the grid wouldn’t just make it possible to rely on more renewable energy, but also “increase resilience and lower energy burdens” for vulnerable communities.
- Although there have been technical breakthroughs on long-duration technologies — notably Form Energy’s July announcement of a 100-hour iron-air battery — experts have cautioned about the limited window to test batteries in the real world.
- ROVI, the proposed initiative from DOE’s national labs, seeks to close that information gap by using machine learning and artificial intelligence to model performance of different long-duration storage technologies, including predicting how the technology will lose performance or hold up physically over time.
Path to 100% Perspective:
Artificial Intelligence (AI) and Machine Learning (ML) will be key elements for the design of future energy systems, supporting the growth of smart grids and improving the efficiency of power generation, along with the interaction among electricity customers and utilities. Centralized power systems enable equal access to clean power at the lowest cost, reducing economic inequality. Regardless of whether the path forward is more or less centralized, AI brings value to all parties. The more AI is used in the dispatch of power plants, the more it will be needed in the design and creation process for new power plants or aggregations of power generation equipment. AI and equipment expertise are needed to enhance the safety, reliability, and efficiency of power equipment and systems. AI and machine learning will play increasingly important roles in future power generation, especially as more communities and organizations come to rely on smart grids and renewable fuels for their electricity needs.
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