Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI)

Date Published
07/2024
Publication Type
Report
Authors
Abstract

The Department of Energy's (DOE) Office of Electricity (OE) sponsored the "Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI) Workshop", which was hosted at Lawrence Berkeley National Laboratory on April 16, 2024. This hybrid event convened industry leaders, researchers, and innovators both in-person and virtually to discuss the transformative potential of AI in enhancing the development and adoption of grid-scale energy storage. Participants presented and discussed how advancements in machine learning (ML) and AI can catalyze innovation in material development, system integration and optimization, performance validation, and strategic policy development.

The wide-ranging workshop spanned topics from accelerated materials development to policy and valuation of long duration energy storage systems as well as the use of AI-powered agentic systems to manage grid operations. Throughout the event, participants highlighted the technical, social, and financial hurdles in building and maintaining robust data infrastructures that serve as the foundation for AI/ML innovation. Key recommendations were made across the entire range of topics, with clear needs for the development of scalable and trustable AI tools, enhancement of data availability and interoperability, the need for interpretability and transparency in policy decisions, and promotion of cross-sector collaboration to realize the full potential of AI in energy storage. Additionally, the workshop underscored the importance of fostering collaboration between academic/government researchers and industry to fully unlock the potential of AI in energy storage and grid operations.

This report summarizes these discussions, with the goal to guide and inform future advancements of AI for energy storage that align with national goals for energy efficiency and sustainability.

Year of Publication
2024
Refereed Designation
Unknown
Organizations
Research Areas
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