Securing Solar for the Grid (S2G)
LBNL will lead a new workgroup for Artificial Intelligence (AI) for IBR/DER cybersecurity that will seek a draft standard report on AI performance requirements for high solar / IBR / DER penetration scenarios. This working group will specifically focus on advancing an understanding of performance requirements for IBR/DER for high solar generation scenarios. As IBR / DER grow from tens of thousands to millions of devices, increased automation is likely required but needs to be considered within conflicting recommendations for manual control. Automation has brought significant advantages in the power grid for ensuring stability, increasing efficiency, and even providing cybersecurity benefits. At the same time, automation significantly increases cybersecurity risks because automated systems can be remotely attackable, and have similar vulnerabilities to other types of computing systems.
LBNL will also lead a new workgroup for privacy-preserving & cybersecure data and model sharing standards for solar / IBR / DER industry stakeholders. Use cases will focus on data and models for high solar / IBR / DER penetration scenarios. This will include threat-sharing program reformations, leveraging prior experience with CESER’s CEDS program.
This project was supported by the U.S. Department of Energy’s Solar Energy Technologies Office (SETO).
Sean Peisert (PI; LBNL)
Daniel Arnold (Co-PI; LBNL)
Publications resulting from this project:
More information is available on other Berkeley Lab R&D projects focusing on cybersecurity in general, as well as specifically on cybersecurity for energy delivery systems.