Machine Learning

Project MAGIC will develop artificial intelligence and machine learning algorithms to detect and mitigate cyber attacks on aggregations of Distributed Energy Resources (DER). The developed algorithms will be demonstrated in hardware-in-the-loop testing and integrated into an open source simulation tool. It is funded by DOE CESER’s RMT program and is led by Daniel Arnold.

This project aims to develop, apply, and test a technique for enabling collective defense of distribution grids with significant penetration of distributed energy resources (DER) and responsive loads, by leveraging a privacy-preserving method of data sharing without exposing raw data that might contain personally identifiable information (PII) or that might otherwise be considered national security information that could be leveraged by adversaries to more effectively compromise and potentially destabilize portions of the electric grid. It is funded by DOE CESER’s RMT program and is led by Sean Peisert.

Final Project Report Overview The SPADES project concluded work in July of 2023. The final report from the SPADES project is included below: Final Project Report

This project aims to produce methods, processes, and architectures applicable to a variety of scientific computing domains that enables querying, machine learning, and analysis of data while protecting against releasing sensitive information beyond pre-defined bounds. It is supported by LBNL CSR funds and is led by Sean Peisert.

This project is developing secure machine learning methods that will enable safer operation of automated, adaptive, learning-driven cyber-physical system processes. It is supported by LBNL LDRD funds and is led by Sean Peisert.

This project aims to develop techniques for enabling data analysis for the purposes of detecting and/or investigating cyberattacks against energy delivery systems while also preserving aspects of key confidentiality elements within the underlying raw data being analyzed. The result will be a complete solution for anonymization of data collected from OT and IT networks pertaining to energy grid cyberattack detection that has been tested for its ability to retain privacy properties and still enable attack detection. It is funded by DOE CESER’s CEDS program and is led by Sean Peisert.

This project is developing the methodology and tools allowing Electric Storage Systems (ESS) to automatically reconfigure themselves to counteract cyberattacks, both directly against the ESS control systems and indirectly through the electric grid. It is funded by DOE CESER’s CEDS program and is led by Daniel Arnold.

LBNL held the second workshop for the SPADES project in Dec. 2021 where the project partners presented deep dives into work conducted in the second year of the project.

This project aims to develop an understanding of security and performance requirements for the use of AI high solar / IBR / DER penetration scenarios, and also to develop an understanding of understanding power grid data confidentiality and privacy requirements. It is funded by DOE’s SETO office and is co-led by Sean Peisert and Daniel Arnold.

LBNL held an end of project workshop for the CIGAR project on Mar. 17, 2021 where project participants, stakeholders, and advisors were convened to discuss outcomes of the CIGAR project.