Power Grid

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 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.

This project performed R&D to enable distribution grids to adapt to resist a cyber-attack by (1) developing adaptive control algorithms for DER, voltage regulation, and protection systems; (2) analyze new attack scenarios and develop associated defensive strategies. It was funded by DOE’s CEDS program and was co-led by Sean Peisert and Daniel Arnold.

LBNL held the first workshop for the SPADES project on Dec. 2, 2020 where the project participants convened to discuss progress made over the past year as well as plan for work to be conducted during Year 2. Due to the COVID-19 pandemic, the workshop was held virtually.

This project aims to explore applications of a Byzantine Fault Tolerant (BFT) architecture in combination with ML/AI methods to ensure that the bulk power system, including protective relays in the transmission grid, and associated substation and control center systems, can perform intrusion tolerant operations. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

This project will bring together a multi-disciplinary UC-Lab team of cybersecurity and electricity infrastructure experts to investigate, through both cyber and physical modeling and physics-aware cybersecurity analysis, the impact and significance of cyberattacks on electricity distribution infrastructure. It is funded by the UC-Lab Fees Research Program. The overall project is led by Hamed Mohsenian-Rad; the LBNL portion is led by Sean Peisert.

The goal of this project is to create advanced, distributed data analytics capability to provide visibility and controllability to distribution grid operators. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

The goal of this project is to develop technologies and methodologies to protect the nation’s power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them. It is funded by DOE OE’s CEDS program via the Grid Modernization Initiative and is co-led by Sean Peisert.

This project is designing and developing a key management system to meet the unique requirements of electrical power distribution systems. It is funded by DOE OE’s CEDS program and is led by Sean Peisert.

This project used micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It was funded by DOE OE’s CEDS program and was led by Sean Peisert.

This distribution level phasor measurement data can be used to understand ways to enables advanced diagnostic, monitoring and control methodologies in distribution systems.

The goal of this project was to design and implement a measurement network, which can detect and report the resultant impact of cyber security attacks on the distribution system network. It was funded by DOE OE’s CEDS program and was co-led by Chuck McParland and Sean Peisert.