The increased proliferation of Distributed Energy Resources (DER) into the electric distribution system simultaneously presents challenges and opportunities for maintaining the cybersecurity of the grid. The remote update capability of DER has the potential to change the behavior of large aggregations of individual units, which can substantially alter grid conditions - for good or bad.

Project MAGIC will develop Artificial Intelligence/Machine Learning (AI/ML) algorithms to detect and mitigate cyberattacks on aggregations of DER and the electric grid, and will deploy and test these algorithms (through hardware-in-the-loop experiments) on the Siemens Spectrum Power Microgrid Management System (MGMS). Additionally, project MAGIC will integrate AI/ML algorithms into the National Rural Electric Cooperative (NRECA) Open Modeling Framework (OMF), allowing electric cooperatives to conduct simulations of cyber attack detection and mitigation strategies for their specific networks.

Project Goals

The objectives of the project are to:

1. Develop secure AI/ML algorithms to detect cyber attacks on aggregations of DER and distinguish attacks from normal operating conditions.
2. Extend a reinforcement learning framework developed in previous RMT/CEDS projects (e.g. CIGAR, SPADES) to mitigate the effect of cyber attacks on DER in a wide array of operating conditions.
3. Integrate the attack detection and mitigation algorithms into a commercially available substation/microgrid management platform for algorithm demonstration.
4. Create an open source simulation tool allowing electric utilities to determine rules to detect and mitigate cyber attacks designed to severely disrupt normal grid operations or cause voltage instabilities.
5. Develop a software test harness to assess the security of AI/ML algorithms for electric grid attack detection and mitigation.