January 31, 2020, Webb 1100

The proliferation of Distributed Energy Resources in electric power distribution grids puts utility operators in a difficult position as they must ensure the cybersecurity of a system whose behavior is increasingly governed by devices outside of utility control. Autonomous control functions in DER smart inverters constitute a potential vulnerability that could be exploited by malicious entities seeking to disrupt normal grid operations. In this talk, I will discuss a portfolio of research being conducted at Lawrence Berkeley Lab (LBL) focusing on the use of adaptive and optimal control techniques to develop a supervisory control structure to mitigate cyber attacks on subsets of DER smart inverters in a given network. The developed techniques allow non-hacked DER to alter their behavior to nullify destabilizing influences introduced into the system by hacked units.

Daniel Arnold is a research scientist in the Grid Integration Group at Lawrence Berkeley National Laboratory. He received the
B.S. degree in mechanical engineering from the University of California, San Diego, in 2005, the M.S. degree in engineering science from the University of California, San Diego, in 2006. From 2006 to 2009 he conducted research and development of unmanned
underwater vehicles for the United States Navy at the Space and Naval Warfare Center in San Diego, California. He then received his Ph.D. from the Mechanical Engineering Dept. at the University of California, Berkeley in 2015. He was a 2015 ITRI-Rosenfeld
Postdoctoral Fellow at the Lawrence Berkeley National Laboratory. Presently, his research focuses on the application of control,
optimization, and machine learning techniques for electric grid resiliency and cyber security.