Daniel Arnold

Adjunct Professor
Research Interests
Management of distributed energy resources, Electric power distribution systems, Application of ML techniques, Power systems, Robotics

655 Davis Hall

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Daniel Arnold is an Adjunct Professor of Civil and Environmental Engineering at UC Berkeley. Arnold's research focuses on developing control strategies for managing distributed energy resources in electric power distribution systems. Additionally, he is studying the application of machine learning techniques to analyze high-resolution distribution system Phasor Measurement Unit (PMU) data. His research interests include controls, optimization, power systems, and robotics. Arnold is an emerging leader in using modern data science tools, including artificial intelligence, machine learning, and control, to improve the stability and security of energy and power systems. He has been active in his service to the professional community, including leading a research group at Lawrence Berkeley National Laboratory (LBNL), serving as a peer reviewer for leading journals and leadership of conference sessions, and participating in application-oriented workshops to bring his research to action.


Ph.D., Mechanical Engineering, University of California, Berkeley, 2015 

M.S., Engineering Science, University of California, San Diego, 2006                                                                      

B.S., Mechanical Engineering, University of California, San Diego, 2005

Arnold's research focuses on improving distributed energy systems' stability and security by using emerging data and computational science tools, including autonomous control, artificial intelligence, and machine learning. Looking to the future, he intends to continue and extend his work on power and energy systems, and also to expand his work to include control and optimization of cyber-physical systems more broadly. Some of Arnold's key research contributions stem from work to mitigate attacks or unintentional outages in energy systems. He has developed strategies that enable real-time control of the methods to contain attacks and prevent system destabilization. He has done this by developing algorithms for adaptive control that utilize non-compromised components of the system. Initial applications of his work include ensuring the security of distributed systems, like home batteries and rooftop solar systems, to prevent large-scale outages and blackouts. Still, the methods can be extended to distributed interdependent systems more broadly.



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