Daniel Arnold 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 was conducted research and development of unmanned underwater vehicles for the United States Navy. He then received his Ph.D. from the Mechanical Engineering Dept. at the University of California, Berkeley in 2015. He is currently a 2015 ITRI-Rosenfeld Postdoctoral Fellow at the Lawrence Berkeley National Laboratory. Presently, his research focuses on the development of control strategies for the management of 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.
Energy/Environmental Policy Research Scientist/Engineer
Batch Measurement Extremum Seeking Control of Distributed Energy Resources to Account for Communication Delays and Information Loss
Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation With Limited Sensing
Model-Free Optimal Coordination of Distributed Energy Resources for Provisioning Transmission-Level Services