Daniel Arnold

Daniel Arnold is an Adjunct Professor of Civil and Environmental Engineering at the University of California, Berkeley. His research focuses on the management of distributed energy resources and electric power distribution systems, with an emphasis on the application of machine learning techniques. Arnold's work includes the analysis of high-resolution distribution system Phasor Measurement Unit (PMU) data and the development of control strategies for enhancing the stability and security of energy systems. He has contributed to research on mitigating attacks or unintentional outages in energy systems through adaptive control algorithms that utilize non-compromised system components. His research also extends to the control and optimization of cyber-physical systems, with applications in ensuring the security of distributed systems such as home batteries and rooftop solar systems.

Daniel Arnold is a researcher at the Lawrence Livermore National Laboratory with expertise in controls, power systems, and optimization. His work focuses on developing advanced control strategies for power systems, including voltage regulation, optimal power flow, and decentralized energy resource management. Arnold's research integrates machine learning and optimization techniques to address challenges in smart grid technologies, such as real-time state estimation, cyber-attack mitigation, and distributed energy services. His contributions span both theoretical and applied aspects of power systems, with an emphasis on improving grid reliability and efficiency through innovative control methodologies.