Research Interests

My research draws on the diverse methods from optimal control and machine learning to aid in the decision making of cyber-physical systems, with particular interest on renewable-rich power systems. Particularly:

• Power system dynamics and control, renewable integration, wind turbines, frequency regulation of low-inertia grid

• Microgrid control, voltage-source converter, grid-interactive and grid-forming converters

• Formal verification, reachability analysis, hybrid systems, and their application in power systems

• Optimal control, robust control, numerical optimal control, reinforcement learning, and their application in power systems

• Surrogate optimization, machine learning for combinatorics, and their application in power systems

• Embeddable and distributed decision-making algorithms, control-in-the-loop real-time simulation for power systems

Here are some press about my research:

• A novel machine learning work for trajectory constrained optimization at Argonne

• A control paradigm for renewable integration at UTK