Research

Thrust 1. Coordination of Large-Scale Converter-Based Resources

The rapid proliferation of power electronic converter-based energy resources (CBRs), such as solar panels, wind turbines, batteries, electric vehicles, and smart buildings, introduces fundamentally different dynamic behaviors, reduced system inertia, and increased complexity for maintaining grid stability. To address these challenges, we develop distributed data-driven control and optimization algorithms to coordinate ultra-scale CBRs and leverage their substantial flexibility to enhance power system operation.

Thrust 2. Scalable Learning-Based Control and Optimization

Learning-based control and optimization approaches can capture hard-to-model dynamics and outperform purely model-based methods in complex dynamical systems. We aim to leverage inherent system structures to design scalable algorithms for large-scale human-cyber-physical systems with provable safety, stability, and optimality performance guarantees. 

Thrust 3. LLM-Powered Agentic AI for Smart Grid

Leveraging the advanced reasoning capabilities of large language models (LLMs), AI Agent is an intelligent system that can autonomously plan, reason, and act through domain tools to complete complex tasks. We are developing a tailored agentic AI system, GridAgent, to assist in power system planning, operation, and analysis. 

Thrust 4. Dynamic Modeling and Grid Integration of Large Loads

The accelerated advancement of AI is driving the rapid expansion of data center infrastructure and imposing unprecedented pressure on the power grid due to the immense electricity demands of ultra-scale AI workloads. AI data centers are emerging as prominent large electric loads in many regional power grids. To support reliable integration, we develop high-fidelity dynamic models and decision-making tools that capture data-center electrical behaviors, enabling rigorous stability studies, ride-through assessments, as well as planning and operational decisions at scale. 

Thrust 5. Carbon Accounting and Decarbonization in Power Systems

The power sector is a major contributor to carbon emissions. Deep and rapid decarbonization of electric power systems has emerged as an urgent priority. To address the complex mission of power system decarbonization, we develop a comprehensive framework for the "Carbon-Electricity Nexus", including Carbon Emission Flow (CEF) for demand-side carbon accounting, and Carbon-Aware Optimal Power Flow (C-OPF) for grid decarbonization decision-making, as well as Carbon-Electricity Market design.