DGRI Lab advances electrical energy transition with a focus on distribution systems and microgrids. We are interested in planning and operational challenges arising from the increasing penetration of heterogeneous stochastic resources and demand, often inverter-interfaced. These include distributed energy resources such as batteries and solar PV, novel power-electronics-based controls, flexible demand, and electrified transportation. Furthermore, we are interested in the active role distribution systems and microgrids can play in enhancing the resiliency of the interconnected system to extreme weather events. Finally, we innovate market models to balance energy distribution democracy and equity in areas with a high penetration of distributed energy resources and electric vehicles. We study definition, modeling, planning, optimization, and control of these paradigms, covering a broad range of phenomena.

Our research hovers at the intersection of smart grids and machine learning (ML). We utilize a wide range of ML methods to estimate variables, enhance control computational efficiency, and mitigate the environment's stochasticity. We apply supervised and unsupervised learning to classify, categorize, estimate, or forecast parameters of a partially observable model (plant), leverage supervised learning for its simulation, and use imitation and reinforcement learning for the optimal control of its high-dimensional, stochastic, and non-stationary state-action space.

DGRI Lab is currently involved in several major initiatives and pursue the following specific research areas:

• - ML-Driven Stability Analysis and Enhancement: 5-year project 2024-2029;
• - ML-Driven Energy Storage Modeling: 2-year project 2024-2026;
• - Resiliency to Extreme Weather Events: 5-year project 2023-2028;
• - T&D Co-Planning & Coordinated Operation: 3-year project 2023-2026;
• - Transactive and Distribution-Level Markets: 3-year project 2023-2026.