Learning-Enabled Modeling, Monitoring, and Decision Making for Distribution Grids

This NSF project aims to propel the zero-carbon emission transition of the electric grid infrastructure by develop a holistic framework for integrating renewable and flexible resources at grid edge. The project will bring transformative changes to the real-time monitoring and coordination of these grid-edge resources in support of the efficiency and safety of their connected distribution grids.

This will be achieved by synthesizing machine learning advances into the algorithmic developments that can recognize the governing physics of the underlying systems and address the limitations in cyber infrastructure in distribution grids. The intellectual merits of the project include a suite of machine learning enabled solutions to attain an efficient and safe operation of grid-edge resources under the information constraints due to limited model knowledge and low observability.

The broader impacts of the project include the acceleration of integrating renewable energy and low-carbon resources into the electricity infrastructure, and a comprehensive education plan consisting of updating power engineering curriculum and designing hands-on demos for pre-college students.

The overarching goal of this proposal is to establish a learning-enabled framework for operating distributed energy resources (DERs) with efficiency, adaptivity, and robustness. To address the status quo of limited sensing and communications in power distribution grids, we advocate to incorporate the unique features of the underlying feeder models and data profiles.

Our proposed research consists of three cohesive thrusts: T1) Designing data-driven distribution modeling approaches under partial observability; T2) Developing monitoring algorithms of grid-edge resources from heterogeneous data sources; and T3) Developing scalable and safe DER policies using graph-based and risk-aware learning. These three tasks will be further integrated to support each other into a holistic framework as validated by real-world feeder systems and datasets.

In a nutshell, our research agenda will fulfill the dual objectives of enabling distribution system operations by fully embracing a multitude of data sources, while attaining timely and safe DER actions to address the information-limited and resource-constrained scenarios.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.