CAREER: Faithful, Reducible, and Invertible Learning in Distribution System for Power Flow

As the electric distribution grid becomes smarter, new services have become available, such as community renewable hubs, home energy management with electric vehicles (EV), and demand response programs. However, these new services pose significant challenges to grid reliability due to (1) system unobservability, (2) data quality issues because of low-resolution and bad data, and (3) highly intermittent energy sources like solar and wind generation and EV charging.

Without solving these problems, power outages and equipment damage can happen frequently, leading to device malfunctions, expensive maintenance costs, and unsatisfied customers. The goal of this CAREER effort is to develop the theoretical foundation of building a rigorous power flow equation in the distribution grid under unobservability. The intellectual merit of the proposed research lies in the generation of new knowledge for learning theories, mechanisms, and architectures.

Key advances are (1) designs of deep neural network (DNN)-based power flow equations that are not only physically reducible but also convex to the intermediate layer in DNN that represents physics, (2) creation of a physical-generative adversarial network to boost the robustness of reconstructing power flow against limited data, and (3) derivation of the solution for inverting the DNN-based power flow equation based on the physical DNN for real-time power flow analysis.

The Broader Impacts include a physics-enhanced AI framework to utilities and companies that also demonstrate a more resilient distribution grid management system due to the reducibility, faithfulness, and invertibility of our models. The proposed work will help reduce the cost of managing behind-the-meter resources and emissions greatly by bridging the gap between physics and learning in distribution grids.

Such interdisciplinary analysis for grid modernization will create a new class of open-source code on new machine learning methods, supporting a thriving community of academics and industry collaborators. The integrated education component will also create a scientific program for K12 students and minorities to engage in activities related to AI for power systems.

To popularize AI among the utility engineers, the PI plans to deploy the proposed platforms. The PI will also expand his webinar series to promote communications between academia and industry.

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.