ERI: Learning to Operate Distribution Grids with Extreme Penetration of Inverter-based Resources (L2ODG)

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The power grid is undergoing a significant transformation as it shifts from centrally controlled systems to increasingly relying on inverter-based resources (IBRs), such as solar and wind power. This change brings new challenges and opportunities for how power systems are operated and managed. The proposed research aims to develop innovative machine learning-based solutions to address real-time operational challenges arising from integrating IBRs in power distribution grids. By creating a suite of robust, scalable, and safety-critical machine learning tools, this project will enable widespread adoption of clean energy sources and transform distribution grids. The broader impacts of this research include training the next generation of scientists and engineers through interdisciplinary research and curriculum design that integrates power engineering, machine learning, and the Internet-of-Things. Furthermore, the project will develop open-source datasets and models while promoting STEM education among underrepresented pre-college students through interactive demonstrations. Successful completion of this research will accelerate the adoption of clean energy resources, enhance power system resilience, and ensure a just and equitable energy transition.The proposed project seeks to develop a learning-based control and optimization framework addressing real-time operational challenges associated with power distribution grids and increasing integration of inverter-based resources (IBRs). The research objectives include: 1) Sample-efficient Hybrid Learning with Inaccurate System Models, combining deep reinforcement learning (DRL) with simplified grid models and real-world trials for rapid learning and performance improvement; 2) Graph Reinforcement Learning for Real-time Network Reconfiguration, integrating DRL with Graph Neural Networks (GNN) to create a novel learning architecture capable of adapting to and generalizing to arbitrary network topologies, addressing issues like voltage violations, reactive power distribution, and system loss minimization; and 3) Distributed Learning and Control over the Grid Edge, establishing a scalable, distributed learning framework that runs on resource-restricted edge devices by combining federated multi-agent learning with deep compression techniques. The proposed framework will be designed to be robust, adaptive, safe, and lightweight, offering real-time optimization and control of grid topology while considering network constraints to ensure system safety. This project will contribute to developing scalable, learning-based control solutions for future distribution systems with massive IBRs and dispersed measurements, fostering a more resilient and sustainable power grid.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.