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

随着电网从中央控制系统转向越来越依赖基于逆变器的资源(IBR)，如太阳能和风能，电网正在经历重大变革。这种变化给电力系统的运行和管理带来了新的挑战和机遇。这项拟议的研究旨在开发基于机器学习的创新解决方案，以应对在配电网中整合IBR所带来的实时操作挑战。通过创建一套强大、可扩展和安全关键的机器学习工具，该项目将使清洁能源和配电电网得到广泛采用。这项研究的更广泛影响包括通过整合电力工程、机器学习和物联网的跨学科研究和课程设计培训下一代科学家和工程师。此外，该项目将开发开源数据集和模型，同时通过互动演示在代表性不足的大学预科学生中促进STEM教育。这项研究的成功完成将加速清洁能源的应用，增强电力系统的弹性，确保公正和公平的能源过渡。拟议的项目旨在开发一个基于学习的控制和优化框架，以解决与配电网相关的实时操作挑战，并增加基于逆变器的资源(IBR)的整合。研究目标包括：1)具有不精确系统模型的样本高效混合学习，将深度强化学习(DRL)与简化的电网模型和真实世界的试验相结合，以实现快速学习和性能改进；2)用于实时网络重构的图强化学习，将DRL与图神经网络(GNN)相结合，创建一种能够适应和推广到任意网络拓扑的新型学习体系，解决电压违规、无功分布和系统损耗最小化等问题；以及3)基于网格边缘的分布式学习和控制，通过将联合多代理学习与深度压缩技术相结合，建立一个可在资源受限的边缘设备上运行的可扩展的分布式学习框架。该框架将设计成健壮、自适应、安全和轻量级的，在考虑网络约束的同时提供网格拓扑的实时优化和控制，以确保系统安全。该项目将有助于为未来具有大规模IBR和分散测量的配电系统开发可扩展的、基于学习的控制解决方案，培育更具弹性和可持续发展的电网。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.