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

职业：潮流配电系统中的忠实、可简化和可逆学习

基本信息

批准号：
2048288
负责人：
Yang Weng
金额：
$ 50万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2026-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048288&HistoricalAwards=false
关键词：
CAREER Faithful Reducible Invertible Learning

项目摘要

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.

随着配电网变得更加智能，新的服务已经出现，例如社区可再生能源中心、电动汽车（EV）家庭能源管理和需求响应计划。然而，这些新服务对电网可靠性提出了重大挑战，原因是：（1）系统不可观测性，（2）低分辨率和不良数据导致的数据质量问题，以及（3）太阳能和风力发电以及电动汽车充电等高度间歇性能源。如果不解决这些问题，停电和设备损坏可能会频繁发生，导致设备故障，昂贵的维护成本和不满意的客户。这个职业生涯的努力的目标是发展的理论基础，建立一个严格的潮流方程在配电网下不可观测。所提出的研究的智力价值在于学习理论，机制和架构的新知识的产生。关键进展是（1）基于深度神经网络（DNN）的潮流方程的设计，这些方程不仅在物理上可简化，而且凸向DNN中代表物理的中间层，（2）创建物理生成对抗网络，以提高针对有限数据重建潮流的鲁棒性，以及（3）推导基于DNN的潮流方程的解，该解基于用于实时潮流分析的物理DNN。增强的人工智能框架，公用事业和公司，也证明了一个更有弹性的配电网管理系统，由于减少，忠实性和可逆性，我们的模型。拟议的工作将有助于减少管理背后的电表资源和排放的成本大大缩小物理和学习之间的差距在配电网。这种针对网格现代化的跨学科分析将为新的机器学习方法创建一类新的开源代码，支持蓬勃发展的学术界和行业合作者社区。综合教育部分还将为K12学生和少数民族创建一个科学计划，以参与与电力系统人工智能相关的活动。为了在公用事业工程师中推广人工智能，PI计划部署拟议的平台。PI还将扩大他的网络研讨会系列，以促进学术界和工业界之间的沟通。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。