EAGER: Leveraging Smart Meter Data for Enhanced Situational Awareness of Power Distribution Systems

EAGER：利用智能电表数据增强配电系统的态势感知

• 批准号: 2225341
• 负责人: Anil Pahwa
• 金额: $ 22万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225341&HistoricalAwards=false
• 关键词: EAGER; Leveraging; Smart; Meter; Data

Electric power distribution systems are becoming active due to integration of consumer owned rooftop solar generation, electric vehicles, energy storage, and advanced metering infrastructure (AMI). The goal of the proposed research is to investigate fundamental approaches to effectively integrate AMI data to increase situational awareness of distribution systems for more effective operation. Further, the research will focus on integrating machine learning models to learn from historical data for increasing modelling accuracy and uncertainty quantification. The research will result in fundamental advancement in data-based operation of power distribution systems by demonstrating the value of AMI and other smart meter data for efficient operation of the distribution system. In addition to accelerating the adoption of data-driven grid management strategies, the proposed work can serve as a practical guide for utilities to leverage AMI data for situational awareness. The utilities will be able operate the system more efficiently which in turn will lead to economic benefits for the utilities and the consumer. The project will support and train a doctoral student on data-based approaches for operation of power distribution systems. The results will be disseminated through conference and journal publications. The research will investigate innovative approaches for optimal polling of smart meter to obtain a holistic picture of the low-voltage distribution system, reconciling multi-time scale heterogenous meter data to empower novel sparsity-based state estimation strategies, and systematic integration of historical data and real time measurements via a machine learning framework that includes uncertainty quantification. The proposed data-driven approach that leverages real time measurements from a limited subset of smart meters represents a significant paradigm shift in current operational practices. It will also lead to new pathways for future research on utilizing the AMI data to enhance reliability, resiliency, and efficiency of power distribution systems.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.

由于消费者拥有的屋顶太阳能生成，电动汽车，能源存储和高级计量基础设施（AMI）的整合，电力发电系统变得活跃。 拟议的研究的目的是研究有效整合AMI数据以提高分配系统的情境意识以进行更有效操作的基本方法。此外，该研究将着重于整合机器学习模型，以从历史数据中学习，以提高建模准确性和不确定性量化。 这项研究将通过证明AMI和其他智能电表数据的价值来有效地运行分配系统的价值，从而导致电源分配系统的基于数据的操作的基本进步。除了加速采用数据驱动的网格管理策略外，拟议的工作还可以作为实用程序的实用指南，以利用AMI数据来获得情境意识。公用事业将能够更有效地操作系统，从而为公用事业和消费者带来经济利益。该项目将支持并培训一名博士生的基于数据的方法，以进行配电系统的操作。 结果将通过会议和期刊出版物传播。该研究将调查智能电表最佳轮询的创新方法，以获得低压分配系统的整体图片，调解多个时间尺度的异质仪数据，以促进基于新颖的稀疏状态估计策略以及通过包括不确定性量化的机器学习框架的历史数据和实时测量的系统整合。提出的数据驱动方法利用有限的智能电表子集实时测量的方法代表了当前运营实践的重大范式转移。这还将为未来的研究带来新的途径，以利用AMI数据来提高功率分销系统的可靠性，弹性和效率。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的。

项目成果

Modified Matrix Completion-Based Detection of Stealthy Data Manipulation Attacks in Low Observable Distribution Systems

• DOI: 10.1109/tsg.2023.3266834
• 发表时间: 2023-11
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: J. Rajasekaran;B. Natarajan;A. Pahwa