CAREER: Learning Smart Meter Data to Enhance Distribution Grid Modeling and Observability

NSF的这一职业项目旨在提供理论和计算基础，以释放智能电表的未开发潜力，并从根本上提高配电网在正常和停电条件下的可观测性。该项目将把现有的依赖昂贵传感器的配电网建模和监控转变为使用广泛部署的智能电表的可扩展和健壮的方法。该项目的智能优势包括开发新的优化和概率图学习方法，以实现数据驱动的实时网络建模、大规模停电的快速检测和稳健的配电系统状态估计。该项目的更广泛影响包括通过培训应对数据挑战的专业工作人员将电力工程教育与数据科学相结合，开发开源数据集，以及向高中生提供智能电网互动学习的外联服务。如果成功，该项目将以最小的传感器投资成本为美国公用事业公司提供更好的态势感知，从而节省数百万美元，同时促进可再生能源的无缝整合，并增强电网的弹性。智能电表的不断部署将监测能力扩展到电网边缘，并提供了前所未有的数据量。然而，大多数公用事业公司仅将智能电表用于计费目的，而不是探索见解或从智能电表中获取可操作的信息，因为这些数据仅限于低分辨率和不同步的测量。拟议的项目将开辟一个新的场所，使公用事业公司能够通过三项主要技术创新从智能电表中提取有用的情报：(1)实时拓扑识别，其中的方法是设计一个类似拉普拉斯的矩阵，该矩阵可以捕获物理网络特征，并利用其固有的稀疏结构来发现节点连接，即使是从低质量的测量。针对在线参数辨识问题，提出了一种仅利用智能电表数据的自下而上优化算法。(2)一种新的图学习方法，该方法利用智能电表和其他停电信息源之间的内在条件独立性，作为快速、可扩展和准确的停电检测的数据融合框架。(3)多目标稳健数据恢复技术，以最小化智能电能表的异步误差。提出了一种分层强化学习辅助方法，以克服可伸缩性问题，并实现一次-二次配电系统的联合状态估计。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。

This NSF CAREER project aims to provide the theoretical and computational foundation that will allow unlocking the untapped potential of smart meters and radically enhance electric power distribution grid observability in both normal and outage conditions. The project will transform existing distribution grid modeling and monitoring that relies on costly sensors to a scalable and robust method using widely-deployed smart meters. The intellectual merits of the project include developing new optimization and probabilistic graph learning methods to enable data-driven real-time network modeling, rapid detection of large-scale outages, and robust distribution system state estimation. The broader impacts of the project include integrating power engineering education with data science through training professional workforce for data challenges, developing open-source datasets, and providing outreach to high-school students for interactive learning of smart grids. If successful, this project will provide U.S. utilities with better situational awareness at minimum sensor investment cost, thus saving millions of dollars, while promoting seamless integration of renewable energy, and enhancing grid resilience.The increasing deployment of smart meters extends monitoring capability to grid edges and provides unprecedented amounts of data. However, most utilities use smart meters for billing purposes only, without exploring insights or gaining actionable information from them because these data are limited to low-resolution and unsynchronized measurements. The proposed project will open a new venue to enable utilities to extract useful intelligence from smart meters through three major technical innovations: (1) Real-time topology identification, where the approach is to design a Laplacian-like matrix that can capture the physical network feature and leverage its inherent sparse structure to discover nodal connectivity even from low-quality measurements. For online parameter identification, a novel bottom-up optimization algorithm using only smart meter data is proposed. (2) A new graph learning approach that takes advantages of intrinsic conditional independencies among smart meters and other outage information sources to serve as a data fusion framework for fast, scalable, and accurate outage detection. (3) A multi-objective robust data recovery technique to minimize smart meter asynchrony error. A hierarchical reinforcement learning-aided method is proposed to overcome the scalability issue, and to enable joint primary-secondary distribution system state estimation.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.