Collaborative Research: Data-Driven Situational Awareness for Resilient Operation of Distribution Networks with Inverter-based distributed energy resources

随着清洁、负担得起、可靠和有弹性的电力网络的日益重要，分布式能源(DER)的大规模集成已经被考虑并在配电网中实施，例如太阳能发电和电池存储。随着这些资源的激增，配电网和DER集群的协调控制和管理变得至关重要，以便在正常模式和电网意外情况下实现最佳、可靠、弹性和稳定的运行。这项研究将推进使用基于逆变器的DER进行电网管理的科学基础，并通过释放住宅、商业或工业客户的额外电网服务能力来满足配电网络的新要求。该项目的结果预计将对我们大规模集成基于可再生逆变器的DER的电网的可靠性和恢复能力产生重大影响。该合作方案的目标是将人工智能(AI)和机器学习与电力系统和电力电子概念相结合，设计新的态势感知和纠正行动识别工具，以实现具有高渗透率的基于逆变器的DER的配电网络的可靠、弹性运行。该项目包括以下关键方面：1)开发新的方法，将配电网中的多速率时间序列数据集与基于逆变器的DER集成，以进行网络状况评估；2)建立基于一致性的强制聚合，用于聚类基于逆变器的DER，即使它们最初是不一致的；以及3)开发基于稳定性的优化框架，用于在电网意外情况下识别各种DER、关键和灵活负载的孤岛稳定集群的边界。该项目的学术意义包括：1)开发了一种基于多速率、多传感器、概率图形模型的方法，用于数据融合和基于逆变器的DER激增的配电网态势感知；2)建立了基于一致性的聚合和动态模型开发技术，以加强DER之间的一致性和实现有效的集群，并实现DER集群的真正聚合模型；以及3)开发了基于Lyapunov稳定性的优化框架，用于识别不同类型DER、关键和灵活负荷的自治孤岛集群的边界，以增强电网在电网意外情况下的可靠性和弹性。该项目的成功完成将对基于逆变器的DER的大规模集成对电网的可靠性和弹性产生重大影响。该奖项反映了NSF的法定使命，并已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。

With the increasing importance of clean, affordable, reliable, and resilient electricity networks, large scale integration of distributed energy resources (DERs), such as solar generations, and battery storages, has been considered and implemented in distribution networks. With the proliferation of such resources, coordinated control and management of distribution networks and the DER clusters have become of upmost importance to achieve optimal, reliable, resilient, and stable operation during normal mode and grid contingencies. This research will advance the scientific foundations of grid management using inverter based DERs and allow the new requirements of distribution networks by unlocking additional grid services capabilities from residential, commercial or industrial customers. The outcome of this project is expected to have substantial impacts on reliability and resilience of our electricity network with large-scale integration of renewable inverter based DERs. The goal of this collaborative proposal is to combine artificial intelligence (AI) and machine learning with power systems and power electronics concepts to design novel situational awareness and corrective action identification tools for reliable, resilient operation of distribution networks with a high penetration of inverter-based DERs. This project includes the following key aspects; 1) Development of new methods to integrate multi-rate time-series data sets in distribution networks with inverter-based DERs for network situational assessment; 2) establishment of an enforced coherency-based aggregation for clustering inverter-based DERs even if they were initially non-coherent; and 3) development of a stability-based optimization framework for boundary identification of islanded stable clusters of heterogeneous DERs, critical, and flexible loads during grid contingencies. The intellectual significance of the project includes: 1) development of a multi-rate, multi-sensor, probabilistic graphical-model-based method for data fusion and distribution network situational awareness with proliferated inverter-based DERs; 2) establishment of a coherency-based aggregation and dynamic model development technique to enforce coherency and enable effective clustering among DERs and to realize a true aggregate model of DER clusters; and 3) development of a Lyapunov stability-based optimization framework for boundary identification of autonomously islanded clusters of heterogeneous DERs, critical and flexible loads in distribution networks to enhance grid reliability and resilience during grid contingencies. Successful completion of this project will have significant impacts on grid reliability and resilience with large-scale integration of inverter-based DERs.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.