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）（诸如太阳能发电和电池存储）的大规模集成已经被考虑并在配电网络中实施。随着这些资源的激增，配电网络和DER集群的协调控制和管理对于在正常模式和电网突发事件期间实现最佳、可靠、弹性和稳定的操作变得至关重要。这项研究将推进使用基于逆变器的DER的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。该项目的成果预计将对我们电网的可靠性和弹性产生重大影响，并大规模整合基于可再生逆变器的DER。该合作提案的目标是将联合收割机人工智能（AI）和机器学习与电力系统和电力电子概念相结合，设计新颖的态势感知和纠正措施识别工具，以实现配电网的可靠，弹性运行，并具有基于逆变器的DER的高渗透率。该项目包括以下关键方面：1）开发新的方法，将配电网络中的多速率时间序列数据集与基于逆变器的DER集成，用于网络态势评估; 2）建立基于强制一致性的聚合，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，用于在电网突发事件期间对异质DER、临界和柔性负载的孤立稳定集群的边界识别。该项目的智力意义包括：1）开发了一种多速率、多传感器、基于概率图模型的方法，用于数据融合和配电网态势感知，并增加了基于逆变器的DER; 2）建立了一种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，使其能够有效地聚类，并实现DER集群的真正聚合模型;以及3）开发基于李雅普诺夫稳定性的优化框架，用于配电网络中的异质DER、临界和柔性负载的自主孤岛集群的边界识别，以增强电网突发事件期间的电网可靠性和弹性。该项目的成功完成将对电网的可靠性和弹性产生重大影响，并大规模集成基于逆变器的DER。该奖项反映了NSF的法定使命，并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，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.