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

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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.