Identification of Critical Nodes Using Granger Causality for Strengthening Network Resilience in Electrical Distribution System

As countries around the world commit to reducing brownfield energy generation and shifting toward clean energy, the placement of renewable energy sources (RES) optimally in the electrical distribution system remains a strenuous issue. Improperly integrating RES could have a detrimental impact on the efficient operation of the grid. This study proposes a real-time data-driven approach for optimal DERs allocation and identification of critical nodes in the electrical distribution system. A community detection clustering is performed on the IEEE 123 node feeder system to optimally cluster the nodes into two regions. Then, the Granger causal analysis is used to identify critical nodes in the system that are susceptible to failure or extreme events which may interrupt the operation of the system. Hence, strategically allocating RES to these critical nodes enhances network resilience, as validated by the computation of the percolation threshold. The findings reveal an impressive 37% boost in the system’s resilience attributed to the optimized deployment of RES.