  With the commitment to climate, globally many countries started reducing
brownfield energy production and strongly opting towards green energy
resources. However, the optimal allocation of distributed energy resources
(DERs) in electrical distribution systems still pertains as a challenging issue
to attain the maximum benefits. It happens due to the systems complex behaviour
and inappropriate integration of DERs that adversely affects the distribution
grid. In this work, we propose a methodology for the optimal allocation of DERs
with vulnerable node identification in active electrical distribution networks.
A failure or extreme event at the vulnerable node would interrupt the power
flow in the distribution network. Also, the power variation in these vulnerable
nodes would significantly affect the operation of other linked nodes. Thus,
these nodes are found suitable for the optimal placement of DERs. We
demonstrate the proposed data-driven approach on a standard IEEE-123 bus test
feeder. Initially, we partitioned the distribution system into optimal
microgrids using graph theory and graph neural network (GNN) architecture.
Further, using Granger causality analysis, we identified vulnerable nodes in
the partitioned microgrid; suitable for DERs integration. The placement of DERs
on the vulnerable nodes enhanced network reliability and resilience.
Improvement in resilience is validated by computing the percolation threshold
for the microgrid networks. The results show a 20.45% improvement in the
resilience of the system due to the optimal allocation of DERs.
