  Smart distribution grid with multiple renewable energy sources can experience
random voltage fluctuations due to variable generation, which may result in
voltage violations. Traditional voltage control algorithms are inadequate to
handle fast voltage variations. Therefore, new dynamic control methods are
being developed that can significantly benefit from the knowledge of dominant
voltage influencer (DVI) nodes. DVI nodes for a particular node of interest
refer to nodes that have a relatively high impact on the voltage fluctuations
at that node. Conventional power flow-based algorithms to identify DVI nodes
are computationally complex, which limits their use in real-time applications.
This paper proposes a novel information theoretic voltage influencing score
(VIS) that quantifies the voltage influencing capacity of nodes with
DERs/active loads in a three phase unbalanced distribution system. VIS is then
employed to rank the nodes and identify the DVI set. VIS is derived
analytically in a computationally efficient manner and its efficacy to identify
DVI nodes is validated using the IEEE 37-node test system. It is shown through
experiments that KL divergence and Bhattacharyya distance are effective
indicators of DVI nodes with an identifying accuracy of more than 90%. The
computation burden is also reduced by an order of 5, thus providing the
foundation for efficient voltage control.
